# Characterizing and Predicting Social Correction on Twitter

Yingchen Ma  Georgia Institute of TechnologyAtlantaGeorgiaUSA
<EMAIL_ADDRESS>,  Bing He  Georgia Institute of TechnologyAtlantaGeorgiaUSA
<EMAIL_ADDRESS>,  Nathan Subrahmanian  Brandeis
UniversityWalthamMassachusettsUSA<EMAIL_ADDRESS>and  Srijan Kumar
Georgia Institute of TechnologyAtlantaGeorgiaUSA<EMAIL_ADDRESS>
(2023)

###### Abstract.

Online misinformation has been a serious threat to public health and society.
Social media users are known to reply to misinformation posts with counter-
misinformation messages, which have been shown to be effective in curbing the
spread of misinformation. This is called social correction. However, the
characteristics of tweets that attract social correction versus those that do
not remain unknown. To close the gap, we focus on answering the following two
research questions: (1) “Given a tweet, will it be countered by other users?”,
and (2) “If yes, what will be the magnitude of countering it?”. This
exploration will help develop mechanisms to guide users’ misinformation
correction efforts and to measure disparity across users who get corrected. In
this work, we first create a novel dataset with 690,047 pairs of
misinformation tweets and counter-misinformation replies. Then, stratified
analysis of tweet linguistic and engagement features as well as tweet posters’
user attributes are conducted to illustrate the factors that are significant
in determining whether a tweet will get countered. Finally, predictive
classifiers are created to predict the likelihood of a misinformation tweet to
get countered and the degree to which that tweet will be countered. The code
and data is accessible on https://github.com/claws-lab/social-correction-
twitter.

Misinformation, Counter-misinformation, Social Correction, Twitter, COVID-19
vaccines

††journalyear: 2023††copyright: rightsretained††conference: 15th ACM Web
Science Conference 2023; April 30-May 1, 2023; Austin, TX, USA††booktitle:
15th ACM Web Science Conference 2023 (WebSci ’23), April 30-May 1, 2023,
Austin, TX, USA††doi: 10.1145/3578503.3583610††isbn:
979-8-4007-0089-7/23/04††ccs: Information systems Social networks

##  1\. Introduction

Figure 1. Examples of misinformation tweets and counter-misinformation
replies.

Online misinformation leads to societal harm including diminishing trust in
vaccines and health policies (Pierri et al., 2022; Ball and Maxmen, 2020),
damaging the well-being of users consuming misinformation (Kumar and Shah,
2018; Verma et al., 2022a), encouraging violence and harassment (Arif et al.,
2018; Starbird et al., 2014), and posing a danger to democratic processes and
elections (Shin and Thorson, 2017; Silverman, 2015, 2016). The problem has
been exacerbated during the COVID-19 pandemic (Micallef et al., 2020; Sharma
et al., 2022); particularly, COVID-19 vaccine misinformation including false
claims that the vaccine causes infertility, contains microchips, and even
changes one’s DNA and genes has fueled vaccine hesitancy and reduced vaccine
uptake (Sharma et al., 2022). Therefore, it is crucial to restrain the spread
of online misinformation (Micallef et al., 2020; Lewandowsky et al., 2012). In
this work, we use a broad definition of misinformation which contains rumors,
falsehoods, inaccuracies, decontextualized truths, or misleading leaps of
logic (Kumar and Shah, 2018; Wu et al., 2019).

To combat misinformation, various countermeasures have been developed
(Micallef et al., 2020; Miyazaki et al., 2022; Vo and Lee, 2019). Recent work
has shown that ordinary users of online platforms play a crucial role in
countering misinformation. According to the research study by Micallef et al.
(2020), the vast majority (96%) of online counter-misinformation responses are
made by ordinary users, with the remainder being made by professionals such as
fact-checkers and journalists. While fact-checking from these professionals
has been widely used due to its prominent and measurable impact (Micallef et
al., 2020; Vo and Lee, 2019), this process typically does not involve engaging
with the actors spreading misinformation. Instead, the ordinary users’
counter-misinformation efforts complement those from professional fact-
checkers by directly engaging in countering conversations through making
independent posts or direct replies to misinformation posts made by others (He
et al., 2023).

Countering of misinformation messages via direct replies from ordinary users
is called social correction (Bode and Vraga, 2018; Kligler-Vilenchik, 2022).
One real example is shown in Figure 1. Notably, social correction has been
shown to be effective in curbing the spread of misinformation (Colliander,
2019; Wijenayake et al., 2020), as well as doing so without causing increases
in misperception (Guess and Coppock, 2020; Swire-Thompson et al., 2020; Wood
and Porter, 2019). While certainly not a panacea for convincing people to
reconsider potentially misinformative beliefs, they are most effective at
reducing the misperceptions of those who may consume it (Bode and Vraga, 2018,
2021; Colliander, 2019; Seo et al., 2022).

However, little is known about the characteristics of misinformation tweets
that attract social correction. Developing this understanding has several
advantages: (1) first, it can help identify inequities in misinformation
correction. For example, comparison of correction across users or communities
(e.g., political ideologies) can reveal whether certain user types/communities
are less likely to be self-correcting, e.g., communities where users correct
misinformation when they see it. Identifying these disparities is the first
step towards addressing them by redirecting resources towards entities that
require external attention to curb misinformation; (2) second, if certain
misinformation content is less likely to be socially corrected, targeted
efforts can be directed toward countering them. Such instances can be
escalated and prioritized for interventions by professionals or social media
platforms; (3) third, if certain misinformation content is likely to be
socially corrected, then additional participants can be encouraged to provide
reinforcements.

Despite these promising benefits, characterizing and predicting social
correction is non-trivial due to several challenges. First, existing datasets
do not contain conversation-style narratives with paired misinformation posts
and counter-replies. Second, existing works (including Miyazaki et al. (2022))
do not analyze counter-replies to misinformation in a stratified manner where
tweets with different numbers of replies are considered separately. This fine-
grained analysis is necessary since comparing across or aggregating statistics
across tweets that have drastically different numbers of (counter-)replies can
skew the findings (Jeni et al., 2013; Higgins et al., 2008).

In this work, we seek to characterize and predict counter-replies to
misinformation. The contributions can be summarized as follows:

  * •

We curate a novel large-scale dataset that contains 1,523,849 misinformation
tweets and 690,047 counter-misinformation replies, along with a hand-annotated
dataset of misinformation tweets and counter-replies.

  * •

We perform a stratified, fine-grained analysis of the linguistic, engagement,
and poster-level characteristics of misinformation tweets that get countered
versus those that do not. Our analysis reveals several features of tweets that
attract social correction, such as anger and impoliteness.

  * •

We create two counter-reply prediction models to identify whether a
misinformation tweet will be countered or not, and if so, to what degree (i.e.
low or high), based on its linguistic, engagement, and poster features. We
achieve promising predictive performance with both of these models, with best
F-1 scores of 0.860 and 0.801, respectively.

The code and data is accessible on https://github.com/claws-lab/social-
correction-twitter.

##  2\. Related Works

###  2.1. Social Correction on Social Media Platforms

Misinformation widely spreads on social media platforms, which has caused
detrimental effects on society (Starbird et al., 2014; Arif et al., 2018;
Colliander, 2019), including harassment and personal attacks (Micallef et al.,
2020). To combat misinformation, users actively employ various strategies (Mu
et al., 2022), including replying to and commenting on misinformation (Vo and
Lee, 2019; Miyazaki et al., 2022; Kligler-Vilenchik, 2022). This debunking
behavior can broadly reduce the misinformed beliefs of the author and the
audience who see the misinformation (Chan et al., 2017; Bode and Vraga, 2018).
Notably, current research works have shown the promising impact of debunking
(Chan et al., 2017; Bode and Vraga, 2018) in both curbing the perception of
misinformation and reducing the belief of false information (Chan et al.,
2017). In this work, we deep-dive into this misinformation-countering behavior
by looking at both the misinformation posts and the counter-misinformation
replies to these posts. Since user response information can indicate the
textual properties of misinformation posts that are highly likely to get
countered, our work sheds light on better understanding of misinformation-
countering behavior, especially, understanding the misinformation tweets that
get countered.

###  2.2. Analysis of Counter-misinformation

Due to the significance of counter-misinformation messages in curbing
misinformation, much research has been focused on analyzing and understanding
counter-misinformation (Micallef et al., 2020; Vo and Lee, 2019).

One type of work is to analyze and compare misinformation and counter-
misinformation messages (Micallef et al., 2020; Vo and Lee, 2019). For
instance, Micallef et al. (2020) first created a textual classifier to
classify tweets into misinformation, counter-misinformation, and irrelevant
groups, and then analyzed the tweets in each group. Interestingly, they find
that a surge in misinformation tweets results in a corresponding increase in
tweets that reject such misinformation. Vo and Lee (2019) first identified
fact-checking replies by checking whether a reply contains a fact-checking URL
toward two trustworthy fact-checking websites (i.e., Snopes.com and
Politifact.com). Then, they retrieved the corresponding misinformation tweet
toward which the fact-checking post replies to, and use them to construct
pairs of misinformation posts and fact-checking replies for fact-checking
content analysis and reply generation.

Meanwhile, Miyazaki et al. (2022) curated a large-scale dataset containing
pairs of misinformation tweets and debunking replies, by first crawling
COVID-19 related misinformation tweets from existing research (Cui and Lee,
2020; Kim et al., 2021; Hossain et al., 2020; Memon and Carley, 2020; Shahi et
al., 2021) and then recruiting crowd-sourcing workers via Amazon Mechanical
Turk to annotate responses to these tweets as being debunking or not. They
then perform analysis to illustrate who counters misinformation and how they
do so. However, contrary to this work, we conduct an in-depth stratified
analysis of the replies to examine which features matter during the
countering. Stratification helps to compare similar tweets by controlling for
the number of replies it receives. Furthermore, we also conduct analysis of
whether tweets get a high or low proportion of countering replies.
Importantly, we also build two new tasks of predicting which misinformation
posts will get countered and to what degree they get countered. Our work
complements the existing counter-misinformation studies.

###  2.3. Birdwatch (a.k.a. Community Note)

Twitter launched Birdwatch (recently renamed to Community Note) to facilitate
misinformation detection by ordinary users. On the platform, users can report
suspicious and/or misleading tweets, as well as annotate tweets reported by
others. Many have investigated this kind of countering (Allen et al., 2022;
Mujumdar and Kumar, 2021) and derived different patterns among this collective
countering. For instance, Allen et al. (2022) looks at the impact of
partisanship during the crowds’ annotation by analyzing existing data from the
Birdwatch/Community Note platform; they find its users are more likely to (1)
give negative annotations of tweets from counter-partisans, and (2) rate
annotations from counter-partisans as unhelpful. Though Birdwatch/Community
Note enables community-based detection of misinformation, it does not provide
a way for users to counter misinformation. Notably, users provide inputs
within the Birdwatch ecosystem only, which is restricted and does not reflect
the larger dynamics of information flow on Twitter. Recent research has also
shown that Birdwatch can be manipulated by motivated bad actors (Mujumdar and
Kumar, 2021). Therefore, we focus on the misinformation that spreads on
Twitter and is countered by ordinary users for a more complete and
comprehensive study.

##  3\. Dataset

In this section, we describe the definition of the problem, as well as the
corresponding dataset curation.

###  3.1. Definitions

Misinformation: We employ a broad definition of misinformation which includes
falsehoods, inaccuracies, rumors, or misleading leaps of logic (Wu et al.,
2019). Building on the existing work (Hayawi et al., 2022b), we focus on
misinformation related to the COVID-19 vaccine due to its broad impact around
the world during the COVID-19 pandemic. Practically, the misinformative claims
include “the vaccine alters DNA”, “the vaccine causes infertility”, “the
vaccine contains dangerous toxins”, and “the vaccine contains tracking
devices”; these topics are popular and widely studied by existing research
works (Hayawi et al., 2022b; Abbasi, 2022).

Counter-reply: Motivated by existing research works on analyzing replies that
show disbelief toward misinformation (Jiang et al., 2020) or fact-check
misinformation (Pereira and Marques-Neto, 2022), a direct response to a
misinformation post $m$ is considered a “countering” reply if it makes an
attempt to explicitly or implicitly debunk or counter the misinformation tweet
$m$. Otherwise, the reply is considered as non-countering. Practically, given
a reply $r$, it is a:

  * •

Countering reply: Motivated by existing research works on identifying and
analyzing text that is countering, debunking, disbelieving, or disagreeing
with misinformation (Micallef et al., 2020; Jiang et al., 2020; Hossain et
al., 2020), a countering reply is a reply that explicitly or implicitly
refutes the misinformation post (“this is misinformation”), points out the
falsehood (“the COVID-19 vaccine does not change DNA”), insults the tweet
poster (“you are born to lie”), or questions the misinformation (“Is there any
reference I can check?”).

  * •

Non-countering reply: Instead of countering, a non-countering reply supports,
is in favor of, comments, repeats misinformation, etc., such as “This is not
the vaccine but the gene therapy”, “Yes, I agree with you”, or “It makes
sense”.

A post $m$ is considered to be countered if it receives at least one counter-
reply. Meanwhile, given that different misinformation tweets have various
numbers of replies, to have a normalized measure of the magnitude of which a
misinformation tweet gets countered, we define the proportion of counter-
replies to total replies, denoted as $p(m)$.

###  3.2. Task Objective

We consider the set $\mathcal{M}$ of misinformation posts about the COVID-19
vaccine. Each misinformation post $m\in\mathcal{M}$ has a set of $n$ replies
$r=[r_{1},...,r_{n}]$ posted in direct response to $m$. Our final goal is to
build a classifier $\mathcal{F}$ such that it can output a binary label
$\mathcal{F}(m)$, which indicates whether the misinformation post will be
countered or not, i.e., whether it will receive at least one counter-reply.

###  3.3. Dataset Curation

####  3.3.1. Misinformation Tweet Collection and Classification

We utilize the Anti-Vax dataset from Hayawi et al. (2022a), a large-scale
dataset of tweets related to the topic of COVID-19 vaccines, in order to
identify misinformation tweets for our study. These tweets range eight months
from December 1, 2020 to July 31, 2021, which was the relevant period covering
a substantial part of the time from when the vaccines were approved by the FDA
in December 2020 (Sharma et al., 2022). Also during this period, many
uncertainties and misinformation about COVID-19 vaccines were spreading on
social media (Muric et al., 2021; Sharma et al., 2022; Hayawi et al., 2022a).
The original dataset consists of approximately 15.4 million tweets collected
from the Twitter API (Hayawi et al., 2022a), each containing at least one of
the following COVID-19 vaccine relevant keywords: {‘vaccine’, ‘pfizer’,
‘moderna’, ‘astrazeneca’, ‘sputnik’, ‘sinopharm’}. Only original tweets were
considered, i.e., retweet, reply, or quote tweets were removed. We utilized
the Twitter API to retrieve the tweet text, user ID of the tweet author,
datetime, conversation ID, reply settings, and tweet engagement metrics (like,
retweet, quote, and reply counts). In total, we were able to retrieve
14,123,209 tweets from the original dataset while the remaining 1.3 million
tweets were unavailable due to the deletion by the users or the Twitter
platform.

Following the definition of misinformation in Section 3.1 and the current
approach of identifying COVID-19 vaccine related misinformation tweets (Hayawi
et al., 2022a), we first get the annotated misinformation tweets from Hayawi
et al. (2022a), train a text classifier to determine if a tweet is
misinformation or not, and classify all non-annotated tweets. Specifically, we
first crawl and get 4,836 annotated misinformation and 8,596 annotated non-
misinformation tweets from Hayawi et al. (2022a). Next, we build a text
classifier using BERT (Devlin et al., 2019). This classifier has a promising
performance in precision, recall, and F-1 scores of $0.972$, $0.979$, and
$0.975$, respectively. This performance is comparable to the reported one in
the original paper by Hayawi et al. (2022a) (i.e., the precision, recall, and
F-1 scores of $0.97$, $0.98$, and $0.98$). The classifier has high performance
as per the metrics and thus can be used for downstream classification tasks.

Finally, we use this misinformation classifier to identify misinformation
tweets in the entire dataset, resulting in 1,523,849 misinformation tweets and
12,599,360 non-misinformation tweets. Since we only focus on replies to
misinformation in this work, we only use misinformation tweets for downstream
analyses.

Next, we perform filtering of the dataset. Since our work focuses on
categorizing misinformation by the composition of their replies, we further
discard misinformation tweets that have zero replies. In addition, we discard
tweets where the poster has limited the set of users who can reply to their
tweet, to ensure that all tweets in our dataset have an equal opportunity to
be replied to. This information is obtained from the Twitter API.

Finally, our COVID-19 vaccine misinformation tweet dataset consists of 268,990
tweets where each tweet has at least one reply. This is the final set of
misinformation tweets that we use.

####  3.3.2. Counter-misinformation Reply Collection and Classification

For each tweet in our misinformation dataset, we use the Twitter API to crawl
all direct replies to the original tweet. In total, we collected a total of
1,991,611 replies to the 268,990 tweets. One misinformation tweet has an
average of approximately 7.4 replies. The distribution of the reply count per
tweet is shown in Figure 2 in blue.

Building a Counter-reply Classifier: Since it is of high cost to manually
annotate all replies, in order to identify all the counter-replies (and non-
counter-replies) from this set of 1.9 million replies, we train another text-
based classifier to determine if a reply counters the tweet or not. Here, we
call this a ”counter-reply classifier”.

Building on the existing works of the reply classification task (Jiang et al.,
2020), we first annotated replies and then built the classifier. Specifically,
two students each first annotated 500 randomly-selected pairs of tweets and
replies based on the textual contents into ‘Countering’ or ‘Non-Countering’ as
per the definition provided in Section 3.1. This annotation resulted in an
inter-rater agreement score of 0.7033 measured by percent agreement, resulting
in 244 responses expressing countering while the remainder were non-
countering. Then, after discussing the disagreements and creating the same
annotation standard, each annotator labeled another 545 randomly selected
pairs of tweets and replies. In total, we get 802 countering replies and 788
non-countering replies in our final annotated counter-reply dataset.

After getting the annotated replies, we utilize the Roberta-base lower-case
architecture (Liu et al., 2019) as the classifier to which the input is the
pair of tweets and replies. After the hyperparameter search across batch size
and learning rate, the classifier achieves a decent performance with a
precision of 0.834, a recall of 0.819, and an F1-score of 0.822, which is
sufficient for counter-reply classification on unlabeled replies.

Finally, we classify 690,047 (34.65%) replies as counter-replies, and the
remaining 1,301,564 (65.35%) as non-counter-replies. The distribution of the
counter-reply count per tweet is shown in Figure 2 in orange. The average
number of counter-replies that a misinformation tweet has is 2.57, and the
average proportion of all replies of a misinformation tweet that are counter-
replies is 0.271.

Figure 2. Distributions of the total number of replies (blue) and number of
counter-replies (orange) per misinformation tweet, each presented on a log
scale.

####  3.3.3. Misinformation Poster Attribute Collection

For each misinformation tweet, we also collect information of the user who
posted the misinformation tweet, which includes date and time of account
creation, number of tweets posted, account verification, follower count, and
following count. In total, information for 137,929 unique users was retrieved.

Additionally, we collected all the tweets that the user posted in the 7 days
leading up to them posting the misinformation tweet; we refer to these tweets
as “pre-misinformation” tweets. Only original and quote tweets were retrieved;
replies and other retweets were excluded. We pull the same set of attributes
as in the misinformation tweet crawling. In total, we retrieved a total of
31,450,114 “pre-misinformation” tweets, with an average of 116.9 “pre-
misinformation” tweets per misinformation tweet. Note that these numbers
include duplicate tweets if the user had posted two misinformation tweets
within 7 days of each other.

As a final step, we identify the subset of “pre-misinformation” tweets that
are related to the topic of COVID-19 vaccines, as well as those that are also
misinformative. We define a “pre-misinformation” tweet belonging to that
subset if it contains at least one of the aforementioned six keywords that
were used to collect the original Anti-Vax dataset, namely {‘vaccine’,
‘pfizer’, ‘moderna’, ‘astrazeneca’, ‘sputnik’, ‘sinopharm’}. In total,
1,781,161 (5.71%) of the “pre-misinformation” tweets are labeled as being
about COVID-19 vaccines. We then utilize the aforementioned misinformation
classifier to identify COVID-19 vaccine misinformation within this subset of
“pre- misinformation” tweets, of which 335,458 (18.83%) were classified as
misinformative.

##  4\. Characterization of Counter-reply

In this section, we analyze the properties of misinformation tweets with
respect to the degree to which their misinformation gets countered. In order
to do so, we identify the tweets that see a high proportion of their replies
being counter-replies, and compare it to the group that see a low proportion
of their replies being counter-replies.

To avoid skewing the results due to extreme data points, for this analysis, we
do not consider tweets at the two extremes of the “reply count” distribution –
specifically, we remove tweets with fewer than three replies, as well as the
top 2% of tweets that have the greatest number of replies, following similar
tweet filtering procedures in existing research works (Yuan et al., 2016;
Aleksandric et al., 2022; Alvarez-Melis and Saveski, 2016). This is done to
remove dataset noise related to low-engagement tweets, along with outliers
associated with the highest engagement tweets. After this process, we are left
with 74,663 misinformation tweets, with reply counts ranging from 3 to 52
(both inclusive).

Figure 3. Distribution of proportion of counter-replies for each stratum. Each
boxplot represents a stratum, displaying the minimum, maximum, quartiles, and
(any) outliers. Figure 4. Number of tweets in each of the “Low Countered”
(yellow) and “Highly Countered” (red) groups for each stratum, presented on a
log scale.

###  4.1. Stratified Dataset Creation

The linguistic, engagement, and user-level properties of tweets that get a low
number of replies are different from those of tweets that receive many replies
(Matsumoto et al., 2019; Bruns and Stieglitz, 2013, 2012). Thus, to avoid
conflating the factors that lead to receiving a high number of replies with
the factors to receive counter-replies, we define and create several strata
based on the number of replies that a misinformation tweet receives.
Specifically, the strata are defined as follows: [3, 5], [6, 10], [11, 15], …,
[46, 50]. Each stratum contains similar misinformation tweets that receive a
similar number of replies, with some tweets that get countered and others that
do not. We then compare these two groups within each stratum. Figure 3 shows
the distribution of counter-reply proportion within each stratum. We observe
that, with the exception of tweets with a lower number of replies (that have
more tweets with relatively fewer counter-replies), the distribution is
similar across reply counts.

Within each stratum, we assign tweets to a “Highly Countered” group if its
counter-reply proportion is in the top quartile (also within that stratum), a
“Low Countered” group if its counter-reply proportion is in the bottom
quartile (within that stratum), or discard it if it does not fall into either
of the two groups. Figure 4 shows the distribution of the tweets in the two
relevant categories.

Within each stratum, we compare misinformation tweets between the two groups.
We identify three types of attributes to perform this comparison along:

  1. (1)

Tweet linguistic attributes, to analyze the degree to which the tweet falls
into meaningful personal, psychological, topical, emotion, and other content-
related categories.

  2. (2)

Tweet engagement attributes, to analyze how and how much the tweet is
interacted with among online users.

  3. (3)

Tweet poster attributes, to analyze the behavior, popularity, and status of
the user behind the tweet.

Table 2 displays the full list of attributes we study within each of these
categories. We present results in the following subsections.

Attribute type |  List of attributes
---|---
Tweet linguistic |  • number of words in the tweet***
|  • VADER (Hutto and Gilbert, 2014) positive sentiment, negative
sentiment***, and compound sentiment*** of the tweet
|  • Politeness*** and impoliteness*** scores of the tweet, computed as the
total number of linguistic strategy instances in the tweet positively and
negatively correlated (respectively) with politeness as proposed by (Danescu-
Niculescu-Mizil et al., 2013).
|  • For each of the 65 (47*** + 18) dimensions of the LIWC (Pennebaker et
al., 2001) 2007 lexicon, the number of words for that dimension.
Tweet engagement |  • number of replies***, likes***, retweets*** (RTs), and quote tweets (QTs)***
|  • number of likes, retweets (RTs), and quote tweets (QTs)***, each divided
by the number of replies
Tweet poster |  • number of followers, number of users following***, whether the user is verified (1) or not (0)***
|  • Total number of tweets the user has posted since account creation***
|  • In the week (7 days) leading up to the misinformation tweet: the average
# of tweets posted per day***, the median count of likes*** and retweets***
received on their tweets, the number of tweets the user posted about COVID-19
vaccines***, and the proportion of COVID-19 vaccine tweets that are
misinformation.
Table 1. List of linguistic, engagement, and poster attributes considered for
the analysis in Section 4. A set of three asterisks(***) next to the attribute
indicates a statistical test result of p ¡ 0.001.222This statistical test was
performed using Welch’s unequal variances t-test between the upper and lower
quartiles (with respect to the proportion of counter-replies) of the data
visualized in Figure 3.This subset of statistically significant attributes is
considered for the predictive tasks in Section 6.

(a) LIWC Affect score

(b) VADER negative sentiment

(c) LIWC Anger score

(d) Impoliteness score

(e) LIWC Health score

(f) LIWC SheHe score

(g) Fraction of quote tweets (QTs) among all the replies

(h) Fraction of retweets (RTs) among all the replies

Figure 5. Means and 95% confidence intervals of the linguistic and engagement
attributes of misinformation tweets that get highly countered versus those
that do not.

###  4.2. Linguistic Attributes of Tweets that are Countered

First, we observe from Figure 5(a) that on average, “Highly Countered” tweets
contain 32.1% higher usage of affective language (words and phrases that
appeal more to emotions) than “Low Countered” tweets (p ¡ 0.05 for all
strata333all p-values in Sections 4.2, 4.3, and 4.4 are calculated using
Welch’s unequal variances t-tests.; average Cohen’s d = 0.277)444“average
Cohen’s d” here (and elsewhere in this paper) refers to the unweighted average
of Cohen’s d values of each stratum.. This indicates that those who post
counter-replies tend to gravitate more towards replying to misinformation that
induces a stronger emotional reaction in them. This is consistent with the
finding that emotional content gets more attention on social media in existing
research works (Schreiner et al., 2021). Further, we find that “Highly
Countered” tweets express significantly higher negative sentiment than “Low
Countered” tweets across all strata. Figure 5(b) shows this result for VADER
negative sentiment (p ¡ 0.05 for all strata; average Cohen’s d = 0.304); we
find similar results for the “negative emotion” dimension of the LIWC lexicon
(p ¡ 0.05 for all strata; average Cohen’s d = 0.279). In particular, we find
that on average, “Highly Countered” tweets contain 104% more anger-related
words than “Low Countered” tweets (see Figure 5(c)) (p ¡ 0.01 for all strata;
average Cohen’s d = 0.347). This implies that the negative tone of
misinformation tweets attract more attention (Huang and Luo, 2007; Haselmayer
et al., 2019), and therefore, more counter-replies.

In addition, we measure differences in the degree to which the misinformation
tweet expresses politeness and impoliteness. We do this by identifying the
sets of linguistic strategies associated with each as presented in (Danescu-
Niculescu-Mizil et al., 2013), and compute the total number of linguistic
instances associated with each set to derive the “politeness” and
“impoliteness” score, respectively. As shown in Figure 5(d), on average,
“Highly Countered” tweets utilize 23.1% more strategies associated with
impoliteness than “Low Countered” tweets (p ¡ 0.05 for all but one stratum;
average Cohen’s d = 0.248); this finding is consistent with the previous
findings involving negative sentiment. Meanwhile, we do not find a significant
difference between the groups for strategies associated with politeness,
implying that trying to be polite in presenting a misinformation tweet does
not significantly impact the chance of being countered.

Next, we find that there exist differences in topical presence between “Highly
Countered” and “Low Countered” tweets. Figure 5(e) shows that on average,
“Highly Countered” tweets utilize 28.7% fewer health-related terms than “Low
Countered” tweets (p ¡ 0.05 for all but one stratum; average Cohen’s d =
0.220). This suggests that for the average counter-reply poster, the inclusion
of more technical medical terminology might pose a barrier for their
willingness or ability to post an effective debunking response. One possible
reason is that the inclusion of technical health-related terms can signal
authority over the topic and be more convincing to the reader (Buehl et al.,
2001; Habernal and Gurevych, 2016).

We also find that “Highly Countered” tweets use 2.5 times more third-person
pronouns (e.g., ‘he’, ‘she’, ‘they’, ‘them’, etc.) than “Low Countered” tweets
(p ¡ 0.05 for all but one stratum; average Cohen’s d = 0.259; see Figure
5(f)).

###  4.3. Engagement Attributes of Tweets that are Countered

In this subsection, we study the impact of engagement attributes (e.g., likes,
retweets, etc.) on whether misinformation gets countered. There are two
possibilities: (1) first, misinformation tweets with higher engagement get
countered more often because the misinformation gets more attention and
therefore, have a higher likelihood of becoming accessible to someone who
would counter it; (2) second, misinformation tweets that get countered are
less likely to be liked or retweeted by others. We investigate which of the
two possibilities hold as per the data.

In addition to the reply count, we compare tweets using the number of likes,
retweets (RT), and quotes (QT) they receive. As these methods of engagement on
the platform serve a different purpose and have different functionality than
the “reply” method, it is worth using these metrics in our cross-group
comparison. In order to effectively capture these differences with respect to
reply count, we first perform a scaling of these attributes by dividing by the
reply count, then performing comparisons of this quotient across the two
groups.

Figure 5(g) shows that on average, “Highly Countered” tweets receive 37.6%
fewer QTs relative to replies on average (p ¡ 0.05 for all strata). This
difference is very small at the lowest stratum (8.9% fewer; Cohen’s d = 0.05),
but is much higher on the highest stratum (57.4% fewer; Cohen’s d = 0.37). We
receive similar results for retweets and likes; on average, “Highly Countered”
tweets receive 27.4% fewer retweets relative to replies (p ¡ 0.05 for all but
one stratum; see Figure 5(h)) and 25.6% fewer likes relative to replies (p ¡
0.05 for all but 3 strata).

These findings show that the presence of counter-replies on a tweet
organically decreases engagement by average users, suggesting that the
practice of countering is potentially effective at reducing the spread of
misinformation (Colliander, 2019; Wijenayake et al., 2020; Friggeri et al.,
2014).

###  4.4. User Attributes of Tweet Posters that are Countered

First, we study the impact of the user being verified on Twitter on the tweet
getting countered. We find that, on average, the proportion of “Highly
Countered” misinformation posters that are verified is 16.8% higher than that
for “Low Countered” misinformation posters (p ¡ 0.05 in all but 3 strata;
average Cohen’s d = 0.143).

Since the majority of the posters on Twitter are non-verified, we study that
set of users next. We compare the attributes of non-verified users in the
“Highly Countered” group versus the “Low Countered” group. For the remainder
of the attributes, we found none of them to be statistically different across
the two groups. Thus, together with the linguistic results presented in
Section 4.2, we find that the content of the misinformation tweet is more
important in attracting countering than the user who posts the misinformation.

##  5\. Inequality In Social Correction

We further investigate the potential inequality in social correction. This can
help identify whether certain types of users are less likely to be countered,
leading to an increase in disparity. Motivated by existing work (Verma et al.,
2022a), we use education level as a key demographic variable to illustrate the
potential inequality between different users. Since lack of education and
literacy play a crucial role in believing in misinformation (Roozenbeek et
al., 2020; Georgiou et al., 2020; van Prooijen, 2017), it is important to
study whether it also impacts correction.

We derived the education level of users by quantifying the readability of
their posts using the Automated Readability Index (ARI), which is known to
produce an approximate representation of education level in prior works
(Senter and Smith, 1967; Flekova et al., 2016; Rajadesingan et al., 2015). A
higher ARI corresponds to a higher education level. We use the “pre-
misinformation” posts of each user (i.e., posts made within the 7 days prior
to posting the misinformation tweet) to calculate that user’s ARI. Then, for
each post, we compute the ARI score (Senter and Smith, 1967; Flekova et al.,
2016; Rajadesingan et al., 2015). Finally, we compute the average of these
scores, and use it as the final ARI value to present the education level of
the user. Thus, it should be noted that the ARI score is not the education
level portrayed in the misinformation tweet, but instead, the education level
derived across the historical posts of the user who spread misinformation
tweets. We randomly sampled 10,000 users who spread misinformation in our
dataset to illustrate the inequality phenomenon.

As shown in Figure 6, we find that misinformation posts made by users with
lower education levels have a higher likelihood of getting corrected. There is
a systematically negative trend with an increase in the user’s (perceived)
education level. This highlights a need to pay attention to misinformation
spread by users who portray a higher education level, since ordinary users are
less likely to correct them.

Figure 6. Comparison of user communities with different education levels. As
shown, users with lower education levels will have higher possibilities of
getting countered when sending misinformation tweets.

##  6\. Social Correction Prediction

In this section, we aim to answer two research questions:

  * •

RQ1: Given a misinformation tweet, can we predict whether it will be countered
or not in the future?

  * •

RQ2: Given a misinformation tweet that will be countered in the future, can we
predict whether it will be countered with fewer or more counter-replies?

Both RQs are important to address for the combating of future misinformation.
By being able to effectively predict future interactions surrounding
misinformation tweets, we can better identify sets of online interactions
where misinformation is being organically countered, along with those where
additional countering needs to be performed. Answering RQ1 can identify sets
of misinformation posts where other users may take the initiative in posting a
counter-reply, while answering RQ2 can predict the intensity or magnitude of
countering.

###  6.1. Dataset

For both the research questions, we use the aforementioned dataset as we
described in Section 4.

For RQ1, we divide the dataset into two sets of misinformation tweets: (1)
misinformation tweets that have replies but none of them are counter-replies;
(2) misinformation tweets that have at least one counter-reply. The sizes of
these sets are 17,787 and 55,136, respectively. For RQ2, we divide the
misinformation tweets into two groups: one with a low proportion of counter-
replies, and another group with a high proportion of counter-replies. Similar
to the stratified setup in Section 4.1, we use the proportion of counter-
replies as an indicator of membership for the two groups. The bottom 25% of
posts with respect to their countering proportion are assigned to the low
countered group. On the other hand, the top 25% posts with the highest
proportion of countering replies are assigned to the highly countered group.
The sizes of these sets are 14,274 and 15,224, respectively.

###  6.2. Experimental setup

Using each of these datasets, we follow similar approaches in tweet prediction
tasks (Micallef et al., 2020; Zahera et al., 2019) to address both RQ1 and
RQ2. We aim to build a binary classifier for each of RQ1 and RQ2, using the
label definitions described above. For both RQs, we use the same set of
features. We begin with the set of attributes listed in Table 2 with p ¡ 0.001
and have non-null values for all datapoints; there are 63 such attributes (53
linguistic, 5 engagement, 5 poster). As shown in the existing tweet prediction
task (Micallef et al., 2020), the semantic information from textual embedding
benefits the prediction task. Thus, we also generate the embedding vector for
each tweet using RoBERTa (Liu et al., 2019), which results in a
768-dimensional feature vector. Finally, we concatenate the above feature
vectors to form a tweet feature vector to comprehensively represent the tweet
and use it for classification.

Classifier: Following similar tweet or general text classification tasks
(Micallef et al., 2020; He et al., 2021b; He et al., 2021a), we deploy widely-
used conventional machine learning classifiers including Logistic Regression,
XGBoost, and a Feed-forward Neural Network with a single hidden layer, using
the feature vector as input. During the experiment, 10-fold cross-validation
is deployed, and we report precision, recall, and F-1 score as the performance
metrics.

###  6.3. Classifier Performance

Method | Precision | Recall | F-1 score
---|---|---|---
Logistic Regression | 0.801 | 0.929 | 0.860
XGBoost | 0.803 | 0.908 | 0.852
Neural Network | 0.804 | 0.914 | 0.855
Table 2. RQ1: Classifier performance of whether tweets will get countered or
not.

In Table 2, we report the classification result for RQ1. As we can see, all
three models are able to achieve good performance on the task. The logistic
regression achieves the best performance in terms of precision, recall, and
F-1 score; this result is also found in other similar tweet classification
tasks (Micallef et al., 2020). This high performance grants the ability to
effectively predict whether a tweet will be countered or not, enabling fact-
checkers and social media platforms to prioritize countering tweets identified
as less likely to be countered organically.

Method | Precision | Recall | F1 score
---|---|---|---
Logistic Regression | 0.731 | 0.742 | 0.737
XGBoost | 0.841 | 0.756 | 0.796
Neural Network | 0.848 | 0.759 | 0.801
Table 3. RQ2: Classification performance of whether tweets will be highly
countered versus that will be low countered.

For RQ2, the classification result is shown in Table 3. As we can see, the
model performance is still reasonably acceptable, but is worse compared to
RQ1. This decrease in performance may imply that the task to identify the
intensity of countering tweets is not only more difficult, but also distinct
from the task to identify whether a tweet will be countered. In other words,
the phenomenon of posting of the first counter-reply is easier to forecast
than that of the posting of additional counter-replies given that at least one
has already been posted.

##  7\. Discussion and Conclusion

In this paper, we studied the tweet and user-level properties of
misinformation tweets that get countered versus those that do not. The in-
depth analysis shows that misinformation tweets expressing negative emotion,
strong emotion, third-person pronouns, and strategies associated with
impoliteness are more likely to result in more countering replies from users.
Our result also shows that tweets that get countered have a higher amount of
reply engagement in proportion to like, retweet, and quote tweet engagement.
Moreover, we develop well-performing classifiers to predict whether a
misinformation tweet will be countered or not, and if so, to what degree they
will be countered (i.e. the proportion of its replies that end up being
counter-replies).

Given the statistical significance of our analysis and the high performance of
our classifiers, we demonstrate that it is possible to identify tweets that
are more or less likely to get countered. In particular, nearly all of these
attributes (tweet linguistic attributes and user attributes) are readily
available as soon as the tweet is posted, allowing for the quantity of future
counter-misinformation (or the lack thereof) to be reasonably forecast. This
can have major implications in times of breaking news or other such events in
which large quantities of (mis-)information are posted to online platforms at
a rapid rate; in conjunction with state-of-the-art misinformation detection
approaches, the counter-reply prediction approach presented in this paper can
be used to identify tweets that are less likely to be countered, possibly
necessitating additional platform-level approaches to control the spread of
misinformation for these tweets. One of these approaches may be adding or
increasing interventions to draw attention towards accuracy, an approach that
has been shown to be effective in discouraging users from spreading
misinformation (Pennycook et al., 2020).

A limitation of this work that it focuses on only one platform: Twitter. On
other online platforms, different mechanisms of post and user engagement, as
well as information exchange, may be present (Micallef et al., 2022), possibly
influencing the types of misinformation tweets and posters users will choose
to counter. Another limitation is that it studies only one topic (COVID-19
vaccines), which has become one of the most widely discussed topics in our
society due to the universal effects of the COVID-19 pandemic. On
misinformation-related topics that might be more obscure or less widely
discussed (e.g. flat earth theories), it could be possible that the more
specific demographics of misinformation and/or counter-reply posters may
affect the ways in which they interact. In addition, we only study text in the
English language; the dynamics and discussion in other languages and other
modalities (images, videos) may differ (Verma et al., 2022b).

For future work, similar analysis can be performed on the user network
surrounding the misinformation poster and counter-reply poster (e.g. their
followers and those they follow, how much misinformation these accounts
spread, etc.) in order to assess if there are any network-related attributes
that may increase the likelihood of counter-replies. In addition, given that
we can reasonably determine which tweets will and will not be countered, it
would also be valuable to perform user studies or field studies to evaluate if
certain characteristics about online encounters with misinformation can
increase (or decrease) the likelihood of a user posting a counter-reply. Also,
while we explore it in Section 5, further studies can be done to understand
the inequities surrounding counter-reply targets along additional demographic,
social, political, and/or geographic dimensions; this can allow further
exploration of the greater societal implications surrounding counter-
misinformation.

ACKNOWLEDGMENTS This research/material is based upon work supported in part by
NSF grants CNS-2154118, IIS-2027689, ITE-2137724, ITE-2230692, CNS-2239879,
and funding from Microsoft, Google, and Adobe Inc. Any opinions, findings and
conclusions or recommendations expressed in this material are those of the
author(s) and do not necessarily reflect the position or policy of NSF and no
official endorsement should be inferred. We thank the CLAWS research group
members for their help on the project.

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