# A Browser Extension for in-place Signaling and Assessment of Misinformation

Farnaz Jahanbakhsh  Stanford UniversityStanfordUSA and  David R. Karger
Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of TechnologyCambridgeUSA sources other

(2024)

###### Abstract.

The status-quo of misinformation moderation is a central authority, usually
social platforms, deciding what content constitutes misinformation and how it
should be handled. However, to preserve users’ autonomy, researchers have
explored democratized misinformation moderation. One proposition is to enable
users to assess content accuracy and specify whose assessments they trust. We
explore how these affordances can be provided on the web, without cooperation
from the platforms where users consume content. We present a browser extension
that empowers users to assess the accuracy of any content on the web and shows
the user assessments from their trusted sources in-situ. Through a two-week
user study, we report on how users perceive such a tool, the kind of content
users want to assess, and the rationales they use in their assessments. We
identify implications for designing tools that enable users to moderate
content for themselves with the help of those they trust.

misinformation, democratized content moderation, fact-checking

††journalyear: 2024††copyright: acmlicensed††conference: Proceedings of the
CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Proceedings of the CHI Conference on Human
Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI,
USA††doi: 10.1145/3613904.3642473††isbn: 979-8-4007-0330-0/24/05††ccs: Human-
centered computing Collaborative and social computing systems and tools

Figure 1. Upon visiting a webpage, the Trustnet extension opens a pane on the
side showing the assessments of the page by those the user follows or trusts.
The figure shows a news article (left) and a Youtube video (right) assessed by
users of the study.

The image on the left shows an article from Breitbart with the headline
“Report: NYC Schools Spent over $200K on Drag Queen Shows”. The assessments
pane from our browser extension is open on the righthand side of the page. It
has a light red background color indicating that the content has been assessed
as inaccurate by the assessors of the user viewing the page. Inside the pane,
there is a segment for the user to submit their own assessment, followed by a
section where assessments of others are displayed, along with who the source
of each assessment is as well as a timestamp. The image on the right shows a
Youtube vide titled “Roblox is Suing Me for $16 Million — Ep1 Death Vallely”.
The assessments pane from the Trustnet browser extension is open on the
righthand side of the page. It has a light green background color indicating
that the content has been assessed as accurate by the assessors of the user
viewing the page. A user’s assessment is shown in the pane.

##  1\. Introduction

The convenience of spreading and consuming misinformation on social media
platforms (Wang et al., 2019; Vosoughi et al., 2018) and the media spotlight
given to the havoc that misinformation has wrought in recent years (Argentino,
2021; Thomas, 2022; Bengali, 2019; Dixit and Mac, 2018) have caused a surge of
attention to this age-old problem. Researchers as well as platform operators
are increasingly investing effort in combating misinformation. At present, the
dominant approach to fighting misinformation is to place the onus for doing so
on the large social platforms where many users spend their time. This approach
places a critical social decision in the hands of for-profit companies, and
limits users’ autonomy in deciding who they trust to declare what is accurate
(Jahanbakhsh et al., 2022a). It also does nothing to combat misinformation
that users encounter off the social platforms, including the many news sites
found on the web. The downsides of centralized misinformation moderation have
motivated some researchers and practitioners to pursue decentralized solutions
to the problem. For instance, Twitter Community Notes is an example of such an
approach which involves all the users of the community in assessing the
accuracy of content and rating each other’s assessments. However, the final
decision of what notes to eventually show to the public is taken in a
centralized fashion, by Twitter’s “bridging algorithm” (div, 2022).

In contrast, a body of work has advocated for giving users more autonomy while
aiding them by providing the tools that they need for discerning truth from
falsehoods. For instance, it is reported that enabling users to assess the
accuracy of content can reduce their likelihood to share misinformation
(Jahanbakhsh et al., 2021), as it nudges users to have accuracy on top of
their mind (Pennycook et al., 2020b, 2021). If user assessments are captured
as structured metadata, they can be displayed on content in structured form as
well, similar to but separate from likes or comments, and have the potential
to warn the assessor’s social circle should they encounter the misinforming
content (Jahanbakhsh et al., 2022a). Jahanbakhsh et al. argue that users
should additionally be given the ability to specify whose assessments they
would like to see—in essence, leaving it to each user to decide who they trust
as their moderators. They report that even on the current platforms, users do
ask those they trust for their assessments; however, they do this without
platform support, and in fact despite the platforms getting in their way. In a
user study of a prototype social news sharing platform that offers these
affordances, the authors determine that users want them and are capable of
using them (Jahanbakhsh et al., 2022a). Capturing assessments and trust
relationships in structured form can be a first step in designing
interventions that point users to fact-checking information from those that
they trust.

To increase user autonomy, platform operators can be urged to incorporate the
affordances of user generated assessments and specified trusted sources.
However, this incorporation is unlikely to happen without activism or
legislation, because changing the user experience drastically, particularly
ceding too much control over content to their users, can impact platform
revenues. Researchers could build a new social platform where they offer these
affordances to users, for instance similar to the prototype platform presented
by Jahanbakhsh et al. (Jahanbakhsh et al., 2022a). But such undertaking
realistically may not succeed because first, it costs much development and
maintenance for this new platform to have a chance to compete with the
existing social platforms; and second, users are unlikely to abandon their
social network on the existing platforms and join a new one without a critical
mass of their connections already present there.

While social media platforms have been an important outlet for the circulation
of misinformation, they are far from being the only one (Fourney et al.,
2017). Efforts concentrated on getting _social platforms_ to moderate content
ignore that users consume content, by choice or by accident, outside these
platforms as well. Users for instance, may encounter links to unverified or
misinforming content on a news or media website that crosslinks to another, in
an email newsletter, or on a news aggregator. It is unrealistic to expect that
every website or platform on the web, some the very perpetrators of
misinformation, offer content moderation and in a fair and rigorous way.

The pursuit for ubiquitous content moderation that preserves user autonomy is
the motivation behind this work. What follows is a case study of how enabling
users to moderate content, specifically in the context of misinformation, can
be deployed _everywhere on the web_ , in a platform-agnostic manner and
without support from the social platforms or individual web sites. We use the
term “moderation” as a concept that involves categorizing the status of
content by applying labels, including those that are commonly used by
platforms such as “misleading” or “explicit” as well as any other label that
can help a user customize their web experience, such as “depressing”. The
other aspect of moderation is taking an action, if any, on content that has
been positively or negatively labeled, e.g., whether to simply flag or remove
it. These two aspects are often bundled together in the status-quo practices.
In this work, we do not focus on the latter aspect and simply signal the
status of content. If the categorization of content is customized according to
users’ needs and input, then a set of tools can exist that take the labels and
act in different ways on them, or within the same tool, the choice of what to
do with the content can also be deferred to the user (Jahanbakhsh et al.,
2022a).

In this work, we design, deploy, and evaluate a browser extension that lets
_anyone_ assess any content—news articles, Youtube videos, tweets, etc.—
_anywhere_ on the web, and lets each user independently decide whose
assessments they _trust_ and want to use to help protect them from
misinformation. This tool is an extension of the Trustnet social platform
previously offered by Jahanbakhsh et al. (Jahanbakhsh et al., 2022a). Upon
visiting a page, the user will see assessments of the content by their trusted
sources, if any have contributed one, as shown in Figure 1. Such assessments
can aid the user in deciding whether the content they are viewing is credible.
However, since links on social media are often not clicked, even when they are
shared (Gabielkov et al., 2016), showing the assessments of a page only when a
user visits the page will not benefit those who do not navigate to the page.
To address this issue, the extension also places any available trusted
accuracy assessments next to outgoing links on the page the user is reading,
as displayed in Figures 2 & 3.

Figure 2. The extension places any available accuracy assessments from the
user’s trusted or followed sources next to outgoing links on the page the user
is visiting. The screenshot shows an example of a red X marking such a link on
the Twitter timeline. The link is visually faded because it has been assessed
as inaccurate.

The image shows a tweet from RedState with a preview of the article that it
has tweeted about. The preview is visually faded and appears with 2 cross in a
circle icons (one on the image, the other on the title and description), to
indicate that the article has been assessed as inaccurate.

Figure 3. Outgoing links to a Youtube video on the page are displayed with a
checkmark icon (marked with blue arrows), indicating that the video has been
assessed as accurate by the user’s assessors.

The image shows previews of 2 Youtube articles, one with a green checkmark in
a circle placed on its preview image and title to indicate that it has been
assessed as accurate by the user’s assessors.

To evaluate the potential of such a design and users’ perception of it, we
conducted a user study of the extension. We recruited 32 users and to use the
extension for two weeks. Users were tasked with assessing the accuracy of two
pieces of content daily. Users also could, but were not required to, share
content with the other users via the extension. The shared content would be
presented on each user’s feed on the Trustnet platform instance that we asked
them to visit on a daily basis. We surveyed our users after the user study to
understand their fact-checking needs and desires and their perception of the
tool. The user study serves as a technology probe (Hutchinson et al., 2003)
where users can interact with a tool that empowers them to have an active role
in moderating misinformation for themselves and others, without needing to
drastically change where and how they consume content. By offering the
experience of a new alternative, our technology probe breaks our subjects free
of the status quo, encouraging less constrained thinking about their needs and
desires for misinformation moderation.

Our user study sets out to answer three main research questions:

RQ1: What are the facets of users’ information needs that a democratized
misinformation moderation, made possible through our proposed tool, can
address?;

Our first research question aims to _empirically_ demonstrate the potential
for democratization of misinformation governance. Investigating the diversity
of users’ needs after their exposure to the tool helps us understand whether
centralized fact-checking can sufficiently cover the breadth of user needs and
desires, some of which users may be empowered to articulate after they realize
other forms of misinformation moderation may be possible. We focus on multiple
aspects of users’ needs: how important they think it is to avoid
misinformation on various topics, how easy they find it to fact-check content
on various topics, whether they want to see assessments from others, and
whether they want to contribute assessments for the benefit of others. We show
that even in our small-scale study, the distribution of the types of content
people deem important to assess differs across individuals and from what is
typically fact-checked by professional fact-checking initiatives. Moreover,
depending on the topic, users wished for including viewpoints and assessments
from than professional fact-checkers alone—e.g., journalists from other
countries and immigrants on issues related to foreign affairs. These
observations further bolster the argument that there is a need for
decentralized approach to content moderation.

RQ2: What criteria for assessing content accuracy do users use when using our
browser extension?

Examining the rationales that users use in their assessments helps us
determine whether regular people can indeed assess content effectively. By
extending a taxonomy of such rationales with new categories not previously
reported in prior work, we contribute to the literature on user-centered
indicators of content credibility (Zhang et al., 2018; Jahanbakhsh et al.,
2021). Additionally, these rationales can be built into tools to capture
users’ assessments of content credibility in structured form.

RQ3: How do users perceive a browser extension for in-situ signaling and
assessment of misinformation from their customized sources?

  * •

RQ3a. What do they perceive as the advantages and downsides of the approach?

  * •

RQ3b. What are their ideas for improving it and how could they be incentivized
to use it?

Investigating these questions helps us determine whether users find the tool
of value to them and how it can be improved.

The main contributions of our work are: 1) the design of a system for
democratized in-place signaling and assessment of misinformation anywhere on
the web which allows users to moderate misinformation for themselves with the
help of those they trust; 2) a taxonomy of criteria for assessing content
credibility not previously reported in prior work emerged from our study,
which can be used to extend the credibility indicators framework offered by
prior work (Zhang et al., 2018; Jahanbakhsh et al., 2021); and 3) An empirical
understanding of how users use and perceive this system.

This tool provides users with the autonomy to moderate and filter
misinformation in-situ anywhere on the web for themselves and to additionally
help their social circle do so. We offer recommendations for extending the
design and architecture behind this tool to domains beyond misinformation, and
designing a class of tools that further empower users to moderate content for
themselves and choose who they want as their moderator.

##  2\. Related Work

Throughout the paper, we use the term “misinformation” as an umbrella term
encompassing the various types of false information that can be found on the
web per Zannettou et al., including fabricated stories, propaganda, conspiracy
theories, hoaxes, biased or one-sided content, rumors, clickbait, and more
(Zannettou et al., 2019). Here, we situate our work in the literature related
to identifying and dealing with misinformation, tools that allow for
personalizing user’s web experience, and systems that aid in sensemaking.

###  2.1. Identifying and Dealing with Misinformation

Given the impact that misinformation on social platforms has had on people’s
lives and even democracy (Argentino, 2021; Thomas, 2022; Bengali, 2019;
Brennen et al., 2020), researchers as well as platform operators have been
investigating and deploying approaches to detect and counter misinforming
content. The space of these approaches is broadly contained within these two
axes:

  * •

Who gets to decide what is misinformation

  * •

and whether the moderation decisions are contained within a single
platform/website or applied ubiquitously

The arbiters of misinformation can range from a central authority to a body of
a trusted few to a democratized process e.g., through majority votes from
users to each individual.

####  2.1.1. Centralization

The dominant approach is centralization of the arbitration power—in most cases
to the platforms—who decide on both the detection of misinformation as well as
the decision on how to deal with the misinforming content. Centralized
approaches to misinformation detection include using machine learning models
for classifying whether content is accurate (Atanasova et al., 2019; Qian et
al., 2021; Braşoveanu and Andonie, 2021; Zeng et al., 2021), and leveraging
human moderators and third party fact-checking and news organizations (fac,
2021; Ananny, 2018). Centralized approaches to dealing with misinformation
include displaying warning labels next to, down-ranking, reduction (e.g.,
shadow-banning), or removal of the content or suspension of offender accounts
(Bickert, 2019; Constine, 2017; Mosseri, 2016; Perez, 2019; Chowdhury et al.,
2021; Gillespie, 2022; Myers West, 2018).

A body of work has investigated the effectiveness of these centralized
approaches. For instance, Bak-Coleman et al. show that content removal and
account banning can be effective at stopping the growth of misinformation
(Bak-Coleman et al., 2022). Researchers have also reported that warning labels
can increase user discernment of content accuracy or reduce users’ likelihood
of sharing misinformation (Yaqub et al., 2020; Bhuiyan et al., 2021; Seo et
al., 2019). Other studies however, found warning labels have limited or
adverse effects (Clayton et al., 2020; Gao et al., 2018; Pennycook et al.,
2020a).

Nevertheless, the role of platforms as moderators of content and speech is
contested by some researches, scholars of law, as well as users who believe
the downsides of ceding such control to the platforms can be grave. One such
downside is that by assuming the role of a truth arbiter, platforms can limit
users’ freedom of speech and listening and their autonomy in deciding what
content to consume (Jahanbakhsh et al., 2022a; Grafanaki, 2018; Koltay, 2021;
Saltz et al., 2021). Related is the concern that even platform-assigned labels
or messages can be perceived as truth governance and a threat to freedom of
speech (Dillard and Shen, 2005; Jia et al., 2023). In fact, some users
perceive platform labels as judgmental, paternalistic, and against the
platform ethos (Saltz et al., 2021). Another concern is that any centralized
decision, by platforms or otherwise, to for instance, down-rank or filter
misinformation cannot address the needs of every user, as some users want to
be aware of what misinforming content their social circle is exposed to, so
that they can talk to them about it (Atreja et al., 2022; Jahanbakhsh et al.,
2022a; Malhotra, 2020; Rader and Gray, 2015). Yet another concern is that for
these centralized approaches to work, users have to rely on the
trustworthiness, goodwill, and competence of the moderators (McCroskey and
Teven, 1999; Lin et al., 2016); however, some users consider platforms profit-
driven and politically biased (Saltz et al., 2021; Jahanbakhsh et al., 2022a).
These concerns are valid given the history of platforms blocking content that
arguably did not have potential to harm or content by activists or dissidents
in certain autocratic countries (Ivan Mehta, 2022; Fac, 2018; Ghobadi, 2022;
Jan and Dwoskin, 2017; Sankin, 2017).

While the centralized approaches mentioned above are generally contained
within a single platform, some exist that have been applied ubiquotiously.
These include classifiers to label clickbait or sensationalist headlines based
on lexical features that were incorporated into browser extensions that warn
users when they view such a headline (Rony et al., 2018; Chakraborty et al.,
2016).

####  2.1.2. A Select Body of a Trusted Few

There exist approaches that put the power of arbitration in the hands of a
trusted few (e.g., fact-checkers or journalists). ClaimBuster is a system of
this nature, which checks claims in live discourses, news, and social media
posts against a repository of fact-checked claims (Hassan et al., 2017).
Another is ConsidertIt, a system for public dialog where participants discuss
pros and cons of issues, integrated an on-demand fact-checking into the Living
Voters Guide (LVG) deliberation space. Fact-checking was staffed by librarians
and any LVG user could request that any pro or con point submitted by other
users be fact-checked. The fact-checking information would then be placed in-
situ (Kriplean et al., 2014). The interactive fact-checking element in this
system is similar to the one in our tool. Another example is Videolyzer that
enables political bloggers and journalists to assess aspects of quality in
videos including accuracy and bias (Diakopoulos and Essa, 2008). Other systems
have also been developed to help the body of the select few monitor and verify
potentially critical content shared in social spaces (Melo et al., 2019).

While these initiatives are valuable, their effectiveness can be limited by
being constrained to a platform and not incorporated into the systems where
users encounter content. This issue is countered by tools such as NewsGuard, a
browser extension that shows detailed transparency and credibility scores
given by journalists to various sources (i.e., websites) (Lapowski, 2018).
These scores are placed alongside links that users encounter as they browse
the web. A shortcoming of displaying credibility ratings at the granularity of
a source is that not all content from an unreliable source is inaccurate; and
conversely, not all content from a reliable source is accurate (Jahanbakhsh et
al., 2022a). However, for approaches that confine the power to assess
credibility to a limited set of individuals, e.g. journalists, scaling the
investigation and signalling of the credibility at _content-level_ would be
impractical. Even at the source-level, such an approach fails to capture the
reliability of sources that are not notable enough.

####  2.1.3. Democratization

Moving farther away from centralization of the arbitration power, a body of
work has studied how to involve users in the process of content moderation.
Most notably, Twitter Community Notes (previously known as Birdwatch) enables
users to anonymously write “notes” critiquing or adding additional context to
a tweet that they find misleading. Other community members can rate notes as
helpful. Twitter’s bridging algorithm then decides which notes are eventually
shown to users by inspecting whether the raters seem to come from diverse
perspectives (div, 2022). Subreddits and Facebook groups also have moderators
from the user body appointed to the role. In these cases however, the
resulting decision is imposed on all users of the community, regardless of
whether they agree with the outcome or the decision making process.

####  2.1.4. Individualization

At the end of the centralization/ decentralization axis lie approaches that do
not impose a single assessment for content on all users and instead enable
each individual to make more informed decisions about content credibility,
e.g., by relying less on intuition and instead, engaging in critical thinking
(Pennycook and Rand, 2019; Mosleh et al., 2021). These include nudges that ask
users to assess the accuracy of content before sharing it (Jahanbakhsh et al.,
2021; Epstein et al., 2021), subtly priming users to have accuracy on top of
their mind (Pennycook et al., 2021, 2020b), or providing users with checklists
based on recommendations from authoritative sources (e.g., the WHO) to guide
users in evaluating content reliability for themselves (Heuer and Glassman,
2022). Jahanbakhsh et al. proposed a set of design affordances, that they
argue platforms should adopt, which democratize the power to not only
determine what content is misinforming, but also decide what to do about such
content. These affordances include enabling users to: 1) assess content as
part of the data model 2) specify whose assessments they consider trustworthy,
and 3) filter misinformation from their feed as assessed by their trusted
sources. These affordances are intended to empower users in their practices
around fighting misinformation that the authors uncover through a study— users
already ask those who they trust within their social circle to verify the
information that they encounter. And they also provide fact-checking
information to their friends and family (Jahanbakhsh et al., 2022a). Indeed,
users are more receptive to correcting information from their friends than
strangers (Hannak et al., 2014; Margolin et al., 2018). However, platforms do
not support these user practices and sometimes even undermine them.

Jahanbakhsh et al. provided these user affordances through a prototype social
media platform that they designed (Jahanbakhsh et al., 2022a). However, it is
unrealistic to expect that the myriad of websites and platforms where users
consume content will adopt these affordances. Therefore, in this work, we
explore using a browser extension that acts as an overlay on the web, and
allows users to assess the accuracy of any content that they encounter across
platforms, as well as see the assessments of their trusted sources on the
content. Our design deals with operationalizing trusted assessments
ubiquitously and in-situ including where they should be placed considering the
particulars of user’s content consumption practices on the web; e.g., the fact
that many users skim headlines and links to content, but click on them only
sporadically (Boczkowski et al., 2017). In addition, our architecture broadens
the coverage of content that can be assessed beyond articles.

There do exist other tools that both are individualized and whose arbitration
decisions are applied ubiquitously. One is the Reheadline browser extension
that enables users to suggest headlines they deem better for news articles and
specify whose suggested headlines they want to see on the web (Jahanbakhsh et
al., 2022b). Another is Dispute Finder, a browser extension that highlights
text snippets making disputed claims and shows articles arguing for and
against the claim. The disputed claims in the system are restricted to those
in certain fact-checking websites such as Snopes and Politifact. With
centralized fact-checking often lagging behind the spread of false claims
(Starbird et al., 2014), the effectiveness of this tool can be limited when
the user encounters content that is not old enough to have been fact-checked
by fact-checkers of the two platforms. Dispute Finder is individualized as it
is the user who specifies the sources of the articles a user wishes to see for
or against the disputed claims, although users are restricted to websites that
meet the Wikipedia criteria for being reliable (Ennals et al., 2010).

###  2.2. Personalizing Users’ Web Experience

Our browser extension belongs to the class of tools that personalize users’
web experience across platforms. A classic example of such a tool was
WebWatcher, which followed the user’s actions on the web, learned the user’s
interests, and highlighted or added certain hyperlinks on the page for the
user to follow (Joachims et al., 1995, 1997). Within this class, web
annotation tools exist that enable users to add content to webpages where they
do not have authoring permission. One such example is Hypothesis, a browser
extension that allows users to highlight and annotate text and for future
visitors on the page to view and interact with the annotations. Hypothesis
users can make their annotations visible to the public or only within a
private group of users (Hyp, 2012; Kalir, 2019). NB is a similar social
annotation tool for classroom settings (Zyto et al., 2012). Other tools have
also existed that enable users to add comments on a page that are not
necessarily tied to a particular piece of content on the page. Examples
include Google Sidewiki and Eyebrowse (Arthur, 2009; Zhang et al., 2016).

Our extension differs from these tools in two ways. One is that the
assessments, i.e., the annotations or metadata, that are submitted via the
extension are structured. The structure makes it possible to differentiate
assessments arguing for the accuracy of the content and the counter arguments
as well as the questions about the accuracy of the content. The
differentiation makes it possible to show each of these categories differently
in the UI. For helping the user determine content credibility as is the
purpose in this domain, it is important to surface arguments as well as
counter-arguments in a salient fashion. The other difference in this tool is
the fine-grained control that the user has in determining their sources of
assessments, i.e., their trusted sources after the model proposed in
(Jahanbakhsh et al., 2022a).

###  2.3. Systems that Aid in Sensemaking

By allowing users to share assessments with each other, and to contextualize
their assessments in-situ, our browser extension is related to a class of
systems that empower and aid users in the process of sensemaking. The
sensemaking brought about by our tool is in fact distributed—i.e., it
leverages other users’ work without those users directly collaborating with
each other (Fisher et al., 2012). Research has shown that empowering users to
scaffold on each others’ learning can increase the depth of their sensemaking
(Kittur et al., 2014). Of the other work that similar to our case, aid users
in making sense of information, Goyal et al. provided a sensemaking
translucence interface for crime solving, which included a hypothesis window
to promote idea exchange and a suspect visualization for automatic feedback on
suspects discussed in the hypothesis window (Goyal and Fussell, 2016). The
role of visualization in improving sensemaking has been explored in other work
as well, including for representing data links or analysis provenance (Goyal
et al., 2013; Li et al., 2020). Other work has reported on the effectiveness
of creating shared representations between domain experts and the crowd at
increasing sensemaking (Venkatagiri et al., 2019). Close to our scenario,
Kuznetov et al. designed a browser extension with the goal of reducing the
friction of collecting information while remaining in the flow of the
sensemaking process. The extension allows users to access and organize
external content on the web in-situ while preserving the provenance of and the
context around the content (Kuznetsov et al., 2022).

##  3\. The System

To build our system, we extended the open source Trustnet platform presented
in (Jahanbakhsh et al., 2022a). The reason we used Trustnet was because it
already provides an API for managing the data models that our envisioned
extension would use. In this section, we provide a summary of the Trustnet
platform and describe the design of the extension that we built.

###  3.1. The Trustnet Platform

Trustnet is an open source prototype social content sharing platform developed
by Jahanbakhsh et al (Jahanbakhsh et al., 2022a). On this platform, users can
post content or import content from other websites using their URL, _follow_
other users or news media (all generally referred to as _sources_) to see the
content they post on their newsfeed, provide assessment of content, i.e., mark
content as accurate or inaccurate and provide their reasoning, _trust_ other
users, and see the assessments of those they follow and trust on the content
appearing in their newsfeed. The platform also offers a filter which a user
can use to filter the posts in their newsfeed based on how the posts have been
assessed by the user’s trusted sources. Users can also inquire about the
accuracy of posts, and optionally elaborate on their question. A user’s
question about content accuracy is relayed to their trusted sources by
default; although the user can choose to specify from whom they would like
assessments. Users can ask their question about content accuracy anonymously.
On this platform, who a user trusts is kept private to the user. Follow
relationships on the other hand, are public.

We extended the API of Trustnet’s backend to serve the browser extension that
we developed. In addition, we updated Trustnet’s client to work with the
extended backend. Using the extended system, users can sign up for an account
on the Trustnet platform, and specify which other users they trust or want to
follow, and then use the same account for logging into and working with the
extension.

###  3.2. The Trustnet Extension

The design decisions pertaining to the Trustnet extension 111Code available at
https://github.com/farnazj/Trustnet-Extension
Extension on the Chrome web store:
https://chromewebstore.google.com/detail/trustnet/nphapibbiamgbhamgmfgdeiiekddoejo
were made through many rounds of discussion within the research team as well
as pilot tests with a broader group of researchers who volunteered their time
as alpha testers. Here we describe the design as well as the rationales behind
the various decisions. The flow of the user interaction with the system is
shown in Figure 4.

When a user is on a web page, the extension checks the URL (and its
variations222Depending on how the page is served, the same resource can be
accessed with slightly different URLs, for instance with or without index.html
or / concatenated to the end, over both secure and plain HTTP, or using
geolocation IP services to redirect requests to different domain names such as
BBC.co.uk to BBC.com if the requester IP is in the U.S.) to see whether its
associated content has been assessed by those the user trusts or follows. If
there are such assessments for the page, a pane that contains the assessments
pops open on the side of the page automatically. The pane stays open for a few
seconds and then is minimized to a floating button on the top right corner of
the viewport to avoid obstructing the user’s view. The pane and the floating
button color is determined by the assessed accuracy of the content, as we will
later describe. Clicking on the floating button re-opens the pane. The reason
we made the decision to show the pane automatically when assessments were
present, as opposed to letting the user notice the colored floating button,
was because we expected that with a small user base, users would only
occasionally encounter pages which their sources had assessed and we wanted to
prevent “change blindness”. Change blindness is a phenomenon where changes in
a visual scene go unnoticed because of visual inattention. The degree of
change blindness is higher when the observer does not expect a change
(Rensink, 2005), e.g., because change happens infrequently. We deemed it
important to successfully deliver the signal that assessments are present on a
page. With a wider adoption and a higher likelihood of assessments being
present on various pages, users could be given the option to have the pane
minimized from the beginning and open automatically in certain situations, for
instance, when the page has been evaluated as inaccurate. For ease of
discernment, the color of the pane as well as the floating button signals the
accuracy status of the content: green for accurate, red for inaccurate (Figure
1), and orange for split opinion—where some of the user’s assessors have
marked the content as accurate and others as inaccurate (See Figure 4).

The accuracy status of the page (and hence the pane’s color) is determined by:
1. the assessment that the user has submitted for the page, if any. 2. In case
the user has not assessed the page, the assessments from those that the user
trusts if any exist. 3. If there are no such assessments, the assessments of
those the user follows. A page is determined as accurate if _all_ the
assessments of the relevant assessors (i.e., the user, or those that the user
trusts, or those that the user follows in absence of trusted assessments) have
evaluated the page as accurate. The same is true for inaccurate. If there is
disagreement among the relevant assessors, the page’s accuracy is determined
as “split opinion”. If the page has no assessments from either the user or
those that the user trusts or follows, the pane contains no assessments and
its color is grey.

We chose the green for accurate and red for inaccurate color scheme to be
externally consistent with fact-checking platforms such as Politifact and
Snopes. We selected the (yellow-)orange color for split opinion because it is
halfway between green and red on the color wheel. Another design that we
considered for the split opinion status was for the color of the pane to be
determined as a distance between red and green based on the proportion of
assessments marking the content as accurate vs inaccurate. However, in cases
when a minority assessment was outnumbered by assessments arguing the
opposite, it was possible that the color would be so close to either red or
green that it would be indistinguishable. We determined it was important to
signal even a lone outlier disagreement amidst one-sided assessments, and that
perhaps such contrary viewpoint can point the user to the specific trusted
assessor who might help guide them out of an echo-chamber. Therefore, we
decided against using a color gradient.

The same pane also shows inquiries about the accuracy of the content from
those that either trust the user who is visiting the page or have specifically
asked that their question be relayed to the user. The user can use the same
pane to submit their own assessment of the page, update their previous
assessment, or ask about the accuracy of the content. They can also use the
pane to share the content into the Trustnet platform after they have assessed
it or asked about its accuracy. By sharing the content, other users who follow
the sharer will see the content in their Trustnet feed along with the sharer’s
assessment of it. To help users expand their network, the pane informs the
user if there exist assessments on the page by someone the user does not
follow or trust. The pane shows up to 10 such sources who are trusted by the
most number of users platform-wide. The user can choose to follow any of these
sources if they wish to see their assessments on the page. Users can expand,
collapse, or close the extension pane.

Figure 4. The flow of user interaction with the Trustnet extension, from
signing up to assessing and seeing assessments from others.

The image shows 9 interactions with the Trustnet system: 1. After the user
installs the extension, on the first tab that they open, the Trustnet
extension pane appears on the corner, and asks the user to log in or sign up.
2\. For sign up, the user is redirected to the Trustnet platform. 3\. Once the
user signs up on the Trustnet platform, they can log in. The platform informs
them how they can browse through sources and select any they want to follow or
Trust. User can log into the Trustnet extension using the same credentials.
4\. With the extension running, user will see a floating button in the corner
of any page they visit. The color of the button signifies the accuracy status
of the page as assessed by the user’s sources. Grey means the page is
unassessed. 5\. By expanding the pane using the button, the user can input
their assessment of the page. 6\. The user can assess the content as accurate
or inaccurate, or they can ask about its accuracy. 7\. If a user is on a page
that has been assessed by their sources, the assessments side-pane
automatically opens for a few seconds and shows the assessments. The color of
the pane signifies the accuracy status of the page as assessed by the user’s
sources. This is a page that some of the user’s assessors have marked as
accurate and others as inaccurate. Questions asked about the accuracy of the
content are shown at the top of the side-pane. 8\. The color of the floating
button in the corner matches the color of the pane. 9\. If the user is on a
page that has links to content assessed by their trusted sources, the
extension places an icon indicating the accuracy status of their associated
resource next to each link. Here, the green checkmark indicates that the
content of this link has been assessed as true.

An advantage of using URLs to bind assessments to and find assessments of
content is that they are universal. Any resource on the web including news
articles, social media posts (e.g., Facebook posts and tweets) and even their
comments, Youtube videos, etc. has a unique URL and therefore the user can see
its assessments by their social circle when navigating to the content’s URL.
However, many of these resources are usually embedded into a feed (e.g.,
social media posts) or are presented as links on an aggregator page (e.g., the
front/home page of a news website). Prior work has reported that links, for
instance on social media, are often not clicked, even when they are shared
(Gabielkov et al., 2016). Therefore, showing the assessments of a page when a
user visits the page will not benefit the multitude of those users who do not
navigate to the page. To address this issue, the extension also checks the
links on the page that the user is presently reading, and places an icon
indicating the accuracy status of their associated resource next to each (if
there exists assessments for the resource from the user’s network) by slightly
modifying the rendered page (see Figure 3). The extension further places a
question mark next to the links about the accuracy of whose content other
users have inquired. We additionally made the design decision to reduce the
salience of links assessed as inaccurate by making them look faded (somewhat
transparent but still visible), as seen in Figures 2 & 3. The motivation for
reducing the visual salience was to reduce the “illusory truth” effect, in
which a single prior exposure to a headline increases subsequent perceptions
of its accuracy (Pennycook et al., 2018). By attempting to bias eye movements
(a proxy for overt attention) through reducing the visual salience of content
assessed as inaccurate, we aimed to make it easier for users to ignore links
that are misinforming (Itti and Koch, 2001). In effect, we tried to introduce
a design friction as a disincentive—a small hurdle that users would need to
overcome to read the content (Ohm and Frankle, 2018). Nevertheless, users can
still consume the content if they wish to as the content is still where it
would otherwise be and is visible.

####  3.2.1. Dealing with Link Complications

A complication of finding assessments of outgoing links on a page is that
these links cannot be used as they are found on the rendered page to check
whether they have associated assessments in the Trustnet system’s backend. The
reason is that many links go through (sometimes multiple) redirections before
finally redirecting to the link from which the resource can be fetched. For
instance, a link shared on X (formerly known as Twitter) is automatically
processed and shortened to another with the format of https://t.co/xxx. Links
on other websites may also first redirect to an intermediary URL so that the
website logs outgoing links (i.e., which link a user clicks to leave the
page).

To address the issue of redirections, when the extension encounters links on a
page, it recursively follows each 333In order to protect against broken
looping links, there is a maximum depth after which the extension abandons the
pursuit. until it encounters the final link that contains the resource. The
reason it is the client rather than the server that follows the redirects is
three-fold: First, the number of links a user encounters on any one page can
be large, and if all the links encountered by all the users are sent to the
server so that it can follow their trail of redirects, the process can incur a
substantial delay as there is a limit on the number of parallel requests the
server can send and therefore, it would need to batch the requests
sequentially. Second, it is conceivable that many of the requests across users
are to the same websites and if a large number of requests are made to these
websites by one or a few servers, the servers can get rate-limited. Third, the
information to which the client has access allows it to visit some links that
the server cannot, for instance, those that are behind paywalls. Nevertheless,
the client cannot follow all the links as there are occasions when upon
following the redirects, the browser encounters a Cross-Origin Resource
Sharing (CORS) issue. The CORS issue typically occurs when a web application
running in one domain (the “origin”) tries to make an HTTP request to a
resource located on a different domain. If the server hosting the resource
does not explicitly allow the requesting domain to access it, the browser will
block the request. This for instance, happens on Facebook or Twitter where the
encountered links are shortened links from the social media domain but the
target links belong to other domains which enforce the CORS policy 444For
example: https://t.co/CUnCxRezGn (origin on Twitter) -¿
https://www.cnn.com/politics/live-news/election-live-updates-11-07-23/
(resource on CNN.com). Because CORS is only enforced in the browser, these
links are sent to the server for following.

It is likely that the links which have been encountered and whose trails have
been followed will be encountered again by the same or other users. Therefore,
to boost future performance, after following the links and fetching their
target links, the client creates a mapping of the (cleaned555The extension
first cleans the links found on the page, for instance by adding missing
protocol or hostname.) original to the target links and sends it to the
server. The server then caches this mapping. When visiting a page, the
extension first sends the encountered links on the page to the server and asks
whether it holds a mapping for any of them, as fetching the targets is faster
this way. With enough people using the extension, the load of following the
link redirects on any one client becomes lower, as it will be more probable
that the server already knows a mapping for at least some of the requested
links. The server discards the mappings that have not been requested for some
time.

Most redirects are either HTTP redirects (returning HTTP code 300) or HTML
redirects (where the URL to follow can be found in a meta element). Another
redirection method is doing so in the JavaScript running on the page which is
harder to detect. We wanted the extension to be general-purpose and to work on
as many platforms as possible. During development, we tested the extension on
numerous news and content sharing websites where we anticipated users would
want to post assessments. One where we found JavaScript redirection was Google
News. We made specific accommodations to fetch redirect URLs from returned
HTML of Google News URLs.

Many URLs have query parameters appended to them that are usually optional key
value pairs customizing the query. For instance, clicking on an article link
on Facebook will append a query parameter of the form ?fbclid=xxx to the
article’s URL, which is used for tracking purposes. Before requesting
assessments for URLs or requesting mappings of them from the server, the
extension strips the URL of these query parameters because they do not play a
part in identifying the resource and their inclusion would result in a false
miss in the cache. However, we found a few websites where we anticipated users
may want to use the extension and which unconventionally use query parameters
to distinguish a resource. These websites include Youtube (where video URLs
are of the form https://www.youtube.com/watch?v=xxx), Facebook photos, videos,
and comments (but not for instance, Facebook posts), and Hacker News posts
(where post URLs are of the form https://news.ycombinator.com/item?id=xxx). We
made specific arrangements so that the extension treats query parameters from
these websites as a resource.

The set of links on a page can change even as the URL of the page is constant,
for instance, because more content is loaded as a result of an infinite
scroll. The extension watches the page for changes and reacts by looking for
new links for which to fetch assessments.

The extension additionally has an Options page where the user can specify
their blacklisted domains, i.e., where they do not want the extention to run.

##  4\. User Study of the Tool

We recruited participants to work with the Trustnet browser extension and the
Trustnet platform. We advertised the study on a behavioral research platform
managed by our institution with a pool of diverse participants. We asked those
who were interested to fill out a survey which in addition to describing the
study and providing the consent form, asked about the news sources
participants consumed and the subreddits, Facebook groups, and Twitter
accounts that they frequented or followed which discussed news. We collected
this information to test the extension on these websites or subcommunities
prior to the onset of the study and resolve any potential issues. In the
survey, we also asked prospective users to refer others in their network who
might be interested in participating because the tool can best be leveraged
among members of a social circle with pre-existing trust relationships.
However, we were unsuccessful in recruiting those that were referred. The
survey additionally asked for demographic information, adopted from prior
studies on misinformation (Jahanbakhsh et al., 2021, 2022a).

For onboarding, we asked that users watch a short video tutorial explaining
the systems and their features, sign up for an account on the Trustnet
platform (which would enable users to log into the Trustnet extension as
well), and add the extension to their Chrome browser through the Chrome Web
Store. To bootstrap the study, we configured every user to initially follow
all other users of the study upon signup, so that they would be able to see
each other’s assessments. Users could later choose to unfollow one another. We
asked that users perform the following tasks each day during the two-week
period of the user study:

  * •

Assess the accuracy of two pieces of content per day. These could be news
articles, tweets, Youtube videos, or any other piece of content that the
participant wanted. We encouraged our participants to assess as many more
pieces of content as they liked.

  * •

Check out the Trustnet feed to see what the users of the study share every
day. The reason was because through simply using the extension, with such a
small user base and varied interests, it was unlikely that users would
serendipitously run into each other’s assessments as they were browsing the
web. We encouraged, but not required users to share content to the Trustnet
feed through the Trustnet extension. The reason we did not require sharing was
because a valid use of the system—a feature missing in existing social
platforms—is assessing content as misinformation for the benefit of whoever
comes across the content, but not wanting to further increase its visibility.

At the study’s conclusion, participants completed two surveys asking about
their experience with assessing, seeing assessments from others and their
perceptions of the tool. The survey included questions such as “To what extent
did you like being able to assess content” and “To what extent did you like to
see the assessments of other users?” with 5-item likert scale responses as
well as free-text elaboration. To better understand users’ thought process in
assessing content, the survey showed each user examples of content they had
assessed as inaccurate and asked them to explain the characteristics of
content they assessed as (in)accurate. The questions on the experience of
assessing included if users believed their assessments helped others and what
content they had difficulty assessing if any, among others. The questions on
the experience of receiving assessments included how well-thought-out users
found others users’ assessments and their accounts of disagreements with
others. Some of the survey questions were inspired by prior work on
democratized misinformation moderation (Jahanbakhsh et al., 2022a).

To compensate participants, we randomly selected 4 via a raffle and awarded
each with a $100 gift card. After analyzing the results, we observed that
users had assessed a wide range of content on different topics, not
necessarily limited to political news. However, their responses in the post-
study survey centered around assessment of political content, possibly because
a few questions had primed them to have politics on top of their mind.
Therefore, we sent our participants another follow-up survey to further
understand their thoughts on how useful they would find assessments on
different types of content and their current as well as desired fact-checking
practices around different topics. This survey included questions such as “How
important is it for you, if at all, to avoid inaccurate information related to
each of these topics? (likert scale)”, “Who (if anyone) would you like to see
assessments from on each of these topics? Please elaborate”, and “to what
extent do you think you can contribute assessments that can help others on
each of these topics”. Participants were each compensated with a $10 gift card
for completing this follow-up survey. The questionnaires are included in the
Supplementary Materials. The study was approved by our Institutional Review
Board.

###  4.1. Participants

A total of 32 users participated in the user study, of whom 25 submitted
assessments for 3 or more days. View the full participation plot in Appendix
Section A. Out of all the participants, 21 completed the post-study survey and
25 completed the follow-up survey asking about assessments on different
topics. 66% were female and 25% were male. 28% identified as Democratic, 19%
as Republican, and 53% as Independent. The median age was 26 (ranging from 19
to 67), the highest degree achieved Bachelor’s degree (ranging from high
school diploma to Doctoral degree or equivalent), and the median income
$60,000 - $69,000 (ranging from less than $10,000 to $150,000 or more).

##  5\. Results

For categorizing the themes surfaced in users’ qualitative data such as their
assessments or answers to the surveys, one member of the research team made
multiple passes over the data and used open coding to assign codes to the idea
units and axial coding to consolidate the themes (Strauss and Corbin, 1998).
These codes were refined through subsequent passes over the data by constant
comparison and theoretical sampling (i.e., building a temporary theory and
then collecting more data to test that theory), resulting in the emergence of
more generalizable concepts. Then through selective coding, the researcher
focused more narrowly on codes of topical interest considering the question at
hand (Muller and Kogan, 2010), e.g., whether there is potential for
democratization or personalization of misinformation moderation. During the
analysis stage, we followed up with some participants via email to ask them to
clarify some of their responses. As described, our use of grounded theory was
interpretive and aimed to yield concepts and themes; therefore, agreement or
measures of inter-rater reliability were not appropriate (McDonald et al.,
2019).

###  5.1. The Breadth of User Needs Supports the Case for Democratized
Misinformation Moderation (RQ1)

####  5.1.1. Users Differ with Each Other and with Fact-checkers on the
Importance of the Accuracy of Various Topics.

Responses in the follow-up survey revealed that users not only differ in the
topics that they consume, but also to what extent they find it important to
avoid misinformation related to each topic. For instance, while some said that
the accuracy of any content matters to them, others considered certain topics
of less relevance or importance to them; some believed certain topics are
inherently non-factual or controversial, or that it is impossible to find
unbiased news related to certain topics. Figure 5 shows the perceived
importance of avoiding misinformation related to different topics averaged
among our participants. The accuracy of health-related and political content
was at least of moderate importance to most users. However, even in our small
user sample, there was some variation in how important they found the accuracy
of other topics compared to the topics that are often scrutinized by fact-
checkers. For instance, one participant deemed it more important for them to
avoid misinformation on spiritual content compared to political or scientific
content. Another considered the accuracy of content on lifestyle, diet,
nutrition, and beauty more important than that on foreign affairs. The status-
quo of centralized fact-checking constrained to a limited set of professionals
is unlikely to match with the breadth of the types of content for which
different users want assessments.

Similarly, the ease of fact-checking content related to each topic, and
therefore the need for the involvement of external fact-checkers, varied
across users. Some participants explained that they fact-check by reading
articles on a topic from different sources (N=6); others relied on their
trusted news sources to have done their research (N=3); some said certain
topics are harder to fact-check because they are rarely presented objectively
or that they do not have objective standards by which they can be assessed
(N=4), they are complex and the user does not have the required background
knowledge to fully understand them or that the knowledge in the field is
constantly evolving (N=5), or that it takes a great deal of effort to assess
the accuracy of an article on the topic along various criteria such as missing
information and statistics (N=4). Figure 5 shows how easy participants on
average find it to fact-check content on various topics.

Figure 5. The extent to which participants found it important to avoid
misinformation on various topics, and how easy they found it to fact-check
information related to the topics they reported at least a little important to
avoid misinformation on (rating ¿= 2), on a scale of 1-5. Each user answered
these questions only on those topics that they reported they consume.

The figure shows means and standard errors around the means for participants’
responses to the importance of avoiding misinformation on various topics as
well as the ease of fact-checking content on those topics. The topics include:
medical & health, political, foreign affairs, science & environment & tech &
engineering, business & economy, lifestyle & diet & nutrition & beauty,
sources from outside the US or non-English sources, entertainment & celebrity
news, spirituality, sports, and gaming. The order of the means for the average
importance of avoiding misinformation from highest to lowest is the order of
the topics mentioned previously.

> “The more dense topics such as medical questions or in-depth political
> issues require a lot of foreknowledge or quick education to understand what
> is being discussed before you can fact-check or make an opinion on
> something.” (P25)

####  5.1.2. There is a desire and potential for including others in providing
assessments, than professional fact-checkers alone.

The viability of an ecosystem that involves users beyond professional fact-
checkers in misinformation moderation is dependent on two factors: 1) Whether
users would want to view assessments from such assessors, and 2) whether they
can contribute assessments that can help others.

To investigate the first aspect, in the follow-up survey, we asked users from
whom they want to see assessments on content related to each topic in an
extension like the Trustnet extension that allows them to see assessments from
those they follow and trust. We have summarized the responses per topic in
Table 1. For all of the topics, users wanted to see the assessments of experts
and professionals as well as other users. However the notion of what
qualifications the experts or what characteristics the users should have
varied across participants. These responses corroborated prior work reporting
that users want to and do seek the judgment of a variety of different sources
they consider trustworthy to determine whether content is credible
(Jahanbakhsh et al., 2022a).

What we additionally uncovered was that depending on the topic, participants
wished to also view assessments from a broader set of individuals. For
instance, on political content, participants liked to see assessments from
users with certain or diverse demographics or political leanings or those
users who have been affected by certain policies. On issues related to foreign
affairs, they wished for the assessments of users or journalists from other
countries and immigrants.

Table 1. Participants’ responses to who they would like to see assessments
from on content related to various topics. Examples of assessors belonging to
various categories that participants cited are shown in parentheses.

Fully described in the text.  Topic Professionals & experts Academics &
researchers Users Journalists Friends & family Other Political N=11 (
political analysts, politicians, previous office holders, judges) N=5
(Political scientists, historians, etc.) N=9 (those from diverse demographics
or political leanings) N=5 N=2 N=4 (certain media outlets from all sides,
political activists) Medical, health N=17 (medical specialists) N=9 N=2
(patients) N=1 N=1 Science, environment, tech, engineering N=9 (those in tech,
designers, engineers) N=9 (the researchers who conduct the studies being
written about, peers in the field) N=4 N=2 N=1 Business, economy N=9 (business
analysts, CEOs) N=4
(economists) N=5 (small business owners) N=3 N=1 Foreign affairs N=6
(diplomats, ambassadors, government workers) N=3 N=7 (locals, people on both
sides of issues) N=8 (foreign correspondents N=1 Lifestyle, diet, nutrition,
beauty N=6 (medical professionals, life gurus, etc.) N=3 N=6 (women, those
interested in weight loss or skincare) N=1 N=1 (influencers) Sources outside
the US or non-English sources N=2 N=3 N=4 (immigrants, international audience,
those who speak the language fluently) N=5 N=1 (firsthand sources) Sports N=10
(athletes, coaches, retired sportspeople) N=1 N=2 (fans) N=3 N=1 Gaming N=6
(gamers, e-sports professionals) N=2 Entertainment, celebrity news N=3 N=2 N=4
N=2 Spirituality N=6 (pastors and spiritual leaders from certain backgrounds
or specializing in certain denominations ) N=2 (theologians) N=5 (religious
users or those with similar moral backgrounds as the user)

To investigate the second aspect, we asked our participants to indicate on
which topics they believe they can contribute assessments that can help
others. In Section 5.2, we will further shed light on this aspect by reporting
on the types of rationales they used in their assessments. Two outcomes that
would pose challenges for the viability of such an ecosystem would be if users
believed themselves incapable of assessing or if on the contrary, they were
ready to profess their opinion about credibility of content without
consideration. What we observed however, was that the extent to which
participants perceived their own assessments on various topics helpful varied
considerably and depended on whether they were an expert on, interested in, or
well-read on a certain topic, their confidence in their knowledge, whether
they could trust themselves to be unbiased, and whether they considered
themselves capable of finding accurate and balanced information to pass their
findings to others. Therefore, it was interesting that users exhibited
discernment about when their assessments would contribute meaningfully, as
demonstrated in this quote:

> “I know more about certain topics because I work in that area or because I
> am interested and consume more about those topics. I feel that my
> assessments would be more useful in those areas rather than in areas I have
> no experience or interest in.” (P20)

####  5.1.3. Users Assessed Content from Diverse Sources

Our participants submitted a total of 641 assessments and asked 39 questions
about the accuracy of various pieces of content. To understand what type of
content users would want to assess if given the opportunity, we investigated
the sources to which the content belonged. The sources of content that
participants assessed or inquired about using the Trustnet extension included
various local, national, and global news, hyper-partisan, or misinformation
websites (e.g., BBC, CBS58 Milwaukee, National Review, Info Wars, Common
Dreams), news aggregators (e.g., AllSides, Yahoo News), non-English news
sources (e.g., El País), government websites (e.g., CDC), academic journals
(e.g., National Library of Medicine), publications of various universities
(e.g., NYU Law and University of Bath UK), posts on content sharing websites
(e.g., Twitter, Youtube, and Hacker News), and specialty news and blogs (e.g.,
related to the economy, sports, entertainment and celebrity gossip, diet and
nutrition, home improvement, etc.). Source of other content assessed included
archives and personal content (e.g., email).

The tweets and Hacker News posts that participants had assessed had embedded
links to outside news articles. The assessed Youtube videos were created by
accounts that had between 200K and 10M (in the case of major media
organizations) subscribers. The content that these videos discussed were
diverse including some that touted certain misleading information and
conspiracy theories, others that explained various scientific and historical
events, some that disclosed the unethical practices of various corporations or
the inauthenticity of certain claims such as those of psychics and mediums,
and, because it was making news at the time, a number that discussed Johnny
Depp vs Amber Heard trials, for instance, inviting body language and behavior
experts to “reveal what she really thought”.

###  5.2. Users’ Criteria for Assessing Content (RQ2)

We investigated users’ assessments to understand what kind of criteria and
rationales they had used for evaluating accuracy. To analyze the themes in
participants’ rationales, we first consulted prior work that presented
criteria for assessing content accuracy: the taxonomy of users’ rationales for
assessing news headlines (Jahanbakhsh et al., 2021), the context and content
credibility indicators developed by the Credibility Coalition in an attempt to
define a shared language for classifying misinformation and credibility (Zhang
et al., 2018), and problems with news headlines (e.g., clickbait,
sensationalism, etc.) that users corrected when given a tool that enabled them
to do so (Jahanbakhsh et al., 2022b). To analyze the rationales that emerged
in the assessments (and questions—since some questions also contained
rationales for why the content may or may not be accurate) of our users, we
first partitioned their responses into idea units, resulting in 732 idea
units, each being a coherent unit of thought. Of these, a total of 204 idea
units did not include rationales, e.g., because they were questions about the
accuracy of the content, or they simply summarized the article. We then
assigned codes from this body of related work to the rest of the idea units
when the codes were appropriate, noting which rationales could not be captured
by any of the proposed codes in the related work. The criteria presented in
prior work and discussed by our users included the trustworthiness of the
source, presenting evidence that corroborates the claim, presentation of all
viewpoints, soundness of inference or logic, citation of studies,
organizations, or experts, representative citations, title representativeness,
among other factors. We then used grounded theory as described in Section 5 to
build concepts that could encompass the user rationales that were not assigned
labels. Some of these emergent concepts were generalizations of, and therefore
encompassed, some of the criteria in the prior work. When reporting the number
of citations of rationales based on these more general concepts, the count
includes instances that were originally coded using the criteria from the
earlier work. To test inter-rater reliability for this part of the results,
another coder was trained on the criteria from the developed codebook as well
as those in (Jahanbakhsh et al., 2021; Zhang et al., 2018) that the criteria
in the codebook did not encompass. A sample of the idea units (about 12%) were
labeled by the coder. Both the training and test samples were selected such
that they covered all of the categories. Cohen’s Kappa was 0.63, indicating
substantial agreement (Landis and Koch, 1977).

Here, we elaborate on some of the criteria that are related to those reported
in prior work. We do not elaborate on the rest of the criteria because the
definitions accompanied by the examples convey the meaning well. We have
summarized all criteria along with representative quotes in Table 2. These can
be used as criteria to extend the list of credibility indicators for content
and context developed in (Zhang et al., 2018).

####  5.2.1. Citations are credible.

Two indicators in (Zhang et al., 2018) that are closely related to this
category include citation of organization and studies and quotes from experts
(in the field), as they can add context or support to the article. Our users
however, found content credibly not only through researcher and expert
citations but also from citations by others, including victim’s family,
eyewitnesses of various events, cancer patients (e.g., to attest to certain
symptoms), and others. Therefore, the notion of who is a credible citation
varies by topic.

####  5.2.2. The details or underlying data are disclosed adequately.

This category is closely related to the rationale reported in (Jahanbakhsh et
al., 2021) about why people _believe_ news claims—that evidence presented in
the article corroborates the claim. In (Jahanbakhsh et al., 2021), there was
no counterpart to this rationale for why people would _disbelieve_ a news
claim. Our users however, when discussing the data or details presented in an
article, sometimes reported inadequate or even more than adequate disclosure
of details as a criteria that detracts from article credibility.

####  5.2.3. The Content Discusses a Scientifically Reproducible Process or a
Claim that Can Be Verified.

This category is related to a rationale reported in (Jahanbakhsh et al., 2021)
for why people _disbelieve_ claims—that the claim references something that is
impossible to prove, e.g., using a metric that cannot be measured. In
(Jahanbakhsh et al., 2021), there was no counterpart to this rationale for why
people would _believe_ news claims. In the assessments that our users
submitted, we observed that because a claim could be verified (e.g., through a
scientific process or by cross-checking the evidence), it made users believe
the claim as accurate. In addition, one use case of this rationale that we
observed, and which was not reported in (Jahanbakhsh et al., 2021), was that
at the moment when the article was published or being read, the general
information or evidence at hand was or was not enough to deduce whether the
article was accurate. Or, while the claim in the article was accurate at that
moment, the situation was still evolving and future developments might render
the claim inaccurate, or vice versa.

####  5.2.4. The Content Distinguishes Between Facts and Opinion.

One of the criteria that our users used for assessing content credibility was
whether the content was intended to be a factual piece as opposed to for
instance, op-ed or anecdote, and if so, whether the piece exhibited any
editorialization. Even if the content was editorialized, users considered
author’s transparency about separating the facts and the opinions as a signal
of content credibility. This category is a generalization of a rationale
reported in (Jahanbakhsh et al., 2021) for why users _disbelieve_ news
claims—that the claim appears motivated or biased.

Table 2. The criteria for content accuracy cited by the users of the extension
which were not reported in prior work on indicators of content credibility
(Zhang et al., 2018; Jahanbakhsh et al., 2021). These criteria can be used to
extend the list of credibility indicators.

Fully described in the text.  Criterion Yes No The content distinguishes
between facts and opinion. (N=116) “It’s a good theory; and it’s presented as
such: a theory. Nothing for sure! So that makes it accurately presented.”
(P14) “Presents fact and personal opinion in the same setting without
clarifying appropriately”(P24) The citations are credible. (N=93) “This
article provides references and published articles/data.” (P17) “… the news
source uses an unverified; anonymous Twitter account as its primary source.”
(P1) The details or underlying data are disclosed adequately (not less or more
than needed). (N=71) ‘Provided quantitative data about the number and location
of grocery stores w/in majority and non-majority Black neighborhoods.” (P7)
“I think the inclusion of surveys; statistics; and examples of what makes ads
invasive and their prevalence on the internet through this article proves how
detrimental they can be to a common user’s experience on the internet.” (P10)
“The writing style feels wrong. There are too many details that should have
been withheld; and that gives me reason to think it might be sensationalized”
(P33)
“The article states over 2000 pre-existing brain scans of people with and
without anorexia were used. There are many missing important factors that the
article author did not address- 1. how many of the scans belonged to people
with anorexia (proportions of data); 2. age groups looked at; 3. gender; 4.
length of illness; or length of remission; 5. any other illnesses or pre-
existing conditions
Appropriate sourcing is used here and the author uses good quotes. I just wish
they would have gone a bit more in depth about the actual data; rather than
expecting readers to believe the conclusion right away.” (P26) The content
discusses a scientifically reproducible process or a claim that can be
verified. (N=19) “Explains a process that is scientifically reproducible.”
(P32) “Not entirely accurate. Reporting of a few unattributed quotes that
businessfolks in China are against the govt’s zero covid19 policy. There is no
way of verifying the veracity of this…” (P4) The investigation appears to be
of high quality. (N=17) “Well-researched; numerous sources including the
original professor/director; explanations of both sides of the issues involved
in lithium-sulfur battery production” (P27) “This article is basically a
series of tweets. Reading only a paragraph in will make it apparent that the
title is absolute clickbait. There isn’t a lot of substance here. This was a
missed opportunity to report more about the 260 employees that got layed [sic]
off.” (P26) The relevant disclaimers are reported and are salient. (N=8) “This
article does a good job of vetting the source and study (not peer-reviewed and
only 1 study author). It also mentions the author’s own; self-admitted
weaknesses to the study. Overall this was a balanced take on an unconventional
study (may be more accurate to call the study an analysis instead).” (P26)
“The caveats named in the story should have been placed higher in the content.
This is unfortunately too common among viral health stories.” (P1) The content
answers the main question it poses coherently and stays on topic. (N=5) — “The
focus of the article is a question that the content does not answer or give
any clarity to.” (P1) The visual artifacts (esp the lede image) are
appropriate and do not have a biased undertone. (N=4) “good use of graphs”
(P24) “I could already tell it was biased given the fact that it posted a
picture of a protest instead of the actual pregnancy center that got torn
down” (P11) Fabricating claims in the piece would not benefit anyone. (N=3)
‘There is nothing at stake for misinformation here. :)” (P32) “The study was
done by a group who actively promotes LGBTQ rights. Of COURSE it’s going to be
a flawed study!” (P33)

While our users mostly used the tool to assess content accuracy, in some
cases, they put it to other use cases which points to opportunities for
enabling individuals to annotate content beyond assessment of factuality. For
instance, they used the tool to explain that an article was not factual, but
satirical or an opinion piece. Other use cases included expressing opinions or
sentiments—that a particular story should not have been published (for
instance because it distresses the subjects the story is about or the story is
not news worthy) or conversely, that the story is insightful, important, or
positive, or to express their (dis)agreement with the message of an article.

###  5.3. Participants’ Perceptions of the Tool and the Affordances It
Provided (RQ3)

####  5.3.1. Perceived Utility of the Tool and Its Advatanges and Downsides
(RQ3a)

Figures 6 and 7 show the extent to which participants liked being able to
assess content and seeing the assessments of other users respectively. We
investigated their free-text responses to understand their reasons. The
reasons they liked being able to assess included that it helped them think
about the news in a more analytical way or gauge their trust in a source
(N=5), and that they liked being interactive with the news content they
consumed and the ability to call out content they found biased or misleading
(N=4). The cited downsides of assessing were that it took extra time and
effort (N=4) and that sometimes they find it hard to assess a piece of content
e.g., when there is no obvious cause to think the article might be inaccurate
or worrying that one is biased in one’s assessment (N=3). A frequently cited
reason why participants liked seeing assessments from others included because
users can get to know other people’s perspectives and standards for evaluating
content credibility or that it was fun (N=11):

> “It was really interesting to see what people thought of different articles,
> and especially to read the reasons WHY they thought that. As opposed to
> people just blindly taking a side, I liked to see people actually articulate
> and think through their assessments. It was especially interesting to read
> when people had different views as to whether content was trustworthy or
> not.” (P14)

Other reasons included that by considering whether they agree or disagree with
their assessments the user can think more critically about content (N=2), and
assessments from others, especially trusted sources, help the user decide
whether they should trust an article (N=2). Some participants however,
disliked democratized assessments because they believed people are biased or
incapable of assessing (N=2).

Figure 6. The extent to which users reported that they like the ability to
assess content in the post-study survey.

A bar plot of how much users liked being able to assess content on a 5 point
likert scale from “none at all” to “a great deal”. Out of the 23 responses,
only 1 has indicated “none at all” and 17 have said that they like the ability
at least “a moderate amount”.

Figure 7. The extent to which users reported that they like seeing assessments
from others in the post-study survey.

A bar plot of how much users liked seeing assessments from others on a 5 point
likert scale from “none at all” to “a great deal”. The distribution is almost
uniform.

Figure 8 shows participants’ post-study responses to how much they believe a
tool like the Trustnet extension would be useful to them if adopted by many
people ($\bar{X}=2.57,s=0.79$). In the follow-up survey, we asked participants
a similar question of to what extent they find it useful to see the
assessments of content right on the page. Responses to this question are shown
in Figure 9 ($\bar{X}=2.84,s=1.07$).

Many of the rationales (N=14) people cited for why they (dis)liked seeing in-
situ assessments were in alignment with reasons why they (dis)liked assessing
or seeing assessments from others. Other pros that users discussed were that
it in-situ assessments would save others the work of fact-checking that they
may not have time for (N=2) and they provide helpful feedback to journalists
(N=1):

“Everyone who has looked into certain topics in-depth should have a right to
voice their opinion to others and I think this is a good way of doing so
online. It can help improve journalism as they are getting more direct
feedback/commenting on their sites instead of comments directed at them but on
other social media.” (P25)

Additional cons that they discussed included that they want to think for
themselves, unassisted by anyone (N=4), a sentiment that has been reported in
prior work as well (Jahanbakhsh et al., 2023), and a worry that users may
become accustomed to taking all content with a green checkmark next to it as
credible, and not fact-check content for themselves (N=1).

Figure 8. The extent to which participants reported the tool would be useful
if adopted by many people on a 1-5 likert scale, in the post-study survey.

A bar plot of how much participants believed the tool would be useful to them
if adopted by many people, in the post-study survey. All but 2 of the
respondents said the tool would be helpful to them at least a little. 13 out
of the 23 respondents found the tool helpful at least a moderate amount.

Figure 9. The extent to which participants found seeing in-situ assessments
helpful on a 1-5 likert scale, in the followup survey.

A bar plot of how much participants found it useful to see in-situ
assessments, in the followup survey. All but 4 of the respondents found in-
situ assessments at least slightly useful, and 17 out of the 25 respondents
found them moderately useful.

####  5.3.2. Incentives to Use the Tool, Perceived Problems with, and Ideas
for Improving It (RQ3b)

We asked our participants what would incentivize them to use such a tool, what
they would consider as potential reasons they might want to avoid it, and
their ideas for resolving those issues. We have summarized their responses in
Table 3.

A common concern about the tool was that those who post assessments may abuse
the ability, e.g., by pushing a certain narrative, using harmful language,
engaging in coordinated attacks, etc. Related were concerns about not knowing
an assessor’s political leanings or agenda. In fact, the Trustnet extension
gives users the ability to explicitly specify who they trust and follow and
only shows a user assessments from their trusted and followed individuals. The
user would not even be aware of assessments on a piece of content by others,
even a multitude of bots, if the user does not follow or trust them. The
reason why our participants were concerned about this point may have been due
to the fact that we set up our study such that all our participants would
follow each other by default. Although in our tutorials and our communications
with them, we told them that they could unfollow others or choose certain
individuals as trustworthy, they may not have paid attention to this point.

In their responses, some participants also mentioned their concerns about
specific UI design decisions that we had made. For instance, while some
participants found the green flag next to links assessed as accurate helpful,
a few said that they would not care as much about if a piece of content is
accurate compared to if it is inaccurate. In addition, although some users
found the partial fading of content assessed as inaccurate helpful, others
were against it or considered it a form of censorship. These differences in
opinion across users point to a need to give users controls for customizing
their experience with the actions taken on content once the content has been
classified.

Table 3. Participants’ reasons to avoid the tool and what would incentivize
them to use it.

Fully described in the text.  Argument Example User abuse including biased
assessors, harmful language, assessment bombing of certain authors or pages,
paid experts (N=9) “Assessors would have a lot of power. They would need
incentives to not also become corrupted (push views of outside parties or
their own)…” (P26)
“I’m pretty shy online and I value my privacy and professional life, and
wouldn’t want anything traced back to me if it could be controversial or seen
as too political.” (P14) Time consuming to contribute or read assessments
(N=8) Security and privacy concerns (the extension tracking user activity or
the user posting assessments publicly) (N=3) Lack of interest or knowledge in
various topics (N=2) Reasons to avoid Wanting to develop their own critical
thinking, not wanting to be told what to believe (N=2) User being
knowledgeable and caring about a particular subject, wanting to avoid
misinformation (N=10) “Some kind of points or a reward system could be an
incentive for contributing. If the tool seems useful and valuable, that itself
provides a good incentive too…” (P20)
“Prior to signing up for the platform, if you are going to be someone who
assesses, I think it should be displayed on what their political leanings are.
If they have certain credentials to assess certain articles or topics, those
should be displayed as well. This would give users a better idea on how the
assessor is assessing information. It can instill more confidence if they have
credentials on certain topics as well. ” (P17) Community engagement, having
user’s trusted sources use the tool (N=4) Keeping the assessors in check,
e.g., by detecting and removing or blocking bots and abusers, or detecting
misinformation in assessments of other users (N=5) Monetary compensation (N=4)
Earning reputation points or praise (N=3) The tool should ask and display
users’ political leanings, biases, and credentials on different topics (N=2)
Having a pool of large and revolving base of experienced assessors (N=1)
Incentives Restricting the showing of assessments to topics of interest (N=1)

##  6\. Discussion and Future Work

The universal in-place assessments displayed through the Trustnet browser
extension would be in contrast to expecting the user to actively seek fact-
checking information from external sources for every piece of content that
they encounter. Furthermore, the extension enabling the user to provide
assessment in-place can help others who find the user a trustworthy source.

We observed that our users used the extension to assess a variety of different
types of content on various websites, blogs, and social media, and on various
topics not limited to politics or health. Not all sources were widely known,
but they still have enough consumers for the accuracy of the content they
publish to be consequential. In the existing fact-checking model on today’s
web, the power to assess content is confined to a limited set of individuals
and fact-checking initiatives who do not have the resources to cover every
piece of content published by every source. For instance, a number of Youtube
channels whose videos our participants had assessed as well as some specialty
websites and blogs had enough subscribers for a wide reach but perhaps not
enough to garner attention from the fact-checking organizations. To scale
fact-checking in a world where everyone could be a publisher, we need all the
help we can get, even from regular users and perhaps especially those who have
interest in and knowledge about a particular topic, to sieve accurate from
inaccurate information.

While assessments in our tool are democratized, how often a user views content
with credibility assessments depends on who and how many sources they have
marked as trustworthy. The issue of scaling such democratized trusted
assessments so that a user sees assessments on content more often can be dealt
with in two ways. One is using an AI that learns assessments from a select set
of people (e.g., a user’s trusted associates) and predicts how they would
assess other similar content, such as the AI in (Jahanbakhsh et al., 2023).
Another is to explore whether a more extensive trust network can be built for
each user by leveraging transitivity of trust. Prior work has also examined
trust transitivity in social networks, in the context of e-commerce,
recommender systems, and chat moderation (Liu et al., 2011; Jøsang et al.,
2003; Cobleigh, 2020). Indeed, there is also the potential for a hybrid
approach where the extension delivers professional fact-checking information
to users in addition to that from regular users, but leaves it to the users to
decide which fact checkers to trust.

###  6.1. Design Implications

####  6.1.1. User Criteria for Evaluating Content Accuracy

We reported on the criteria that our users used for assessing content
credibility in Section 5.2. In contrast to other studies that have presented
criteria for assessing content credibility (Zhang et al., 2018) and
(Jahanbakhsh et al., 2021), in our study, users were not restricted to a set
of curated news claims or articles, but could use the tool to submit their
assessment of any piece of content of their choosing. In this in-the-wild
setting, some of the rationales that emerged out of the assessments
encompassed, but were broader than, those previously reported in (Jahanbakhsh
et al., 2021) and (Zhang et al., 2018), and some had not been reported before.

Tools that allow users to evaluate content credibility, such as ours, can be
augmented with a checklist of criteria surfaced in our study as a form of
guideline to nudge the user to reflect on various aspects of credibility, when
sharing content, similar to the proposal in (Jahanbakhsh et al., 2021), or
even when reading content. While in (Jahanbakhsh et al., 2021), going through
the checklist in addition to providing free-text reasoning at the time of
sharing did not lead users to share less misinformation compared to providing
free-text assessments alone, future work should study whether a different set
of criteria such as ours could be effective. Even if the structured checklist
does not reduce the sharing of misinformation, it can still help those who
view the criteria that the assessors have highlighted in structured form, and
perhaps even use them in filtering through content or assessments.

####  6.1.2. Incentivizing Participation

A hurdle in using the tool that participants mentioned is that it can be time
consuming to contribute or read assessments. Future work can investigate how
to further aid users in their sensemaking process, e.g., by providing a
mechanism using which they can collectively cluster, synthesize, and surface
unique information in the assessments, similar to Crowdlines or Wikum (Zhang
et al., 2017; Luther et al., 2015). The tool can then visualize the analysis
provenance so that other users can audit the sensemaking pipeline (Li et al.,
2020). The assessments pane can also be structured to include a section into
which users can clip “evidence”, including links to public hearing videos,
studies, fact-checking articles, etc. that can help establish the veracity of
claims in the content.

Our users also desired some form of compensation or recognition for their
effort. The tool can be enhanced with a reputation system similar to the one
in Stack Overflow where users accrue points based on how others rate their
assessments or questions about content accuracy. To protect against
coordinated attacks against an individual, the reputation score a user A views
for user B would not be a global score, but rather a function of the scores
that A’s trusted sources (or those who have an extended trust path to A) have
given to B’s assessments. In fact, the score system can be used to incentivize
users to write assessments that can reach across the political divide or to
read assessments from others that are not similar to them, e.g., by assigning
a higher weight to the upvote on an assessment from an out-group user compared
to those from the in-group users.

####  6.1.3. Democratizing the Design Space of Actions on Labeled Content

We scoped our approach and consequently our tool to democratizing the power to
categorize the accuracy status of content. Informed by literature, we then
made specific design decisions about what to do with the content that was
labeled as accurate or inaccurate and how to signal its status. Our users
however, were not in agreement about whether they liked these specific design
decisions. For example, while the accuracy and inaccuracy of content were of
equal importance to some, others wished to only be notified of inaccuracies.
These differences make the case for democratizing the choice in the design
space of actions as well. In fact, this is another aspect of moderation that
can be democratized regardless of whether the arbitration itself is. Even if
accuracy labels are generated by a select few, the decision of whether, how,
and in what circumstances users want to view labels can be deferred to users.

###  6.2. Content Moderation on the Client Side

In the current information ecosystem and without much control over choosing
what content they wish to see or avoid, users have submitted to the will of
the platforms and their feed curation algorithms. The power of what
information flows where is much tilted in the favor of the platforms. Our work
is a case study of how we can give more agency to the users and democratize
content moderation on the web, without support from the underlying platforms.
While our tool was focused on enabling users to assess the accuracy of content
and see assessments from their trusted sources, the system can be generalized,
empowering users in a variety of other use cases. For instance, our users
desired to see assessments from users or news outlets with diverse
perspectives or from different sides. Taking this idea further, the
architecture and design of our tool can be used to enable users to provide
alternative sources for content— those with a different political leaning
similar to an in-situ AllSides (all, 2012), those that a user thinks are more
credible than the source at hand, or those that together with the source can
paint a more complete picture of the story. The system can be adopted to label
content that is hostile to various marginalized identities, similar to the
Shinigami Eyes browser extension that colors trans-friendly and anti-trans
social media groups, users, and search results (shi, 2018). The system can be
used to label content with certain valence, for instance “depressing” or
“angry”, so that the user may choose to filter out labeled content if and when
they are not in a state of mind to consume it. Future work can study different
types of democratized governance on these tools, e.g., leaving the decision of
categorization to customized trusted sources specified by the user as in our
study or allowing the user to choose among various sets of moderator bodies
with different standards for what constitutes a positive label. The choice of
whether such content should removed from the user’s view can be given to the
user as well. Indeed, in our study, not every user was in agreement about
whether visual fading of content assessed as inaccurate is an acceptable
design decision.

The user-generated data in such a tool can further be augmented with machine
learning or user-configurable word filters similar to (Jhaver et al., 2022) to
block content on certain topics, with certain valence, or from certain people
that appears on the user’s feed. While users cannot stop the flow of unwanted
and unasked for messages, for instance those that are forcefully boosted to
them (e.g., Elon Musk’s tweets receiving the “power user multiplier” special
treatment (Schiffer and Newton, 2023)), they can leverage browser extensions
such as ours to locally flag such content or remove it from the rendered page.

###  6.3. Broader Social Implications

####  6.3.1. Are regular users able to find trustworthy sources?

The ecosystem that we propose relies on the users to find and vet others who
should be trusted to provide assessments. But is this a challenge that could
render the approach not feasible? We argue that on the contrary, this approach
enables users to do online what they would do in natural settings, as finding
trusted sources and heeding the information that they provide is how people
have always determined the truth. Epistemically, social construction of
knowledge is individuals receiving testimony from trusted sources about
information that they cannot verify by direct observation (Steup and Neta,
2005). On social platforms, users commenting about a post’s accuracy
contribute to the socially constructed knowledge of others who trust them
(Bruckman, 2022). Additionally, the presupposition of democracy is that we
recognize our fellow citizens as having the capacity to participate in
collective governance. However, social platforms, or the web, currently do not
accommodate these age-old practices well.

####  6.3.2. Echo Chambers

An adverse effect of giving users autonomy to decide from whom they would like
to see assessments can be the potential for echo chambers. But does this mean
that instead, a governing body should make decisions that it decrees are in
the best interest of users? Prior work has warned against relinquishing the
power of content moderation to platforms and advocated for user autonomy in
this space and argued that individuals have a moral right to freedom of
listening and against compelled listening (Jahanbakhsh et al., 2022a, 2023).
These rights are delivered to users on some platforms through affordances that
allow them to for instance, tag, rate, or upvote content and filter based on
this metadata. Therefore, such affordances already have precedence on the web.
Another advantage to structuring trust and assessments on the web is that such
tools as ours can deliver features aimed at directing users to trustworthy
contrary viewpoints. In fact, prior work has also reported that users are more
receptive to fact-checking information from friends than strangers (Hannak et
al., 2014; Margolin et al., 2018). In our tool, we made an initial attempt at
encouraging users to expand their network by having the extension inform the
user if there exist assessments on the page by someone that they do not follow
or trust. Because of the sparsity of the data in our small-scale user study,
the recommended sources on a page were those who were trusted by the most
number of users platform-wide. However, with a larger adoption of the tool,
the extension can recommend to a user contrary viewpoints by sources that for
instance, have a history of assessments that align with the user’s, or sources
that are trusted by those that user deems trustworthy.

###  6.4. Challenges of Finding Link Redirects and Possible Solutions

To display user-generated content, for many use cases including ours, it is
necessary to find embedded links on the page that the user is visiting as well
as to which resource each link redirects. A potential complication of the
client following the trail of redirects for many links from the same domain is
that the client can get rate-limited. In our system architecture, because the
server caches the redirects that the clients have found, with a large enough
number of users, the number of links for which each client needs to find the
redirected resource becomes fewer. To further protect against getting rate-
limited, the client does not fetch all the links it finds on the page at once,
but rather batches the requests and spreads them out. We set the delay between
the request batches the same across all websites because many websites do not
disclose their rate limits. This static delay results in suboptimal delay in
fetching the links on some domains that would allow for more frequent requests
and conversely, getting rate-limited on others that only allow for less
frequent requests.

However, this issue can be dealt with in two ways. The first is to keep an
estimate of the rate-limit of each domain on the server and dynamically change
it until the estimate appropriately captures the domain’s actual rate-limit.
This can be done by for instance, additively increasing the rate of requests
the client makes on the domain if it does not get rate-limited, and
conversely, multiplicatively decreasing the rate of requests if it does,
similar to congestion control schemes in networks. The second is to learn
whether the links to internal and external resources on a website are likely
to go through redirections. While the extension currently attempts to follow
all links found on a webpage, the links on many websites in fact—at least
those that direct to internal resources—do not go through redirections.
Identifying these websites after a number of encounters can prevent the
extension from getting rate-limited, decrease the delay of fetching user-
generated content on the links, and reduce the load of caching mappings of
links on the server.

##  7\. Limitations

A limitation of the empowerment brought about by our tool is the inherent
limitation of browser extensions—that they only work on desktop and not on
mobile browsers or within mobile applications. This is unfortunate especially
because certain messaging apps such as WhatsApp have been a hotbed of
misinformation (Varanasi et al., 2022). To partially address this issue, we
can provide databases or platforms such as (Jahanbakhsh et al., 2022a) where
users can copy and paste in-app messages or links to assess them or see their
assessments. However, this solution relies on users adopting this extra step
into their content consumption process.

Another limitation of this tool concerns how Facebook deals with links. When
displaying posts or external links embedded in posts (e.g., posts from news
media), the rendered DOM does not include their URLs, unless as a result of
user action such as the user hovering their mouse over the elements.
Therefore, without this extra action from the user, the extension cannot know
the URLs of the displayed posts or content to fetch their assessments.
Interestingly, embedded links used to have their URL in the DOM in the early
days of the Trustnet extension, but no longer do. The rationale behind this
change in design is unclear. However, it points to the need for active
maintenance of browser extensions such as Trustnet to keep working on hostile
platforms.

While it is conceivable to view a source as generally well-informed in their
assessments, trust in a source may not always be a simple binary choice of yes
or no. Future work should study how users reason about their degree of trust
in various sources and how to incorporate that into such a tool as ours.

##  8\. Conclusion

In this work, we explore democratizing misinformation moderation on the web by
providing users with a browser extension that empowers them to assess any
content including news articles, social media posts, or videos, and to see
assessments from the sources they have marked as trustworthy in-situ. We
evaluated the potentials and the challenges of such a design through a two-
week user study where we asked users to use the tool to assess any content
that they wanted. We observed a gap between the content that users want to
assess and whose accuracy they consider important and the type of content that
is usually assessed by fact-checking initiatives, pointing to a need for
giving users autonomy in this space. Our users perceived value in the ability
to assess content or see assessments from others right on the content, for
instance, because they believed the process helped them think critically about
news. They also had ideas for improving the process such as incorporating a
reputation or a point system to incentive users to contribute assessments. We
also investigated user’s rationales for their assessments and believe they can
extend credibility indicators reported in prior work and can be used for
guiding users in how to evaluate content accuracy. Our work suggests there is
potential for democratizing content moderation on the web through tool such as
ours.

###### Acknowledgements.

We thank all the alpha testers of our tool as well as the users of our study.
In addition, we thank Professor Michael Bernstein for his feedback on the
manuscript.

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