# Mapping the Design Space of
Teachable Social Media Feed Experiences

K. J. Kevin Feng  University of WashingtonSeattleUSA<EMAIL_ADDRESS>,  Xander
Koo  Georgia Institute of TechnologyAtlantaUSA<EMAIL_ADDRESS>,  Lawrence
Tan  University of WashingtonSeattleUSA<EMAIL_ADDRESS>,  Amy Bruckman
Georgia Institute of TechnologyAtlantaUSA<EMAIL_ADDRESS>,  David W.
McDonald  University of WashingtonSeattleUSA<EMAIL_ADDRESS>and  Amy X. Zhang
University of WashingtonSeattleUSA<EMAIL_ADDRESS>
(2024)

###### Abstract.

Social media feeds are deeply personal spaces that reflect individual values
and preferences. However, top-down, platform-wide content algorithms can
reduce users’ sense of agency and fail to account for nuanced experiences and
values. Drawing on the paradigm of interactive machine teaching (IMT), an
interaction framework for non-expert algorithmic adaptation, we map out a
design space for teachable social media feed experiences to empower agential,
personalized feed curation. To do so, we conducted a think-aloud study
($N=24$) featuring four social media platforms—Instagram, Mastodon, TikTok,
and Twitter—to understand key signals users leveraged to determine the value
of a post in their feed. We synthesized users’ signals into taxonomies that,
when combined with user interviews, inform five design principles that extend
IMT into the social media setting. We finally embodied our principles into
three feed designs that we present as sensitizing concepts for teachable feed
experiences moving forward.

social media, interactive machine teaching, feed curation

††journalyear: 2024††copyright: rightsretained††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: 10.1145/3613904.3642120††isbn: 979-8-4007-0330-0/24/05††ccs: Human-
centered computing Empirical studies in collaborative and social
computing††ccs: Human-centered computing Web-based interaction

##  1\. Introduction

Social media feeds shape our everyday online interactions with others. Their
interface designs and affordances define boundaries on how we can engage with
people and content, while their algorithms dictate both what and who we engage
with in the first place. Taken together, they shape our collective behaviors
and social norms, cementing their role as social architects in the digital
age.

The rise of algorithmic content curation and distribution via feeds happened
alongside a key shift in the ownership structure of social networks—in the
past 20 years, social media platforms transitioned away from being hosted by
distributed, independent servers to operating under a small number of private
corporations reaching millions of people worldwide (DeNardis and Hackl, 2015).
The funneling of capacities for algorithmic curation into the hands of a
select few results in what Reviglio et al. call a lack of “algorithmic
sovereignty” (Reviglio and Agosti, 2020). It is by this process that the
complexity and richness of our social realities have been distilled into a
small number of homogenized parameters. In the face of seemingly ubiquitous
promises of in-feed personalization, this has brought about a “personalization
paradox” (Simpson et al., 2022).

Increasingly centralized curation can have significant negative consequences
for users’ agency (Kim and kyung Lim, 2021; Sap et al., 2019; Jia et al.,
2022; Pavlov, 2014; Lukoff et al., 2021; Baughan et al., 2022). Platforms
today often employ a top-down, one-size-fits-all approach when it comes to
platform design and governance. In doing so, they marginalize those who fail
to conform to the platform-wide majority. For example, user groups with
culturally significant patterns of language use may have their content
mislabeled and “downranked” by platforms (Sap et al., 2019; Jia et al., 2022),
while neurodiverse users may find many posts too overwhelming to consume
(Pavlov, 2014). Even users who fit within the majority may get frustrated from
being shown irrelevant content with no efficient way to set controls that
would eliminate such content from their feeds.

Prior work has documented users’ attempts to reclaim their agency by deriving
algorithmic folk theories to probe black-box feed curation algorithms (Eslami
et al., 2015, 2016; DeVito et al., 2018; Lee et al., 2022; Karizat et al.,
2021; Siles et al., 2020) and “teaching” these algorithms to better align with
their preferences through strategic in-feed interactions (Kim and Lim, 2023;
Eslami et al., 2016). The efficacy of these ad-hoc techniques, however, is
often unclear, and using them can even leave users with undesirable feelings
of coercion and manipulation (Burrell et al., 2019). Why might this be? The
problem is unlikely to be rooted in the quantity of feedback that the user
provides to the algorithm—after all, modern recommender systems leverage a
wide variety of both implicit (e.g., content dwell time, mouse movements) and
explicit (e.g., likes, blocks) feedback elicitation techniques (Stray et al.,
2022; Narayanan, 2023; Knijnenburg et al., 2011; Mendez et al., 2017),
sometimes learning user preferences to a startling degree of accuracy (Simpson
et al., 2022). Instead, we posit that this is due to users’ lack of
opportunity to agentially articulate pertinent feedback to the algorithm, and
have the algorithm respond accordingly based on their feedback.

Given this, we draw upon literature in interactive machine teaching (IMT)
(Ramos et al., 2020; Ng et al., 2020; Wall et al., 2019) to chart out avenues
for enabling teachable feed experiences111By “experiences,” we refer to the
combination of in-feed interface affordances and underlying algorithmic
behavior orchestrated by, or enabled using, those affordances. on social
media. IMT proposes an interaction framework by which a user (the human
teacher) without expertise in machine learning can train a model (the
algorithmic learner) to accomplish desired tasks with limited amounts of pre-
labelled data. Applications of IMT in social media settings, however, has been
limited. To extend IMT’s framework to social media, we conducted a think-aloud
study ($N=24$) with users from four feed-based social media platforms with
diverse cultures and affordances—Instagram, Mastodon, TikTok, and Twitter222As
of July 23, 2023, Twitter has been rebranded to X. Since the platform was
still known as Twitter during the study, we will refer to it as so throughout
the paper.—to answer the following question:

> What are prominent signals relied upon by users to judge the value of
> content in their feeds, and are thus amenable to teaching to an algorithmic
> learner?

We define a “signal” as a pair of one feature (a category of information that
can be extracted from a post, such as the author or included hashtags) and one
characteristic (a statement describing the significance of the feature, such
as “is part of a recent fashion trend I’ve been following” for the feature
included hashtags)333For more on features and characteristics, see Section
3.1.2.. We define “value” broadly based on the desired order of consumption:
Post A has a higher value than Post B if the user prefers to see A before B in
their feed.

We find from our study that users leveraged a variety of signals to evaluate
content from their feeds. These evaluations are nuanced in ways that current
preference elicitation methods, such as “likes” or content dwell time, may
fail to capture. Many users also expressed desires for feed experiences
centered around individuals they cared about rather than content-based
recommendations, better management of saved content, and more agency—in
particular self-causality—when curating their feeds. Supplementing our
findings with prior work on IMT, we offer five IMT-inspired design principles
for teachable feeds. Finally, we embody these principles into three proposed
feed designs that serve as sensitizing concepts to catalyze future research in
this area.

Concretely, our paper offers the following contributions:

  1. (1)

Cross-platform taxonomies of prominent signals used to determine the value of
posts in social media feeds, enriched by themes extracted from user
interviews.

  2. (2)

Five principles to guide the design of teachable social media feed
experiences.

  3. (3)

Three proposed feed designs that illustrate our principles and serve as
sensitizing concepts for teachable social media feed experiences going
forward.

These contributions pave a path to equipping today’s social media systems with
novel design patterns to empower agential, personalized feed curation.

##  2\. Related Work

To motivate our work, we review prior literature on agency and algorithms on
social media, incorporating human values into content distribution algorithms
(most notably recommender systems), and interactive machine teaching.

###  2.1. Agency and Algorithms on Social Media

In HCI literature, increasing user agency is often discussed as an
aspirational ideal (Bennett et al., 2023; Lukoff et al., 2021; Zhang et al.,
2022; Baughan et al., 2022; Coyle et al., 2012; Madary, 2022; Purohit et al.,
2023). But how exactly do HCI researchers understand agency? Bennett et al.
(Bennett et al., 2023) surveyed 161 publications across 30+ years of HCI and
identified 4 key aspects of agency: self-causality/identity,
material/experiential, interaction time-scales, and tradeoff between
independence and interdependence. In our work, we draw mostly from the aspect
of self-causality/identity444Hereafter, we refer to this aspect as simply
“self-causality.”, which refers to the level and directness of a user’s
decision-making and action execution in line with their own values. This
concept is similar the concept of agency in cognitive science, which refers to
the sense of deliberately controlling one’s actions and affecting the world
through those actions (Coyle et al., 2012). From a more normative angle, prior
literature has also argued that agency holds intrinsic value as a “fundamental
human need” (Zhang et al., 2022), a “basic psychological need” (Lukoff et al.,
2021), and a “moral right” (Reviglio and Agosti, 2020). We integrate these
perspectives into our work.

Reduction of user agency is a common concern on social media (Narayanan, 2023;
Lukoff et al., 2021; Tristan Harris, 2016). A primary source of this concern
is the opacity with which social media feed algorithms operate. Indeed, these
algorithms are commonly referred to as “black-boxes” in HCI and social science
literature (Christin, 2020; Pasquale, 2015; Bartley et al., 2021; Reviglio and
Agosti, 2020; Lustig et al., 2016). While prior work in explainable AI for
social media has attempted to open the black-box and make the algorithms more
understandable to lay users (Ehsan et al., 2021; Shang et al., 2022; Kou et
al., 2022; Kirchner and Reuter, 2020; Lai et al., 2022; Arnórsson et al.,
2023), even a fully transparent “glass-box” algorithm may still reduce agency.
For one, attempts to explain a complex algorithm may trigger information
overload, weakening users’ decision-making abilities (Poursabzi-Sangdeh et
al., 2021). Transparency also does not guarantee self-causality—users may
watch and understand the inner workings of the algorithm without any
opportunity for control (Lipton, 2018). Transparency aside, widespread user
behavior such as mindless scrolling (Purohit et al., 2023) and dissociation
(Baughan et al., 2022) are telltale signs of users’ agency loss when
interacting with algorithmically-driven feeds.

Dwindling agency has led to users deriving “algorithmic folk theories” to make
sense of their social media experiences (Eslami et al., 2015; DeVito et al.,
2018, 2017; Karizat et al., 2021; Siles et al., 2020; Eslami et al., 2016).
Examples of folk theories include that users will see more content from
friends who are more similar to them in their Facebook News Feed (Eslami et
al., 2016) or that the TikTok For You Page algorithm prioritizes videos that
feature aesthetics associated with wealthier lifestyles (e.g., large houses)
(Karizat et al., 2021). Theories may emerge from both endogenous (originating
within the platform, such as content patterns, friend count, likes, and
comments (Bernstein et al., 2013)) as well as exogenous (originates outside
the platform, such as user location (Burbach et al., 2018)) information
(DeVito et al., 2018; Rozenblit and Keil, 2002).

Upon formulating their theories, users applied them to better align the
algorithm to their preferences through strategic in-feed interactions. For
example, Facebook users regularly visited profile pages of those whom they
wanted to see more of and sought out more opportunities to tag them in posts,
to signal their preferences to the algorithm (Eslami et al., 2016). To attempt
to spread a video to other users’ For You pages, TikTok users watched videos
multiple times (even when they understood it perfectly the first time
through), left longer comments, and hit the like/share buttons repeatedly even
though they could only like or share a video once (Karizat et al., 2021). On
Twitter, users systematically created and shared content with certain words
omitted or misspelled to evade the surveillance of the algorithm (Burrell et
al., 2019). Users, however, remained uncertain of the efficacy of their
techniques and even considered their actions to be manipulative and forced
(Burrell et al., 2019; Eslami et al., 2016).

In our work, we seek to bring forth interactive tuning as a core component of
social media algorithms, instead of treating such interactions as under-the-
radar workarounds for preference and value alignment. We frame these
interactions as teaching to highlight two essential qualities: naturalness and
agency. As teaching is an inherent human ability that we both perform and
receive throughout our lives, we are already naturally acquainted with its
methods and mental models. Additionally, teaching is an agential
activity—teachers are in charge of the teaching curriculum, delivering
concepts to students, and evaluating student performance. Given this, how
might we leverage our well-acquainted mental models of teaching in our online
algorithmic interactions? We look to the paradigm of interactive machine
teaching (Ramos et al., 2020; Ng et al., 2020; Wall et al., 2019) for
inspiration.

###  2.2. Human Values in Recommender Systems

Here, we focus on a particular class of content algorithms ubiquitous on
social media and other everyday applications—recommender systems. A growing
body of work proposes techniques and broader calls to action to embed human
values into recommender systems (Stray et al., 2022; Stray, 2020; Ovadya and
Thorburn, 2023; Dean and Morgenstern, 2022; Friedman et al., 2023; Leqi et
al., 2023), drawing upon the field of value-sensitive design (Friedman, 1996;
Borning and Muller, 2012). Borning and Muller define “value” as “what a person
or group of people consider important in life” (Borning and Muller, 2012). We
borrow this definition of value in our work; we use the term “values555Note
the distinction between “values” and the “value” of a post. The latter is
defined in Section 1.” to abstractly refer to a unique set of important
considerations of an individual or group, and “preferences” to refer to
particular considerations within that set.

Many modern recommender systems infer human values by implicitly learning them
from user information and interaction history (Stray et al., 2022; Narayanan,
2023). Commonly tracked attributes for implicitly eliciting values include
tracking clicks (Zhao et al., 2019), content dwell time (Yi et al., 2014), and
affinity with other users (Kang et al., 2022; Jeff Widman, 2015). Implicit
learning can be desirable as it reduces interface-level friction and allows
for more effortless and rapid consumption of content (Knijnenburg et al.,
2011; Chen et al., 2021). Additionally, advancements in deep reinforcement
learning have improved the robustness and sophistication of implicit learning
to the point that learned systems are, at times, capable of truly reflecting
users’ values (Chen et al., 2021; Lawo et al., 2021). For example, users were
startled by the accuracy with which TikTok’s For You Page algorithm could
capture their preferences without any explicit signals (Simpson et al., 2022).
However, an important downside of implicit learning is its inability to
facilitate user agency. A 2021 investigation of TikTok’s algorithm found that
it quickly led users down niche content rabbit holes towards “fringe” content
(Wall Street Journal, 2021). Others have raised concerns about recommender
systems’ ability to distort speech (Stray et al., 2021; Saveski et al., 2021),
exacerbate polarization (Asimovic et al., 2021), discriminate users and
creators (Lambrecht and Tucker, 2019), and erode mental health (Stray, 2020;
Lup et al., 2015).

Given the concerns around implicit learning, many have sought to develop
recommender systems with explicit controls (Jannach et al., 2016; Knijnenburg
et al., 2011; Ooge et al., 2023; Liang et al., 2023; Dooms et al., 2014;
Petrescu et al., 2021; Friedman et al., 2023; Mysore et al., 2023). Users have
reported higher levels of satisfaction (Jin et al., 2017; Kim and Lim, 2023),
trust (Ooge et al., 2023), and engagement (Liang et al., 2023; Jannach et al.,
2016) when they were given opportunities to exert control over the system,
even when the controls had no impact on the output (Vaccaro et al., 2018;
Bennett et al., 2023). Examples of explicit controls include thumbs up/down
buttons to rate recommended items (Zhao et al., 2013; He et al., 2016),
sliders and toggles for adjusting desired content characteristics (Jhaver et
al., 2023), drag-and-drop topic specifiers (Dooms et al., 2014), keyword
critique (Petrescu et al., 2021), and manual selection of the recommender
algorithm (Ekstrand et al., 2015; Bluesky, 2023). More recent works have
leveraged the semantic capabilities of large language models to enable the
expression of preferences conversationally through a chat interface (Friedman
et al., 2023) and through editing a natural language user profile (Mysore et
al., 2023). Explicit controls come with their own set of challenges—users may
not know that these controls exist or what they do (Hsu et al., 2020; Spandana
Singh, 2020; Jhaver et al., 2023), find them cumbersome to use and keep up-to-
date (Jannach et al., 2016), or do not see value in engaging with them (König,
2022). Indeed, prior work showed that most users prefer a hybrid approach that
combines implicit and explicit learning (Knijnenburg et al., 2011; Mendez et
al., 2017). One promising approach in this vein is to design controls that
allow for simultaneous expression of direct feedback and less direct social
signals; real-world examples include “react” options on Facebook and LinkedIn
(Stray et al., 2022).

A common theme across both implicit and explicit learning is that particular
content features are assumed to be more important to the user—features that
the system then learns on. However, few works have questioned whether those
features are truly ones a user cares about or wants the system to learn when
they consume content. A news recommender may accurately capture (implicitly,
explicitly, or some combination of both) a user’s preferred article length,
but will still fail to align with user values if the user does not care much
for article length. In our work, we seek to elicit which content features
users truly value in social media feeds to orient future work in value-
sensitive recommender systems.

###  2.3. Interactive Machine Teaching

Interactive machine teaching (IMT) is an interaction framework by which
subject matter experts—who are often not experts in machine learning—draw upon
their personal expertise to train machine learning (ML) models that can
operate effectively within their domain (Ramos et al., 2020; Ng et al., 2020;
Jörke et al., 2023; Feng and Mcdonald, 2023; Zhou and Yatani, 2022; Wall et
al., 2019). While conventional ML is primarily concerned with developing
algorithms that automatically learn conceptual representations from training
data, IMT argues that learnable representations should directly come from
human knowledge (Wall et al., 2019; Ng et al., 2020). This way, users feel
more agency while maintaining a firmer grasp of what the model learns, making
models more transparent and debuggable (Ramos et al., 2020; Kim and kyung Lim,
2021). IMT consists of three main stages that form a “teaching loop” (Ramos et
al., 2020):

  1. (1)

Planning: the human teacher (the subject matter expert) identifies a task for
the algorithmic learner (the machine learning model) to complete, along with a
curriculum (set of examples and representations to help teach the learner,
typically in the form of a small dataset).

  2. (2)

Explaining: the teacher shows the learner examples and explicitly identifies
concepts the agent should learn.

  3. (3)

Reviewing: the teacher allows the learner to predict some unseen examples,
corrects any erroneous predictions, and updates the teaching
strategy/curriculum accordingly.

Central to IMT is the teaching language, an interface by which the teacher
crafts expressive representations that communicate desired concepts and are
learnable by an algorithmic agent (Ramos et al., 2020). In order to derive and
use a teaching language, users must engage in a practice known as knowledge
decomposition. Ng et al. (Ng et al., 2020) define knowledge decomposition as
“a process of identifying and expressing useful knowledge by breaking it down
into its constituent parts or relationships.”

IMT shares similar goals with the paradigm of reinforcement learning and, more
specifically, reinforcement learning with human feedback (RLHF) (Ouyang et
al., 2022). Both IMT and RLHF strive to interactively and iteratively embed
specialized human knowledge into a machine-learned system. In RLHF, an
algorithmic agent strives to find a policy (a map of the agent’s states to
actions) that maximizes some long-run measure of reinforcement as defined by a
reward function in a dynamic environment (Sutton and Barto, 2018; Kaelbling et
al., 1996; Christiano et al., 2017), after which human feedback is be used by
the agent to fine-tune the model to act in accordance with the user’s
intentions (Ouyang et al., 2022; Stiennon et al., 2020). The key difference
between IMT and RLHF, however, is that in IMT, the human teacher has
agency—defining the curriculum, explaining concepts, and evaluating
performance—whereas in RL(HF), the human is merely an oracle that the agent
queries to guide its decisions (Christiano et al., 2017). While we see
potential in both IMT and RLHF to better align a feed curation agent with user
values, we seek to center user agency throughout the feed curation experience.
As such, we focus on IMT and defer exploration of RLHF approaches to future
work.

How can IMT inform end-user empowerment in social media feed curation?
Applications of IMT in social media contexts so far have been limited.
Jahanbakhsh et al. (Jahanbakhsh et al., 2023) employed an iterative loop
bearing some resemblance to IMT to train a personalized AI capable of learning
and predicting a user’s misinformation assessments, but identifying
misinformation is just one of many salient dimensions for users when consuming
content in social media feeds. Additionally, higher-level tasks such as
misinformation assessment may be composed of lower-level observations such as
low-quality images or suspicious links that can be informative characteristics
for curating content in other scenarios. Our paper seeks to draw out such
observations through knowledge decomposition to articulate the design space
for teachable social media feed experiences.

##  3\. Method

In this work, we map out the design space for teachable social media feed
experiences through the lens of IMT. Specifically, we sought to understand
what or how users would teach a personalized feed curation agent. To answer
our research questions, we first designed a study inspired by techniques in
knowledge decomposition (Ng et al., 2020) to obtain taxonomies of salient
signals for assessing the value666In this work, we define value broadly based
on what participants want to see before others. Posts that participants want
to see first first in their feeds has the highest value. of the posts in a
social media feed. We supplemented our taxonomies with qualitative interview
data from the study.

###  3.1. Signal Elicitation Study

####  3.1.1. Participants

Our study was conducted with 24 regular social media users between the ages of
18–65. We recruited some participants through the platforms of interest in the
study (Instagram, Mastodon, TikTok, and Twitter; see Section 3.1.3 for more
details) and others through our institution’s Slack channels and word-of-
mouth. Prospective participants indicated which of the four platforms of
interest they used, their frequency of use for each platform, and basic
demographic information such as age range and gender, in our screening form.
We started by accepting participants who used their platform(s) at least a few
times a month, on a first come, first-serve basis. Selected participants then
chose one of the four platforms of interest to use for the study. Later on,
because we wanted similar numbers of participants for each platform, we only
sent invites to participants who had a higher likelihood of choosing a
platform for which we were seeking more participation, based on their
indicated frequency of use for that platform. We stopped recruiting when we
reached data saturation across all platforms.

In the end, we had an even distribution of participants across our 4
platforms, such that there were 6 participants per platform. The far majority
of participants (20) used their platform of choice daily, while 3 used it a
few times a week and 1 used it a few times a month. Half of our participants
(12) were aged 18–24, 6 were 25–34, 3 were 35–44, 3 were 45–54, and 1 was
55–64. 14 identified as women, 8 as men, and 2 identified as non-binary. All
had at least 3 years of experience on social media, with all but one having at
least 5 years and just over half (14) having at least 10 years.

We note that our participant pool may not be a representative sample of social
media users, neither on a per-platform basis nor in aggregate. However, our
study’s intention is to map out a design space illustrating some interactive
possibilities for teachable feed experiences, rather than to make
generalizable assertions about the relative frequency or importance of
observed interactions. We also recognize that our resulting taxonomy is not
exhaustive and new signals may emerge from specialized use cases and
communities.

####  3.1.2. Study setup and procedure

To formalize the knowledge decomposition process (Ng et al., 2020), we defined
a signal as an information unit consisting of two primary components: a
feature and a characteristic (see Fig. 1). A feature is a class of information
that can be extracted from a post. Example features include the post’s author,
the textual content, image(s), the number of likes, and the post’s topic. A
characteristic is a subjective statement that describes a feature. It is
subjective in the sense that its significance may vary between participants.
For example, the feature “the post’s author.” is described by the
characteristic “is someone I know from in-person interactions.” A third,
optional component, an action, is something the participant can perform to the
post in response to a feature-characteristic combination. For example, if the
post author is someone the participant knows through in-person interactions,
they may choose to take an action of “trigger a notification” for posts from
that author. Actions were an optional component of a signal and could also be
triggered by any combination of feature-characteristic pairs.

Figure 1. Examples of signals from our study. On top is a default signal
template that we provide to users, and on the bottom is one completed by a
participant.

Two signals, one on top of the other. Each signal consists of a text element
that reads: ___is/has ___so ___. The one on top contains a blue sticky note in
the first blank with text that reads: [a particular feature of the post]. It
also has a yellow sticky note in the second blank that reads: [some
characteristic]. It contains plain text in the third blank that reads:
[optional display action]. The signal on the bottom contains a blue sticky
note in the first blank with text that reads: the video. It also has a yellow
sticky note in the second blank that reads: shows something related to my
interests (art in the form of singing). It contains an orange sticky note in
the third blank that reads: add label [art].

Participants were asked to compose signals (Fig. 1) and arrange them on a Miro
board.777Miro is an interactive and collaborative virtual whiteboarding tool
(Miro, 2023). Our study was conducted virtually, 1:1 through Miro and Zoom. We
invited each participant onto a copy of the board and interactively co-
authored signals with them in Miro while communicating through Zoom. Before
the start of the study, participants submitted 10 screenshots of content on
their own social media feeds from a platform of their choice out of Instagram,
Mastodon, TikTok, or Twitter. We specifically asked for the first 10 posts
they saw (and were comfortable sharing with us) in their “home”888On Instagram
and Mastodon, this refers to the main feed users see when they log in. For
TikTok, we equated the home feed with the For You Page. For Twitter, we
equated it with the Following feed, as Twitter was just starting to roll out
its For You feed as we were conducting our study. feed. Participants’ posts
were privately confined to their board and were not shared with anyone beyond
the research team.

The study board was separated into 5 main areas; Fig. 2 shows the board in its
initial state. We populated the 10 screenshots participants submitted to us to
area (3) in a random order before the start of the study. Area (1) contained
editable signal templates while area (2) contained optional elements such as
actions and boxes for participants to visually group posts they want to keep
together. Area (4) was a space for participants to construct an “ideal” feed
by organizing their posts into an upper, middle, and lower feed. While a
linear feed may not have this exact distinction, we used it to roughly
separate participants’ perceived value of posts into 3 categories of
descending value. Finally, area (5) was where participants could place any
content that they would like to remove from the “ideal” feed they were
assembling. An example of a Miro board after the activity has been completed
can be seen in Fig. 3.

Figure 2. Areas of the Miro board. (1): signals bank with editable signal
templates. (2): actions bank with sample actions and boxes for grouping
content. (3): area where we uploaded participants’ posts prior to the study.
(4): the upper, middle, and lower feed boxes. (5): area for placing content
that the participant would like to be removed from their feed.

A workspace on Miro, a collaborative whiteboarding platform, with a gray
background and colorful rectangles (2 on top and 5 on the bottom) to indicate
regions where activities will take place. One of the rectangles on top is
called the Signals Bank and contains 10 signals while the other is called the
Actions Bank and contains actions one can take written in text on top of
orange sticky notes, along with boxes to batch together content. Instructions
for the activity are written in text on the left side of the board.

Figure 3. An example of a Miro board after a participant has completed the
signal elicitation study. The posts themselves have been obscured by the
research team to preserve the participant’s privacy.

A workspace on Miro, a collaborative whiteboarding platform, after the signal
elicitation activity has been completed. The structure of the board is the
same as the previous figure. There are 10 screenshots of social media posts
(the contents of which have been redacted for privacy) spread out among 4
rectangles. Each screenshot has 1–2 completed signals next to it.

Using this board, we first asked participants to drag their content from area
(3) into one of the 3 feed sections in areas (4) or area (5). We then asked
them to further elaborate on their organization (why they value or do not
value a particular post) by writing at least one signal using the templates in
area (2) for all 10 posts. We co-authored these signals with participants by
collaboratively editing the text in the signal template until participants
were satisfied that the signal accurately represented their perspectives.
Because the signals conformed to given templates, we relied on participants’
think-aloud dialogue, which was recorded and transcribed, to capture their
more nuanced reasoning. Many participants wrote more than one signal per post
as there were multiple features they took into consideration. After all 10
posts were associated with at least one signal, we allowed participants to
optionally assign actions to the signals.

Upon conclusion of this interactive activity, we conducted a brief interview
where participants reflected on their most frequently mentioned features and
characteristics, as well as general experiences and desiderata with social
media feeds. In total, the study took around 45 minutes to complete.

Our study was reviewed and approved by our institution’s IRB under Study
#00016757. All participants received a $20 USD gift card after completing the
study. All studies were conducted virtually on Zoom between January and April
2023, audio recorded, and transcribed.

####  3.1.3. Platforms

Our participants submitted content from their choice of one of 4 platforms:
Instagram, Mastodon, TikTok, and Twitter. We were interested in these
platforms as they were already popular or were gaining popularity at the time
of the study, and also varied along two dimensions of interest: content format
and engagement optimization. Content format refers to the primary format with
which content is displayed and consumed on the platform. For Mastodon, this
was text, while Twitter uses a combination of text and visual media (images
and videos). Instagram is mostly image-based, while TikTok is mostly video-
based. Engagement optimization refers to the extent to which a platform draws
from its broader content pool based on a user’s prior engagement with a piece
of content, rather than relying solely on those the user follows. Mastodon is
strictly reverse chronological and has zero engagement optimization, while
Twitter provides users with a choice of viewing an engagement-optimized “For
you” feed and a more lightly optimized “Following” feed. Instagram offers an
engagement-optimized home feed by default. TikTok is well-known for its
engagement optimization on its For You feed (Wall Street Journal, 2021).

We recognize that users may choose to engage with these platforms for
different reasons—for example, one may log onto Instagram to share pictures
from their personal life and catch up with friends, while only logging onto
Mastodon for professional networking. We see this as a potentially rich source
of insight in our study. Feed design and affordances are heavily influential
in shaping perceptions of what a platform is best used for (Kender and
Frauenberger, 2022), and hearing participants’ varied interaction strategies
across different use cases can help us better envision the design principles
and affordances users may seek given a particular use case or goal.

###  3.2. Data Analysis

Our study generated data in the form of 411 signals (with some repetition
within and between participants) and 24 interview transcripts. We analyzed the
two data sources separately while noting complementary and contrasting themes
between the two.

####  3.2.1. Signal data

After all studies were completed, the first author aggregated signals across
all participants and processed them into a spreadsheet to preserve pairing
between features and characteristics. Within each group, the first author
separated features or characteristics referring to an account from which a
post was made from those referring to a post’s content,999This step was
necessary for a coherent analysis as the two were fundamentally distinct (see
Section 4.1) looking up the pairing in the spreadsheet as necessary.

We used a hybrid approach of inductive and deductive coding (Fereday and Muir-
Cochrane, 2006) to transform our data into taxonomies. The first author
inductively sorted features and characteristics into broad themes through
affinity diagramming; this process was independently performed on features and
characteristics. Results from our inductive analysis were discussed and
iterated upon with the broader research team at weekly meetings. For features
specifically, the first author referenced common information available on
interfaces of social media posts (e.g., username, handle, like/reshare/comment
buttons) to further code the features in a deductive manner. Initially, the
coding was not informed by the need to accommodate multiple platforms and some
platform-specific features, such as lists on Twitter and Mastodon, were
included. However, it became clear as coding proceeded that the vast majority
of features were shared across all platforms in our study. As a result, we
incorporated platform-specific codes into platform-agnostic ones—for example,
the “list” code was split up and merged into multiple account-related features
depending on how participants discussed their use of lists. We instead relied
on our interview data to capture discussions of platform-specific experiences.

Our initial round of coding produced 7 and 16 account-related features and
characteristics, respectively, and 20 and 31 content-related features and
characteristics, respectively. The results seemed excessively granular for a
taxonomy, so we iteratively grouped our codes into higher-level labels,
discussing with the rest of the team as necessary. Labels that fell outside of
this paper’s scope were also eliminated. Our final set of labels contain 4 and
10 account-related features and characteristics, respectively, and 9 and 18
content-related features and characteristics, respectively. Features and
characteristics defined the vertical and horizontal axes of our taxonomies,
respectively. We did not include an analysis of the “action” component of
signals as participants used them sparingly in the study. The definitions for
all labels in our taxonomies can be found in our Supplementary Materials.

The intersection of each feature-characteristic theme was tallied and marked
with either “$+$” to indicate that this combination had positive value
(increased a post’s perceived value), “$-$” to indicate negative value
(decreased perceived value), or “$\circ$” to indicate that there was no
consensus among participants. Although we tallied exact counts, we expressed
them as broader value ranges using saturation maps in the taxonomies to
embrace the qualitative nature of our data and avoid invoking notions of
generalization or numerical comparisons based on our counts.

After our aggregate taxonomy with data from all platforms was finalized, the
first three authors then completed the same taxonomy using disaggregated,
platform-specific data. We separated the signals by platform and deductively
coded the features and characteristics using the themes we developed from the
aggregate data, resolving any disagreements in weekly data analysis meetings.

####  3.2.2. Interview data

We took an inductive approach to performing thematic analysis on our interview
data. Interview transcripts from the studies were initially auto-generated
from Zoom recordings, after which the first author manually reviewed them
while referencing the audio recordings to correct any incorrectly transcribed
text. The first author took a first pass of open coding over the data,
identifying insightful regions of the transcript and possible themes for
further analysis. The first three authors then developed a codebook containing
themes from open coded data that the team agreed were relevant and
interesting. This codebook was used to collaboratively code the transcripts,
with disagreements resolved through discussions among coders. Coders also
wrote summary memos from the themes and discussed them with the broader team
at weekly meetings, iterating on the themes and memos as necessary. Initially,
the codebook consisted of 7 high-level themes synthesized from over 20 sub-
themes. After some deliberation among the team, some high-level themes were
combined and sub-themes were remixed to form the 5 high-level themes in the
final codebook. Our final codebook is shown in Table 1.

Theme |  Description
---|---
Passive and active use of feeds |  Participants’ engagement in passive browsing, active consumption, or a combination of the two patterns.
People- vs. content-centric feeds |  Participants’ (often platform-specific) preferences for people-centric or content-centric feed experiences.
Unactualized value of saved content |  Participants’ strategies and pain points in using existing archival features to to revisit content on platforms.
Nuanced evaluation of posts |  Participants’ consideration of positive and negative facets of a post, as well as factors outside the post context, when judging a post’s value.
Self-causality and lack thereof |  Participants’ experiences with (the lack of) algorithmic control and transparency while curating content.

Table 1. Our 5 themes based on analysis of the signal elicitation study
participants’ responses

A 2-column table with headings Theme and Description. There are 5 rows with
the 5 high-level themes alongside their descriptions.

##  4\. Findings

We begin this section by presenting our taxonomies for account- and content-
based features and characteristics. We then showcase findings from our
transcribed data, some of which complement our taxonomy data while others
reveal what our taxonomies could not capture by themselves. Throughout this
section, we use a two-letter abbreviation (IG for Instagram, MA for Mastodon,
TT for TikTok, and TW for Twitter) to indicate a participant’s chosen platform
in the study.

###  4.1. Taxonomies

Our analysis yielded two distinct sets of taxonomies: account-based (with
features and characteristics relating to accounts that post and engage with
content) and content-based (with features and characteristics relating to
content that accounts post). We made this distinction as the two sets were
fundamentally different in nature—characteristics from one were incompatible
with features from the other, and vice versa. Definitions for all labels used
in our taxonomies can be found in our Supplementary Materials.

Figure 4. Our account-based taxonomy, aggregated across Instagram, Mastodon,
TikTok, and Twitter.

A gridded grayscale table with 4 rows and 10 columns, excluding row and column
labels. The cells are either not filled in, or filled in with either light,
medium, or dark gray. The cells that are filled in either have a $+$, $-$, or
$\circ$ in them. The top row is filled in with mostly dark gray cells that
contain $+$.

Figure 5. Our content-based taxonomy, aggregated across Instagram, Mastodon,
TikTok, and Twitter.

A gridded grayscale table with 9 rows and 18 columns, excluding row and column
labels. The cells are either not filled in, or filled in with either light,
medium, or dark gray. The cells that are filled in either have a $+$, $-$, or
$\circ$ in them. Cells that are filled in are distributed throughout the
table, mostly shaded light gray containing a $+$.

Fig 4 shows our account-based taxonomy with data aggregated across all four
platforms. We see participants rely heavily upon the original poster as a
feature when evaluating a post. This feature is described with a wide range of
characteristics—that is, there are diverse reasons why participants may pay
attention to the original poster. Some characteristics, such as “knows
personally” depend on personal context and even offline interactions the user
may have had with the account holder. Other prominent characteristics were
concerned with users’ evaluation of the account holder’s personal traits
(e.g., if the account holder had a trait that the participant admired) or some
trend in the content they released (e.g., alignment with the participants’
topical interests). We note that recognizing an account from network effects
is the only characteristic that garnered mixed valuations, dependent on
whether the participant’s network portrayed the account in a positive light.
In our disaggregated, per-platform view of the same taxonomy (Supplementary
Materials), we observe that TikTok’s taxonomy is noticeably sparser than the
others, indicating there is less interest in account-related features, a
possible result of platform design. We dive into a deeper investigation of
this with our transcript data (see Section 4.2.2).

Our aggregate content-based taxonomy (Fig. 5) shows that participants’
evaluation of content is distributed over a much greater range of features
when compared to our account-based taxonomy. Unsurprisingly, the most
prominent characteristic associated with a negative evaluation is “related to
ads and consumerism.” Topic was a prominent feature contributing to positive
evaluation, especially when the topic aligned with users’ existing personal
interests. However, topic differs from many of the other features as it is
often interpreted by the user rather than explicitly defined in a post.
Participants were also generally more drawn towards personal interests rather
than professional ones in their feeds (except for Mastodon), although both
were considered across all platforms. “High consumption effort” is a
noteworthy characteristic due to the mixture of positive and negative
evaluations in the same column. While some participants enjoyed shorter
content due to their ease of consumption, P4[TT] saw longer-form content as
more likely to contain material of interest. In our disaggregated taxonomies,
we observe that TikTok was the sole contributor of the characteristic “belongs
to a notable trend/genre.” This leads us to believe that participants are more
attuned to the trendiness of content on TikTok than on other platforms.

Ultimately, our taxonomies show that participants’ evaluations of posts from
their feed were diverse and multi-faceted. Such preferences are unlikely to be
captured in their full richness with a simple “like” or even a “react” on
today’s platforms, nor a one-size-fits-all approach to feed curation. We thus
see potential in leveraging techniques from IMT to design new affordances that
allow users to teach more nuanced preferences to the algorithm for more
agential and expressive curation.

###  4.2. Interview Findings

In addition to writing signals, we asked participants to think aloud
throughout the activity and reflect on their broader experiences with social
media feeds in a post-activity interview. Here, we discuss salient themes that
emerged from our transcript data.

####  4.2.1. Participants engaged in both undirected and directed use of their
feeds

Many participants described using their feeds to consume information in an
undirected manner. We characterize undirected consumption here as consumption
without a specific goal in mind. Common scenarios in which participants
engaged in undirected consumption include: (1) browsing the feed to be
entertained (2) casually catching up on interesting conversations, and (3)
instinctively opening the feed out of habit. This entertainment-induced
passive consumption, which was especially common among our TikTok
participants, can often lead participants to perceive content as more
disposable. For example, P11[TT] noted that “if [a post] doesn’t raise a
question or doesn’t seem like something I’m interested in, then I’ll just
scroll away because there’s an infinite number of videos I can watch.” Besides
entertainment, participants also engaged in undirected consumption when
casually browsing for interesting updates and catching up with relevant
conversations. P2[TW] likened this to “hallway chats […] or water cooler
conversations.” Finally, some admitted that their undirected browsing
tendencies were simply driven by habit and instinct. P7[IG] shared that “if
I’m waiting for something, my hand just automatically goes [to my phone].”
P23[TT] felt similarly for TikTok: “I feel like it’s more so a habit of […]
opening the app, and just hoping I find something interesting.”

On the other hand, participants also mentioned many instances of using their
feeds in a directed manner. We characterize directed consumption as
information seeking with a specific goal. Active consumption was more
frequently linked to the activity of searching rather than browsing. P4[TT]
discussed their use of TikTok as “kind of like a Google for me. I want to
learn about, for example, these new car features, or ‘how do I bake a cake?’
And what are some good restaurants in [my area]?” P24[MA] even kept a
dedicated search column in their multi-column Mastodon feed to search for
particular hashtags.

Given these characterizations, participants did not limit themselves to solely
undirected or directed consumption. They would often use their feeds for both,
switching between the two based on their goals and context. P15[TW] summarized
this aptly:

> “If I am in a low focus parsing mode, then I will read everything in the
> timeline. If I want to focus and get things that are a little bit more
> important […] or I’m trying to answer a question, I’ll say, ‘I care about
> this topic, or I care about [hearing from] these experts.’ Let me dig in and
> see what’s going on here.” (P15[TW])

P13[TT] mentioned that they will engage in undirected browsing “if I have time
to fill” but otherwise will actively seek or follow up on information, such as
restaurant recommendations. P6[TW] used Twitter “very intentionally for
professional [networking]” but is otherwise just “scrolling and looking
around.”

Our observation of participants’ consumption behaviors not only highlights the
need for feeds to accommodate diverse modes of browsing, but also enable users
to fluidly move between them.

####  4.2.2. Tension existed between desires for people- and content-centric
feed experiences

In recent years, social media platforms have shifted towards content-based
recommendations in an attempt to more effectively drive user engagement,
particularly for younger demographics (Heath, 2021). While it is not clear
from our taxonomies (Figures 4 and 5) alone how participants prioritized
account-based or content-based signals, our interviews shed more light on
this. Many expressed a desire for more people-centric experiences in their
feeds—curating and viewing content based on individuals they care about rather
than the contents of the post itself. To P17[MA], the post author can serve as
a reliable indicator of relevance: “There are some people that I know 95% of
the things they say are relevant to what I’m doing.” Indeed, when asked about
the features they prioritized for post evaluation, all but 7 participants
indicated an explicit preference for account-based features over content-based
ones.

This people-centric mental model thus came into tension with platforms’
increasingly content-centric recommendations. The most striking example of
this tension was Instagram. Of the 6 Instagram users in our study, 4 saw no
content from close friends or acquaintances in their first 10 posts of their
home feed. P7[IG] felt that their feed hindered their ability to stay updated
with those they cared about: “None of these 10 posts were posts about people I
know […] And I’d like to know what’s going on in their lives.” P12[IG] echoed
this and the desire for prioritizing posts from specific people: “I would love
if I could see what my friends post at the top.” P8[IG] leveraged the ‘Close
Friends’ feature (Instagram Help Center, 2023) to combat the algorithm’s
content-based recommendations so that they could still “be connected to [my
friends].”

Interestingly, 5 of the 7 users who did not express a preference for account-
related features used TikTok for this study. For TikTok users such as P10[TT],
their home feed (For You Page) is mostly filled with content from “people I
don’t follow, or I’ve never heard of.” Unlike P17[MA], who trusted certain
authors to post relevant content, P23[TT] was the opposite: “even if it wasn’t
[this creator], I would stay tuned to the specific topic she’s talking about.”
A couple reasons (or combinations thereof) may prompt deeper engagement with
content on TikTok. First, the primary use of TikTok was for entertainment (see
Section 4.2.1). As is the case with YouTube, TikTok users’ focus on
entertainment may lead them to be more concerned with content consumption
rather than fostering social interactions with people (Khan, 2017). Second,
the design of the TikTok interface may serve to prioritize content, more so
than other platforms. As prior design analyses of social media UIs (Kender and
Frauenberger, 2022) point out, TikTok offers an immersive content-viewing
interface with minimal whitespace—unlike other platforms, all other features
within a post are overlaid on top of a TikTok video.

Our takeaway here is that both people- and content-centric feed experiences
can be desirable. On some platforms, users prefer content-centric experiences;
on others, they prefer people-centric ones. The tension between the two comes
into play when, on a particular platform, users desire one but their feed
forces them towards the other. Thus, when formulating designs for teachable
feed experiences, we aim to equip users with necessary tools to agentially
express their desired type of experience, and tweak that experience as they
see fit.

####  4.2.3. Perceived value of saved content was often not actualized

Many participants used archival features (e.g., “save”, “bookmark”) on their
platforms for a variety of reasons. For some, the times when they checked
social media were not when they wanted to take action on a piece of content.
For example, P2[TW] shared that they “usually check social media in the
mornings and then I have to work,” so they saved posts related to science
fiction (a topic of their interest) to read after work. P7[IG] saved
encouraging quotes for “when I’m sad, or when I really need a pep talk.” Over
several months, P7[IG] also saved career-related posts in preparation for the
upcoming recruiting season.

It is important to note the distinction between simply allowing a user to save
content and allowing them to curate their content archive such that they can
easily retrieve desired content. While all platforms in our study offered the
ability to save content, there is much less support for managing saved content
so participants could actualize their value. To circumvent the issue of not
being able to retrieve saved content, P8[IG] said they would sometimes send
content to another person instead of saving it on the platform so that they
could rely on that person’s memory to help retrieve the content later on: “I
would send to my husband a lot of the things I like […] That’s also a strategy
for ‘watch later.”’. It also does not help that saved content often has low
discoverability in platforms’ interfaces. As of the time of writing, saved
content in Instagram is 4 taps away from the home feed and requires traversing
a user’s profile menu, an area reserved for options such as privacy settings
and payment information.

In many ways, an archive of saved content resembles a feed, but is free of
algorithms—all curation happens manually. P24[MA] considers the lack of
algorithms to be a double-edged sword: “inside of [archives], I don’t get the
good of the algorithm, but at least I wasn’t getting the bad stuff either.”
While a sophisticated curation algorithm may be unnecessary, we recognize that
an organized content archive can act as a collection of examples
representative of user preferences, acting precisely like a curriculum in IMT.
We further explore this idea in Section 6.2.

####  4.2.4. Participants’ evaluations of posts were nuanced

Many current social media platforms have explicit and implicit means by which
users can provide feedback to content algorithms (Stray et al., 2022;
Narayanan, 2023). However, this feedback can fail to capture users’
consideration for complex factors that influence their overall assessment.
Participants often gave nuanced evaluations of posts which took into account
multiple feature-characteristic pairs contributing to both positive and
negative evaluations of a post, or factors outside the context of the post.

More complex evaluations were frequently due to conflicting assessments of
different aspects of the content, such as a post that is about an undesirable
topic but provides value to the participant in another way. For example,
P10[TT] said that they are not interested in the Grammy Awards, but still
wanted to keep related posts in their feed “just to stay updated on it.”
Similarly, P16[MA] described how an undesirable post could still enrich their
social media experience: “I wish to some degree that it wasn’t posted but at
the same time, it does have value to me. It gives me information about the
person and it makes it feel like a real conversation.”

Sometimes, users may give mixed assessments of the post content and the post
author. A negative assessment of the content of a post may outweigh a positive
assessment of the author (or vice versa) in evaluating the desirability of a
post in their feed. For example, in response to an offensive Instagram post,
P22[IG] noted that “there’s no way for me to tell Instagram, ‘don’t show me
pictures like this anymore,’ ” although they still wanted to see posts from
the author and “don’t really want to mute the person.” A negative evaluation
of the content of a post may even impact a positive/neutral evaluation of the
author, or vice versa. P1[MA] explained how the distasteful content of a toot
on Mastodon led them to change their evaluation of the author: I might even
unfollow this person for tooting this thing out […] [the post was] in some
ways useful in that, like ‘wow, I can’t believe the person is buying into this
garbage, I don’t know if I can trust other stuff that they put in my feed.’
This suggests that our two taxonomies, which capture both account- and
content-based evaluations of posts, respectively, are not strictly independent
and should be considered in concert.

In other cases, however, a participant’s evaluation of a post was influenced
by context not local to the post itself. For example, mood can affect a
participant’s social media preferences. P8[IG] preferred “short and sweet”
posts when stressed, to avoid spending too much time on their phone, and
P12[IG] only wanted to see “corny Instagram captions” when they were in a good
mood. Such temporary preference shifts might extend beyond a user’s mood on a
given day and instead apply across a larger span of time. P17[MA] noted that
while they typically did not mind seeing professional content on their
Mastodon feed, they had “been kind of overwhelmed with mid and senior career
people recently.” Echoing this need, P19[MA] suggested a potential feed
customization feature where users “could just mute a category for a day, or
for some period of time.”

Users’ evaluations of posts involve multi-dimensional and often conflicting
assessments of features within a post, as well as outside factors, including
mood. We see a need for teachable experiences that accept both structured
(e.g., via UI buttons) and unstructured feedback (e.g., via natural language)
over longer timescales. This way, users have more flexibility to indicate
temporary or context-specific preferences for feed curation.

####  4.2.5. Participants experienced a lack of self-causality in feed
curation

Many participants did not trust algorithms’ ability to curate feeds per their
preferences. While describing why they were not interested in a post from an
author they usually appreciate, P2[TW] said that they “don’t trust the
algorithm to know” that distinction. P9[IG] explicitly said that they “don’t
want algorithms to try to sort the best content for me” and would rather do it
themselves.

P9[IG]’s statement demonstrated a desire for self-causality, a key aspect of
agency defined by the ability to make personal choices and see these decisions
reflected in an outcome in line with their values and goals (Bennett et al.,
2023). Participants even prioritized self-causality above content enjoyment:
P8[IG] said that while they sometimes enjoyed the algorithm’s suggested posts,
they “would put it towards the end [of the feed], because it’s not something
I’ve decided I want to see.” This desire for greater control over feed content
also led participants to take actions to experience self-causality, for
example, by forming rudimentary feed training behaviors. P10[TT] shared that
they “will like videos that I liked and dislike on the ones I don’t want to
see anymore”, because they are “pretty sure the algorithm will learn what you
like”.

Participants’ efforts to experience self-causality were hampered by an
inability to determine why the algorithm fed them specific content.
Participants often evaluated this based on different dimensions of relevance
of the content in question, as represented in our content-based taxonomy. When
describing how platforms know what kinds of feeds users are interested in,
P23[TT] said that they “think [the platforms] kind of guess”, as they had a
STEM-related feed that they said was “cool, but I don’t know why [the
platform] would pick that feed specifically for me.” P10[TT] described how
they were “somehow on autism TikTok, even though I’m not diagnosed with it or
anything,” which “feels weird” as they “don’t know how I got there.” This
uncertainty made it difficult for P10[TT] to use like/dislike signals to
curate their feed, as they described the process as “not foolproof… I will
still get fully random [posts].” Although participants could see that the
algorithm was not accurately learning their preferences, they did not know why
it was learning in this way or how to teach it more effectively, limiting
their self-causality.

While participants expressed some negative sentiment towards algorithmic feed
curation, we see that this sentiment may not be caused by the presence of
algorithms themselves, but rather the lack of self-causality experienced in
current platforms. IMT was motivated by similar concerns, and has the
potential to increase algorithmic teachability and user agency if applied to
feed settings.

We now proceed by synthesizing our study’s findings through the lens of prior
literature on IMT to inform design principles for teachable feed experiences.

##  5\. Design Principles for Teachable Feed Experiences

A primary objective of IMT is to leverage an end user’s subject-matter
expertise to train a learnable agent that can effectively aid the user in
fulfilling their desired goals (Ramos et al., 2020; Ng et al., 2020; Zhou and
Yatani, 2022). However, teaching languages101010Recall that a teaching
language is an interface by which the human teacher can expressively
communicate desired concepts for an algorithmic agent to learn. proposed in
prior work, such as PICL (Ramos et al., 2020) and Pearl (Jörke et al., 2023),
may not translate smoothly to social media settings—they demand high-effort,
meticulous interactions from users to provide meaningful labels and
descriptions from data examples. Given that social media is designed to
support fast-paced consumption of information, actively introducing excessive
friction via conventional IMT interfaces may worsen the user experience rather
than improve it.

Based on our taxonomy and participant interviews, along with prior literature
in IMT, we outline five design principles to set the stage for teachable
social media feed experiences. We do so to extend the core principles of IMT
to the modern social media landscape. In some cases, this extension directly
builds off of classic IMT principles; in others, those principles are re-
examined and reconfigured. Our principles may be used independently or
(ideally) in combination.

D1: Situate the teaching language within the feed. In IMT, teaching has
conventionally been performed in isolation from the environment in which the
teaching materials originated. For example, when creating a recipe classifier
in PICL (Ramos et al., 2020), the user first imports a set of documents
containing recipes before they can start teaching. Note that PICL is a
separate environment from where users may encounter recipes in-the-wild, such
as on websites. A separate teaching environment can enable more feature-rich
and expressive teaching languages, but it can also disengage users from the
act of teaching in social media settings. For one, the additional effort
required to move posts and organize them outside of the feed is already seen
as burdensome by our participants (see Section 4.2.3). Additionally, it may
not be in platforms’ best competitive interest to support exporting a post and
any informative metadata off the platform. We therefore situate the teaching
language within the feed itself. This way, we can directly leverage platforms’
existing representations and users’ existing mental models, while lowering the
effort required to perform teaching. A key question then arises: what exactly
do users’ mental models of current platform representations look like in the
context of feed curation? Our taxonomies shed valuable light on this; we can
leverage salient features identified in our taxonomies to inform the design of
our in-feed teaching language.

D2: Be available, but not intrusive. We heard from many participants that
social media can act as a much-needed break or distraction in the middle of
the day, a means of relaxation, and a casual time-killer when small pockets of
idle time arise. That is, participants were satisfied simply by letting the
algorithm entertain them. In these scenarios (described by P15[TW] as “low
focus parsing mode”), persistently demanding extra attention and effort via
IMT may in fact worsen the user experience and force users to expend more
energy than they desire. We saw in practice that many participants switched
between this low-focus mode and a more attentive, information-seeking mode
(see Section 4.2.1), and that this switching was largely dependent on
difficult-to-predict factors such as mood and context. In light of this, we
ensure that our teaching language is available, but unobtrusive. That is, the
interface is easily accessible to users across the entire feed experience, but
can also be easily dismissed or hidden when not needed.

D3: Embrace a multiplicity of feeds. Some of our participants questioned why
so many feed experiences had to be constrained to a singular feed and
expressed a desire for multiple feeds to better organize their content.
P12[IG] touched on the oddness of having diverse content mix in their feed:
“it’s weird to go past someone’s bikini photo and then, ‘5 people killed at a
refugee camp.”’ Indeed, the range of characteristics used to describe features
of posts as captured in our taxonomy corroborates this diversity. As a less
extreme example, it may be jarring for users to see content that “contains an
existing professional interest” if they expect to scroll through posts whose
content “is funny.” P16[MA] mentioned that they would like to create different
feeds for the various Mastodon instances they were on. P8[IG] likened their
feed to a newspaper and suggested different sections: “news, updates, events,
pop culture, etc.” In fact, platforms have also started to explore multi-feed
experiences and broadening of algorithmic choice. Many, including Twitter, now
offer an engagement-based “For You” feed alongside a “Following” feed
featuring content from followed accounts. One smaller social platform called
Bluesky has introduced Custom Feeds, where developers can create feed
algorithms to which users can browse and subscribe (Bluesky, 2023). TikTok has
also introduced topic feeds based on inferred user interests (Malik, 2023). We
consider this shift towards feed multiplicity a promising direction,
especially when providing users with a means of organizing their IMT
curriculum.

D4: Seek structured and unstructured feedback. IMT workflows have
traditionally been scaffolded with affordances that elicit structured feedback
(e.g., data highlighting and labelling). Our findings in Section 4.2.4 and the
range of both positive and negative feature-characteristic pairs in the
taxonomy suggest the need for teachable algorithms that accept both structured
and unstructured feedback in order to capture users’ nuanced evaluation of
social media posts, which corroborates existing research in IMT. For example,
Jörke et al. (Jörke et al., 2023) provided a form-like interface for
specifying key teaching concepts in which some fields allow the selection of
existing data tags while others allow the user to write freeform natural
language. Likewise, Zhou et al. (Zhou and Yatani, 2022) noted the importance
of relying on users’ unstructured object show-and-tell gestures in addition to
a structured UI. This combination of structured and unstructured feedback is
desirable in real-world applications as users may make nuanced judgements that
cannot be captured with structured feedback alone, but also require some
guidance to express concepts in a format parsable by an algorithmic learner.
Parsing unstructured input, however, is now significantly less challenging due
to technological advancements in large language models. We take this into
consideration when balancing structured and unstructured feedback.

D5: Enable teaching and evaluation at varying timescales. Bennett et al.
(Bennett et al., 2023) identified timescales of interaction—ranging from
micro-interactions (a few seconds or less) to episodes (seconds to hours) to
life (days to years)—to be a key aspect of human agency and autonomy. When
asked to articulate their preferences with limited access to their feeds
during the study, many participants had difficulty doing so because their
preferences evolved over time based on the content they saw. As such, they
expressed a desire to agentially refine their feeds’ behavior over an extended
time period. This also happened to be less cognitively demanding, as P6[TW]
pointed out: “I don’t have to intentionally be like, okay, I’m gonna sit down
and coordinate everything.” Our participants also described how their
preferences shifted temporarily based on factors like mood (see Section
4.2.4). Indeed, we can see the pitfalls of assuming static preferences and
designing only for interactions at short timescales via the inefficacy of set-
and-forget personal content moderation tools (Jhaver et al., 2023). That said,
classic debates in HCI over direct manipulation interfaces (UIs providing
immediate control feedback through elements such as buttons and sliders)
versus interface agents (systems that perform actions on behalf of users,
often after learning user preferences over time) reveal that aspects of both
are vital in information-dense environments (Shneiderman and Maes, 1997).We
thus explore designs that enable teaching and evaluation at varying
timescales. Specifically, we aim to leverage teaching interactions afforded
through direct manipulation as a familiar interaction pattern to ground
evaluations of algorithm performance over longer timescales. In doing so, we
situate the teaching language as not only a tool for the teacher to articulate
key concepts, but also one that aids evaluation of the algorithmic learner.

##  6\. Proposed Feed Designs

We now propose three feed designs for teachable social media feed experiences
that embody our findings and design principles. Our goal is not to constrain
the design space of teachable feeds with these designs, but rather present
them as sensitizing concepts—emergent ideas that help direct attention to
promising topics or phenomena (Blumer, 2017; Zimmerman et al., 2010)—to
illuminate salient paths for future research.

Note that the mockups illustrating our feed designs feature a generic
microblogging platform, similar to Twitter or Mastodon. Our reasons for making
this decision were twofold. First, we wanted to show how our designs can
operate on platforms with a diverse range of feature and characteristic
preferences, as opposed to ones like TikTok where a particular media modality
(and therefore certain sets of features and characteristics) is significantly
prioritized over others. Second, we tap into the increasing design and
development interest in microblogging alternatives since Twitter’s change in
ownership (Silberling, 2022; Jolly, 2022). Many platforms in this space,
including Mastodon and Bluesky, are also open source, making experimentation
with novel ideas more accessible than on closed platforms.

###  6.1. Exploded UI Views

In 3D diagramming, an exploded view is one where the individual components of
an object are shown slightly separated from each other as if a small explosion
occurred at the center of the object. Exploded diagrams depict inter-component
relationships and are thus commonly found in instructional manuals. We employ
a similar concept in a post’s in-feed UI to serve as a teaching language for
the elicitation of more granular preferences. On current platforms, “liking” a
post can signal that the user enjoys some feature(s) in the post, but it does
not clearly communicate what specific feature(s) they found like-worthy. An
exploded UI view aims to recover this information by allowing the user to
specify features within the post that they find (un)appealing.

Once a user expresses a generic signal (e.g., like, reshare) on a post, the UI
explodes out into individual features, such as the author, text descriptions,
attached media, and hashtags (all features in our taxonomies—see Section 4.1).
These features may be directly available in the post or algorithmically
extracted—indeed, topic was a popular feature in our content-based taxonomy
that needed to be inferred from posts (Fig. 5). In Fig. 6, we distinguish
inferred features from extracted ones by rounding out their UI. The user can
then engage in teaching the feed algorithm their preferences in this exploded
view by selecting the features that they consider (ir)relevant. While we could
further elicit detailed characteristics associated with those features, just
like we did in our study’s Miro board, we reduced this down to simple plus and
minus buttons (indicating a positive or negative characteristic, respectively)
to avoid overburdening the user.

Figure 6. An example of the exploded view feed design with 5 features: author,
text content, images, hashtags, and topics. (A): the exploded view is
triggered as the user provides a generic signal—in this case, a “like.” (B):
the exploded UI can be collapsed back into the normal UI using the caret.

Two mockups of feed UI screens side by side. The one on the left shows a post
in a feed. The one on the right shows the same post but its UI is separated
into smaller UIs, each containing a separate part of the original post. These
parts include the post author, the post’s text, and the hashtags included in
the post.

We ensure that “explosion” happens within the feed so that teaching can be
done without leaving the feed, satisfying D1. Additionally, since the exploded
view can occupy more space than the normal post UI, especially when there are
numerous features available to teach with, we provide a convenient option for
the user to collapse back the exploded view (see Fig. 6 (B)). The user can
also simply scroll away. This simple dismissal of the exploded UI aligns with
D2. Finally, even as direct manipulation interfaces affording immediate
interaction, exploded UIs allow users to gradually accumulate preferences
informed by what they view, satisfying D5.

###  6.2. Multi-Feed Curriculum Organization and Seeding

A teaching language alone cannot close the teaching loop. Here, we propose a
design in which assembling the teaching curriculum and evaluating the
learner’s performance is closely integrated with the teaching language via
feed multiplicity. Key to this design is the use of curriculum organization to
curate a multi-feed experience.

As a user expresses preferences using a teaching language, those preferences
are saved into curriculum “folders.” Due to the differing nature of account-
and content-based preferences, we separate the two in Fig. 7. A folder can
then spawn a new feed that aims to provide more focused content that adheres
to the folder’s theme. The post on which the user first expressed preferences
becomes a “seed” for the new feed to guide the recommendation of related
content. This multi-feed experience serves as a way for users to evaluate the
algorithmic learner’s performance—a relevant and well-curated feed is a sign
that the learner is effectively acting on taught preferences. Otherwise, the
user can provide feedback to the learner through the in-feed teaching
language, closing the teaching loop.

If a user does not want a folder to form a feed, they can toggle it off in the
curriculum; folders created from negative feedback are toggled off by default.
Furthermore, as participants pointed out in Section 4.2.3, lightweight
algorithmic interventions, such as suggesting folders for unorganized areas of
the curriculum (see the “Unsorted” folder’s Sort For Me option in Fig. 7) can
also aid with curriculum organization.

Figure 7. The teaching curriculum, depicted on the interface as “Preferences,”
as a multi-feed experience. (A): the positive account-based feedback provided
by the user in the exploded UI view prompts further organization from the user
before the account is deposited into the curriculum. (B): tab to easily switch
between viewing the feed and the curriculum. (C): the user’s categorization of
the account as “Friend” creates a new entry in the corresponding curriculum
folder. Folder titles have arrows indicating whether the folder was created
from positive or negative feedback. (D): feeds are formed from folders that
users have toggled on. The posts on which users first provided feedback act as
seeds for the folder feed.

Three mockups of feed UI screens side by side. The leftmost screen shows an
exploded UI concept. The first screen leads to the middle screen, which shows
4 folders of organized content on a tab called Accounts. The other tab is
called Content. The rightmost screen shows how each folder becomes a tab that
contains a feed of its own. This is in addition to a Home feed that exists by
default.

Given that user trust hinges on observed learner performance (Wall et al.,
2019; Mosqueira-Rey et al., 2023), additional support may be added to further
facilitate learner evaluation. One possible approach is to reuse already-
familiar interaction patterns in the teaching language scaffold evaluation.
Fig. 8 offers an “Explain” option for posts recommended by the learner in a
feed formed from a folder. Selecting that option would expand the post into an
exploded UI with pre-selected preferences that the learner infers from
existing ones. The titles of features then become explanations that link back
to folders and other curriculum material used by the learner to infer that
preference. With limited work in explanation and evaluation techniques in IMT
(Teso et al., 2023), employing the teaching language as an aid for evaluation
suggests one approach to mend this gap.

Figure 8. The teaching language (exploded UIs in this example) is invoked to
scaffold learner evaluation once the “Explain” option is selected. (A):
explanatory feature titles indicate relation to users’ previously expressed
preferences and links to an existing curriculum folder. (B): the learner pre-
selects option(s) in the UI to show its preference prediction, which the user
can correct by un-selecting if desired.

Two mockups of feed UI screens side by side. The left screen shows a post that
when selected, shows two options: Report and Explain. Selecting the Explain
option will lead to the second screen, where the author and text within the
post are in separate containers and indicate how they relate to previously
expressed user preferences. These indications are pre-selection of the +
button within the container and explanatory text in the container title that
links to a curriculum folder.

This design’s use of feed multiplicity embodies D3. Additionally, unlike the
set-and-forget approach to creating custom feeds on Bluesky (Bluesky, 2023),
this design enables users to iteratively build, refine, and evaluate their
feeds in the spirit of D5. This design can also be easily dismissed (D2):
users can toggle feeds off from within the curriculum and the curriculum
itself is located in another tab separate from regular feed activities.
However, if the user does choose to engage with this design, the proximity to
and reliance on an in-feed teaching language satisfies D1.

###  6.3. Purposefully Finite Feeds with Natural Language Feedback

The infinite scroll is a dominant design pattern in contemporary social media.
Implementation-wise, this effect is achieved by loading posts in batches such
that another batch of content is quickly available once the user reaches the
end of the previous batch. What if we can repurpose this transition between
batches into an opportunity for preference elicitation and reflection?

In this feed design, we explore what it means for feeds to be purposefully
finite. We split a feed into individual “stacks” of content; users can set the
size (number of posts) of each stack. When the user reaches the end of a
stack, they are presented with a teaching language in the form of a text input
area in which they can specify, in natural language, any preferences to
incorporate into future stacks. The combination of finite stacks and natural
language feedback addresses two insights participants raised in our study.
First, preferences may not be well-formed before consuming content—by allowing
users to first view content and then reflect on what they viewed, users may be
able to articulate their preferences with more precision. Second, the feed was
used to both browse and search for content. An adjustable stack size allows a
user to smoothly transition between the two consumption modes. With a low
stack size and frequent feedback input, the feedback starts to resemble
intent-driven search queries, while a high stack size brings the experience
closer to that of a conventional infinitely scrolling feed. On top of all
this, unstructured natural language allows users to specify nuances that may
be difficult to communicate through structured means, such as UI buttons.

The accumulation of natural language feedback also presents novel interaction
opportunities. Users may use consecutive pieces of feedback to assemble a
chain of preferences that can help them discover content with specific
features and characteristics. Users can then exit from these more focused
views by deleting preferences from their chain, or removing the entire chain
to start afresh. Users’ preferences can also be automatically summarized by
the algorithmic learner into “Observations” (see Fig. 9) are shown to the user
for additional reflection. These observations can be edited by the user to
guide future recommendations, similar to editable natural language user
profiles in modern conversational recommender systems (Friedman et al., 2023).

Figure 9. A stack of feed content followed by an opportunity for natural
language feedback. (A): the user can set the size of the stack, or select the
infinity option for an infinitely scrolling feed. (B): text box for
unstructured natural language preference expression. (C): option to view the
history of submitted preferences. (D): submitted preferences are cumulative
and appear in a timeline. Users can remove individual preferences from the
timeline or clear all of them using the option at the bottom of the page. (E):
to aid user reflection, a synthesis of their preferences is generated and can
be edited to guide future recommendations.

Two mockups of feed UI screens side by side. The left screen contains a slider
at the top labelled Stack Size that is set to 8 but can be set from 1 to 30,
with an additional button for infinity. The bottom of the screen is a text
input bar containing placeholder text that reads “Type your preferences” with
a checkmark button and a history button that leads into the second screen on
the right. The second screen contains a vertical timeline showing 3 text
inputs a user has provided within a period of 30 minutes and an Observation
panel under the timeline that synthesizes the 3 inputs into a higher-level
observation. At the very bottom of the screen, there is a Clear All button.

This natural language feedback, when used in combination with the more
structured feedback presented in Section 6.1, results in an expressive set of
teaching languages (D4). Like exploded UI views, it also operates in-feed, per
D1. If the user does not wish to engage in this design, they may simply
proceed to the next stack without providing feedback, or revert to an
infinitely scrolling feed by setting the stack size to infinity, satisfying
D2. Finally, by first accumulating feedback over time as they view content,
users can refine system behavior over longer timescales, while still being
afforded the ability to directly and immediately update the system’s learned
knowledge as preferences evolve (D5).

##  7\. Discussion

###  7.1. Ethical Implications

Our goal of enabling teachable social media feed experiences is to empower
users in reclaiming agency and enriching self-expression. While we hope that
such a goal would bring positive change to social media systems, certain
factors, if not carefully considered, may lead to the opposite outcome.

####  7.1.1. Issues around (hyper)personalization

Granular preferences captured through teachable feeds can lead to hyper-
personalization—the use of deeply personal traits such as one’s personality,
motivations, and goals (Warshaw et al., 2015; Zhou et al., 2019) to
personalize experiences. Hyper-personalization may exacerbate privacy concerns
many already have about social media platforms. In particular, users’ lay
knowledge of behavioral profiling for targeted advertisements has led to
negative perceptions and distrust of platforms to responsibly store personal
information (Barbosa et al., 2021; Ur et al., 2012). Prior work has also
warned about social pressures that can lead to over-sharing of hyper-
personalized information on social media (Warshaw et al., 2015).

More broadly, John Cheney-Lippold coined the term “dividuals” (Cheney-Lippold,
2017) to highlight an inherent paradox of personalization on large-scale,
data-driven platforms: one’s “personalized” experiences online are never truly
local to an individual, but are instead constantly shifting collections of
data points—dividuals—aggregated across many users. That is, one is never an
“individual” on these platforms, but is instead an algorithmically assembled
profile of dividuals. Simpson et al. (Simpson et al., 2022) claim that this
shifting assemblage results in an “identity spiral” in which users’ online
identities are always being revised and connected to other identities in
uncontrolled and sometimes disagreeable ways. Enhanced personalization,
including via teachable feeds, may perpetuate this spiral and dividualization
as a whole.

####  7.1.2. Potential solutions

At the same time, teachable feeds have potential to alleviate some of these
concerns. Many concerns around user privacy and targeting stem from the
opacity with which algorithms operate—users are unaware of what kind of data
is collected about them and where the data is transferred to after collection.
By eliciting explicitly taught preferences, users can more clearly grasp what
the algorithm has learned about them. From a platform’s perspective, the
improved granularity of user feedback may reduce the need for implicitly
inferring certain preferences. Taken together, the reclamation of user agency
may in fact aid platforms in reaching transparency ideals. Furthermore, by
accumulating their own set of taught preferences, users can be their own
architects of their individualized online identity rather than relying on an
algorithm to assemble it from dividuals. While some forms of automated
profiling may still persist, the nuanced experiences of the individuated user
can now remain uniquely local, not simply dissolved into the dividual crowd.

We also hope that our proposed design patterns can pave the path for new forms
of content moderation, such as detecting curriculum folders containing high
concentrations of malicious content and natural language feedback that
indicates intent to disseminate such content. This will hopefully
disincentivize adversarial actors from using teachable feeds to spread harm
(e.g., collaboratively plot disinformation campaigns (Starbird et al., 2019)).
Similar methods can also be used to identify when users are exposed to
increasingly homogeneous perspectives111111We use “increasingly homogeneous
perspectives” in this work but recognize the rich, yet inconclusive, body of
empirical work investigating similar phenomena termed “echo chambers” and
“filter bubbles” (Auxier and Vitak, 2019; Quattrociocchi et al., 2016; Celis
et al., 2019; Fletcher et al., 2021; Dubois and Blank, 2018; Pariser, 2011).
from their content, and when to introduce nudges to increase content
diversity. Users can also use these moderation mechanisms to better reflect on
their own content browsing habits, treating their role as a teacher as a
learning experience. Prior work in IMT showed that reflecting upon workplace
calendar data throughout the teaching process surfaced new insights from
personal data and informed future digital wellbeing goals (Jörke et al.,
2023). We see room to further explore this in a social media context.

###  7.2. Limitations and Future Work

As with any sensitizing concept, our design patterns are meant to be iterated
upon and refined. We showed that these patterns can embody the design
principles derived from our study data, but we did not perform empirical user
evaluations of our designs. Future work may integrate these designs into
existing or new social media platforms and run user studies to gather
feedback. Our design patterns were also applied to a microblogging-style
platform similar to Mastodon or Twitter. Future work may explore translating
them into a TikTok-style platform, as 1) the medium of video may cast
additional considerations onto our proposed patterns, and 2) the user can only
see one post at any given time, unlike the other feeds. Social media aside,
other feed-based systems, such as news applications, may also benefit from our
design patterns. Further research is needed to determine if or how they should
be applied in domains outside of social media.

Teachable feeds also present exciting opportunities for shared, collaborative
IMT experiences. For example, a user who has a curriculum folder for
celebrities they are interested in seeing more of in their feed may share that
folder with a friend with similar celebrity interests. Users living in the
same town or city may share curricula with local events or news. More broadly,
users who share their established curricula assembled over time can help those
with less experience as a teacher in IMT jumpstart the IMT process.
Collaborative IMT can thus be of particular interest to future work aiming to
expand existing strategies to support novice teachers, strategies that are
currently limited to showing push notifications of expert teachers’ best
practices using Wizard-of-Oz methods (Wall et al., 2019).

Additionally, the platforms used in our study were more different than they
were similar—users opened their feeds for different purposes, followed
different accounts, and were exposed to different feed affordances. While this
enabled us to create a broader mapping of our design space and witness shifts
in taxonomies across the platforms, it did not allow us to isolate any
particular variables. The launch of Threads (Meta, 2023) in July 2023 presents
a rare opportunity to address this. Because Instagram and Threads accounts are
automatically linked, one may follow the same accounts on both platforms but
find themselves in two completely distinct user experiences. Future work can
use this as a case study for how variations in feed affordances can impact
content perception and preferences, thus shaping the “culture” of a platform.

Finally, even though we proposed teachable feeds with users’ best interests in
mind, we acknowledge that social media platforms may not always act in those
interests en route to achieving their financial goals (Bruckman, 2022). We do,
however, see potential avenues for incentive alignment and adoption of our
proposed designs. Currently, the dominant financial model for platforms
revolves around maximizing ad revenue by engagement optimizing techniques to
prolong users’ ad exposure. We encourage future work to investigate how to
increase ad quality using feedback willingly provided by users in the teaching
loop to reduce reliance on ad quantity and ad exposure time. This way, ad
targeting can be made more transparent, as users have a record of what they
taught to their feed. Irrelevant ads, which clutter many of today’s platforms,
may also be reduced upon platforms receiving higher-quality user feedback.
Platforms may, and have already started to, explore new financial models. For
example, Mastodon has adopted a non-profit financial model, choosing “not [to]
implement any monetization strategies in the software” (Mastodon, 2023) and
instead funding ongoing development with grants and crowd-sourced funds
(Elysse Bell, 2023). This lack of commercial involvement means that Mastodon
can better align with user interests but constrains development resources to
build experiences that are polished and compelling enough for mass-market
adoption (Bruckman, 2022). Public funding may be crucial in the success of
these non-profit models. As such, policymakers may see more opportunities to
engage with this space to advocate for users while fostering financially
sustainable platforms.

##  8\. Conclusion

An architect may design a home, but it is ultimately up to the home’s
occupants to decorate the space to reflect their unique tastes and
preferences. Today, social media feeds act as digital architects of
personalized social spaces, but users often lack such decorative agency.

In this work, we explored the idea of teachable social media feed experiences
for agential, personalized feed curation. To do so, we conducted a study with
24 social media users across Instagram, Mastodon, TikTok, and Twitter to
elicit key signals they used to determine the value of posts in their feeds.
We found that users evaluated content in multi-faceted and nuanced ways that
cannot be fully captured by affordances on current platforms. To enable users
to better “teach” an algorithm their preferences, we offered five IMT-inspired
five design principles for teachable feed experiences, informed by findings
from our study. We then embodied these principles in three feed designs to
inspire future efforts on integrating teachable feeds into real-world social
media systems.

Altogether, our contributions lay the groundwork for continued exploration of
teachable feed experiences. We hope this exploration can empower users and
platforms to craft more comforting and expressive digital homes.

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