# Inside the echo chamber: Linguistic underpinnings of misinformation on
Twitter

Xinyu Wang  The Pennsylvania State UniversityUniversity Park, PAUSA
<EMAIL_ADDRESS>,  Jiayi Li  The Pennsylvania State UniversityUniversity Park,
PAUSA<EMAIL_ADDRESS>and  Sarah Rajtmajer  The Pennsylvania State
UniversityUniversity Park, PAUSA<EMAIL_ADDRESS>
(2024)

###### Abstract.

Social media users drive the spread of misinformation online by sharing posts
that include erroneous information or commenting on controversial topics with
unsubstantiated arguments often in earnest. Work on echo chambers has
suggested that users’ perspectives are reinforced through repeated
interactions with like-minded peers, promoted by homophily and bias in
information diffusion. Building on long-standing interest in the social bases
of language and linguistic underpinnings of social behavior, this work
explores how conversations around misinformation are mediated through language
use. We compare a number of linguistic measures, e.g., in-/out-group cues,
readability, and discourse connectives, within and across topics of
conversation and user communities. Our findings reveal increased presence of
group identity signals and processing fluency within echo chambers during
discussions of misinformation. We discuss the specific character of these
broader trends across topics and examine contextual influences.

Social Media, Misinformation, Echo Chamber, Social Networks, Socio-
linguistics, Group Identity, Processing Fluency Theory

††journalyear: 2024††copyright: acmlicensed††conference: ACM Web Science
Conference; May 21–24, 2024; Stuttgart, Germany††booktitle: ACM Web Science
Conference (Websci ’24), May 21–24, 2024, Stuttgart, Germany††doi:
10.1145/3614419.3644009††isbn: 979-8-4007-0334-8/24/05††ccs: Information
systems Social networks††ccs: Applied computing Sociology††ccs: Applied
computing Psychology††ccs: Computing methodologies Discourse, dialogue and
pragmatics

##  1\. Introduction

In recent years, Online Social Networks (OSNs) have developed into a primary
source of news for a growing majority of the US population (Levy, 2021). As
OSNs have expanded, so have concerns about the role of social media in
promoting misinformation and deepening social fissures (Allcott et al., 2019;
Zimdars and McLeod, 2020), the consequences of which were particularly
tangible during the COVID-19 pandemic and response (Abrams and Greenhawt,
2020; Brennen et al., 2020; Bursztyn et al., 2020).

User interactions in OSNs are scaffolded by platforms’ algorithms and
moderation practices. Optimized primarily for engagement, these algorithms
promote interaction with like-minded peers and isolate users from disagreeable
content. While this can create safe space for individuals to share opinions
and find validation, it can also drive polarization (Barberá, 2020; Cinelli et
al., 2021; Rhodes, 2022). Some have suggested that the effects of so-called
_filter bubbles_ and _echo chambers_ have been overstated or oversimplified
and have argued for a more nuanced characterization based on, e.g., ideology
or topic of discussion (Barberá et al., 2015; Bruns, 2017; Guess et al.,
2018). Irrespective, these phenomena speak to the broader, well-studied
phenomenon of homophily in social group formation (McPherson et al., 2001;
Currarini et al., 2009). In the online setting, user-generated content
facilitates aggregation around shared interests and worldviews, which in turn
serves to support the deepening and reinforcement of social ties (Del Vicario
et al., 2016).

At the heart of OSN communities is discourse rich with linguistic signals of
individual and relational identities. That is, users of language perform their
identities within the uses of language (Benwell, 2006; Zappavigna, 2014; Zhang
et al., 2017). This is both heightened and complicated by the affordances of
social media (Tagg and Seargeant, 2014). Identities are partially performed
through alignment with communities, opinions, and cultural issues (Tagg and
Seargeant, 2014). Online, this can be enacted as direct engagement and
participation in groups, or through indirect interactions, e.g., hashtagging,
sharing memes (Zappavigna, 2011, 2014), or even simple repetition (Rosenblum
and Muirhead, 2019).

Several schools of thought have driven work to understand the co-evolution of
language, social ties, and the environment (Group” et al., 2009; Lupyan and
Dale, 2010; Roberts and Winters, 2012; Bentz et al., 2018; Raviv et al.,
2020). Languages adapt to the constraints and biases of their learners, which
include social, physical and technological context (Dale and Lupyan, 2012;
Lupyan and Dale, 2016). Foundational work in sociolinguistics, for example,
has studied the ways in which language change correlates with social network
dynamics (Milroy and Milroy, 1992; Blommaert, 2010). Shared linguistic
features can enhance so-called _processing fluency_ , making communications
more effective and reinforcing community bonds (Canestrino et al., 2022;
Pancer et al., 2019). Within the landscape of social media, both notions of
community and language practices are flexible and interactively constructed
(Tagg and Seargeant, 2014).

Our work is a study of the relationships between social media users’
linguistic patterns and the community structures in which they are embedded.
We have selected misinformation as our point of inquiry because prior work has
suggested that misinformation spreads particularly quickly (Karlova and
Fisher, 2013; Kumar and Geethakumari, 2014; Del Vicario et al., 2016; Shin et
al., 2018; Vosoughi et al., 2018) and because a shared vocabulary has emerged
around these topics. We are motivated by two primary research questions.

RQ1: Is there an observable increase in users’ signaling of group identity
through publicly shared content within echo chambers? Is this phenomenon more
pronounced during discussions that involve topics related to misinformation?

RQ2: Is there heightened processing fluency in publicly shared content within
echo chambers? Is this phenomenon more pronounced during discussions that
involve topics related to misinformation?

To address these questions, we identify echo chambers from user interaction
networks and study their linguistic characteristics. Our analysis suggests
that while there are some shared linguistic patterns among certain topics,
linguistic characteristics observed within echo chambers do not exhibit
uniformity across all discussions of misinformation. Rather, these patterns
appear to be contingent upon contextual factors (e.g., political nature of the
topic). This interplay between linguistic uniformity and contextual
particularity constitutes is at the core of our work, contributing to deeper
understanding of the linguistic underpinnings of echo chambers, particularly
in the context of misinformation-related topics.

##  2\. Related Work

###  2.1. Misinformation dynamics

The extensive presence of user-generated content on online social media
platforms enables the formation of communities that unite around shared
beliefs and narratives. Echo chambers, where beliefs are amplified and
reinforced by repeated internal validation, play a role in the dynamics of
misinformation within online platforms (Del Vicario et al., 2016). The
architecture of modern social media algorithms, which prioritizes user
engagement, can inadvertently foster echo-chambered communities (Zollo et al.,
2015). Within these environments, misinformation thrives and evolves, taking
on new narratives that further reinforce the community’s existing beliefs
(Vosoughi et al., 2018).

###  2.2. Social identity formation

Social identity formation emerges as a critical process underlying group
dynamics and individual affiliations in social networks. In this study, social
identities are performed and measured through a combination of explicit
identity cues, as explained by social identity theory, and implicit identity
cues via ambient affiliation.

####  2.2.1. Social identity theory.

Social identity theory posits that individuals tend to see themselves and
others in relation to groups (Tajfel et al., 1979; Tajfel and Turner, 2004;
Stets and Burke, 2000). When a group identity becomes prominent, individuals
develop their self-concept around paradigmatic ingroup characteristics (Brewer
et al., 1999). They are motivated to differentiate between their ingroup and
relevant outgroups (Li and Su, 2020). Social identity theory has been engaged
to describe political group formation in online social communities, e.g.,
(Greene, 2004; Trepte and Loy, 2017).

Recent work (Li and Su, 2020) looked at discussions containing the term “fake
news” on Twitter and found an uptrend in the use of identity language, as
measured by increased frequency of group pronouns. They also found that group
pronouns were likely to collocate with words boosting ingroup messaging. These
findings dovetail with concerns about polarization. Rathje et al. (Rathje et
al., 2021) found out-group animosity to be a predictor of engagement on
Facebook and Twitter, due in part to algorithmic designs favoring messages
containing out-group antagonism. Our work operationalizes linguistic
indicators of social identity as in-/out-group reference language.

####  2.2.2. Ambient affiliation.

So-called _ambient affiliation_ through hashtagging has evolved as a
ubiquitous mechanism for social media users to associate and communicate in
digital environments, especially on OSNs. It allows for the spontaneous
aggregation of like-minded individuals around shared interests, experiences,
or sentiments, often leading to the establishment of virtual communities or
“micro-groupings” (Zappavigna, 2011). More than just categorization, the act
of hashtagging serves as an instrument of social group identity formation. By
selecting specific hashtags, users signal an allegiance or a stance, fostering
both inclusion within communities and distinction from others. Consistent
patterns of hashtagging behaviors can solidify these group identities,
creating online communities unified by shared narratives and ideologies
(Zappavigna, 2012, 2014).

###  2.3. Processing fluency

Processing fluency theory suggests that the subjective experience of ease with
which people process information influences people’s judgments of that
information (Reber et al., 1998; Reber, 2012). People are more likely to judge
information favorably if they are able to digest it easily. This effect has
been shown robust over diverse experimental settings and a variety of
formulations of fluency, e.g., conceptual, perceptual, linguistic
(Oppenheimer, 2008; Alter and Oppenheimer, 2009). Central to this theory is
the notion that enhanced fluency can be interpreted in multiple ways: it might
signify the benefits of a simplified message; the role of priming and
familiarity; or, the importance of explanation and elaboration.

####  2.3.1. Simplicity bolsters information processing.

Recent work has shown that easy-to-read social media posts facilitate
processing fluency, resulting in more likes, comments, and shares (Bailey,
2013; Pancer et al., 2019). Likewise, repeated assertions gain a valence of
credibility (Hasher et al., 1977; Reber and Unkelbach, 2010; Unkelbach and
Rom, 2017). The so-called repetition-induced truth effect explains one
mechanism by which individuals come to trust obviously false information, a
concern magnified by echo chambers (Pennycook et al., 2018; Unkelbach and
Speckmann, 2021). In the context of today’s misinformation landscape, scholars
have noted that conspiracism is increasingly expressed as bare assertion
(Rosenblum and Muirhead, 2019). New conspiracists repeat simple statements,
signaling solidarity with one another through retweets, likes, and shares
(Rosenblum and Muirhead, 2019).

####  2.3.2. Elaboration bolsters information processing.

In parallel, there exists a body of psychological theories suggesting that
explanations bolster credibility of a claim, namely, the idea of _rational
persuasion_. Information-processing theories of human cognition have explored
the ways in which we engage in the reception, encoding, storage, and retrieval
of information (Simon, 1978; Anderson and Crawford, 1980). Systematic
information processing is associated with explanations, whereby individuals
engage in effortful scrutiny and comparison of information (Trumbo, 1999;
Todorov et al., 2002; Chaiken and Ledgerwood, 2012). Relatedly, the
Elaboration Likelihood Model (ELM) (Petty Richard and Cacioppo, 1986; Petty
and Wegener, 1999) suggests that when people process information deeply, they
are more likely to be influenced by it. Prior studies on the QAnon conspiracy
have demonstrated the efficacy of this technique. Individuals aligned with
these beliefs use archival practices to enable the construction and diffusion
of knowledge within their community. Members are urged to engage in critical
thinking and do extensive research using key terms promoted by the group
(Marwick and Partin, 2022).

##  3\. Data

We collected tweets using the Twitter Developer API and keywords associated
with our target topics, namely: anti-vaccination; 2020 US election fraud; and,
QAnon. We pulled author ID, tweet ID, and referenced tweets (type and ID) for
interaction network construction and tweet content for linguistic analyses.
Tweets were collected using popular keywords and hashtags over date ranges
selected for greatest topic relevance. With the exception of discussion
surrounding the US election which has a shorter activity period and limited
number of tweets, we collect approximately 1 million tweets per topic,
adjusting date ranges accordingly. In addition, we collected discussions to
serve as comparative for our misinformation-related discourse: the MeToo
movement, Black Lives Matter movement (BLM), and #mondaymotivation. While not
generally associated with misinformation, these have been topics of widespread
social media discourse, representing very different degrees of social
sensitivity and polarization.

_Pre-processing._ We retained only tweets containing interactions within the
datasets, as well as original tweets without interactions, to ensure that all
users in the social network have representative linguistic content. Dataset
statistics are provided in Table 1. We removed mentions, #hashtags, URLs, RT
signs, and emojis. For readability tests, we retained punctuation marks to
allow sentence length calculation. Hashtags and URLs were extracted directly
from the raw data. Tweet IDs and code have been made publicly available and
are provided with detailed documentation including a Datasheet (Gebru et al.,
2021) and README111Github link: https://github.com/XinyuWang1998/Linguistic-
Underpinning.

Category | Topic | keywords (case-insensitive) | Time Frame |  Total Tweets |  Total Tweets after Filtering
---|---|---|---|---|---
Misinformation |  US Election Fraud |  voterfraud, discardedballots, cheatingdemocrats, stopvoterfraud, voterfraudbymail, voterfraudisreal, ballotharvasting, ballotvoterfraud |  [2020-11-01, 2021-02-28] | 256,036 | 219,872
Anti- Vaccination |  antivax, antivaxx, novaccine, novaccinemandates, pureblood, unvaxxed, unvaccinated, naturalimmunity, nomandatoryvaccines |  [2021-11-15, 2021-11-30] | 1,224,306 | 1,139,378
QAnon |  qanon, thegreatawakening, pizzagate, savethechildren, deepstate, thestormisuponus |  [2019-11-01, 2020-03-31] | 902,862 | 792,733
Hyper-polarized |  MeToo Movement |  metoo, hertoo, ustoo, metoocongress, timesup (Xiong et al., 2019) |  [2017-10-15, 2017-10-31] | 845,472 | 828,856
BLM Movement |  blacklivesmatter,georgefloyd, icantbreathe, blm, justiceforfloyd, justiceforgeorgefloyd, georgefloydprotests, worldagainstracism, walkwithus, kneelwithus, blackouttuesday, voteouthate, nojusticenopeace, blackwomenmatter, blackgirlsmatter, theshowmustbepaused(Field et al., 2022) |  [2020-06-01 09:00AM, 2020-06-01 05:00PM] | 994,663 | 968,270
Neutral |  Monday Motivation | monday motivation |  [2021-09-01, 2021-11-30] | 896,350 | 892,096
Table 1. Dataset statistics

##  4\. Preliminaries

We introduce key definitions and measures used throughout.

###  4.1. Linguistic metrics

Linguistic features were computed using and extending state-of-the-art open-
source toolboxes for linguistic analyses. In- and out-group language was used
to measure group identity formation. Group identity is expressed and sustained
through pronouns; pronominal choices are not just categorical references but
also reflect power dynamics and relationships (Íñigo-Mora, 2004; Pennycook,
1994). Specifically, “we-pronouns” are used to invoke ingroup solidarity while
“they-pronouns” create distance with the outgroup, often implying the
superiority of one’s own community (Brewer et al., 1999; Li and Su, 2020). We
operationalize simplicity and elaboration as theoretical constructs through
discourse connectives, big words, and readability metrics. Research has shown
that textually less complex and highly readable content facilitates processing
fluency, resulting in more social interactive behaviors (Pancer et al., 2019).
While, discourse connectives may also improve online processing and reading
comprehension in both narrative and expository texts (Zufferey et al., 2015;
Crible et al., 2021). Evidence from these studies forms the basis for our
approach.

####  4.1.1. In-/out-group identity language.

In-/out-group language ratio (Crossley et al., 2016) is used as an index of
group identity. Exemplars of in-/out-group cues include: we, our, us, they,
their, them, themselves.

####  4.1.2. Big words.

Words containing seven or more letters serve as a rough indicator of language
complexity. This metric is derived from The Linguistic Inquiry and Word Count
(LIWC) (Pennebaker Conglomerates, Inc., 2022) software.

####  4.1.3. Readability.

We employ the Flesch reading ease score (Flesch, 1948) as a measure of
readability based on two factors: _average sentence length_ and _average
number of syllables in each word_.

####  4.1.4. Discourse connectives.

We consider the incidence of discourse connectives (Addawood et al., 2019;
Crossley et al., 2016) as an indicator of explanation/elaboration. Our
analyses consider three families of discourse connectives.

Logical connectives reflect logical explanation, e.g., because, consequently,
hence;

Additive connectives reflect agreement and further explanation, e.g., and,
besides, further;

Negative connectives, indicate an adversative argument, e.g., but,
alternatively, although.

Connectives ratios are calculated as the number of connective words from the
given family divided by total number of words.

###  4.2. Network representation

We construct user interaction networks for each topic. Users are represented
as nodes and interactions–replies, quotes, retweets–are represented as
directed, weighted edges. Edge weights indicate number of interactions.

Echo chambers are recognized by coherent content and structural connectivity.
Given that our interaction networks are topic-specific by virtue of our data
collection, there is coherence in content by design. For structural
identification of echo chamber members, we focus on _strongly connected
components_ , i.e., subgraphs in which there is a path from each vertex to
every other, respecting directionality of the edges. This approach follows
precedent set in prior literature, e.g., (Cota et al., 2019), and is
particularly suitable for modeling echo chambers on platforms like Twitter
where interactions are often non-reciprocal (Kwak et al., 2010; Smith et al.,
2012). Following, we identify users within strongly connected components
($n\geq 2$) as echo chamber members and their shared content as echo chamber
tweets. User statistics are provided in Table 2.

###  4.3. Ambient affiliation

Sociolinguists have theorized that Twitter users signal group identity
implicitly through hash-tagging. Hashtags serve as a semiotic identifier for
the discourse community they establish, as well as a searchable record of
discourse for the purpose of establishing new affiliates (Zappavigna, 2011).
We measure ambient affiliation through the use of hash-tagging.

Topic | EC |  EXT |  Total
---|---|---|---
Election Fraud | 470 | 97,510 | 97,980
Antivax | 18,200 | 393,331 | 411,531
QAnon | 8,934 | 233,166 | 242,100
MeToo | 3,468 | 449,627 | 453,095
BLM | 1,505 | 664,930 | 666,435
Monday Motivation | 3,019 | 339,903 | 342,922
Table 2. Number of users/nodes engaged with each topic (EC=echo chamber;
EXT=external).

##  5\. RQ1: Group identity formation

RQ1 asks whether there are observable disparities in group identity between
echo chambers and external groups within discussions surrounding
misinformation-related topics. We assess this through two approaches:
analyzing explicit signaling of group identity through the use of first and
third-person pronouns; and, exploring implicit signaling of group identity
through ambient affiliation.

###  5.1. Explicit identity signaling through in-/out-group cues

####  5.1.1. Methodology.

We employ the zero-inflated beta regression model to examine the relationship
between a user’s membership to (presence within) an echo chamber and their use
of group pronouns. The zero-inflated beta model is particularly suited for
this analysis due to the nature of the data, which contains many zeros and is
bounded between 0 and 1. We concentrate on the model’s non-zero component
because it remains largely unaffected by extreme outliers, specifically
instances where the content is very short thus not containing target
vocabularies. The coefficient of the predictor variable in this regression,
which represents echo chamber membership, provides an estimate of the log odds
ratio of the change in identity measurement in response to echo chamber
membership. If this coefficient is positive and statistically significant, it
suggests that membership to an echo chamber is associated with higher log odds
of using group pronouns. Recognizing that analyses with large datasets often
yields small, less informative p-values, we employ a stratified random
sampling method (Fox and Weisberg, 2002). This involves selecting 1000 data
points each from echo chamber and non-echo chamber data subsets (2000 total),
ensuring a balanced representation of both categories. To address potential
small sample biases and enhance the robustness of our findings, we conduct
1,000 iterations of this regression analysis, obtaining median values for the
coefficient, standard error, T-score, and p-value. Analyses without sampling
are also provided, for completeness, alongside datasets and code through the
project’s Github page.

####  5.1.2. Findings.

We observe clear, heightened group identity development through the deliberate
utilization of target plural pronouns in echo chambers within US election
fraud and QAnon discussions. See Table 3.

Following is a typical tweet from within a QAnon echo chamber that exemplifies
pronounced group identity. After mentioning 50 names in a chain, the tweet
reads:

> Totally agreed. We Americans chart our own destiny The Individual is THE
> most protected of its rights by the Constitution we need to know this, and
> go forward confidently. DeepState, we will deal w/them in unity w/our
> President. For such a time like this-we have our fighting man!

Likewise, strong group identity cues are evident within election fraud echo
chambers. For instance:

> @realDonaldTrump keep fighting like the warrior you are! We are with you!
> #VoterFraud https://t.co/RuxhsXyO8U

Note that we observe an inverse coefficient for Monday Motivation. We expect
this may be attributed to the inherent characteristics of the challenge,
wherein individuals share personal experiences with their communities (Ridings
et al., 2002).

###  5.2. Implicit identity signaling through ambient affiliation

Figure 1. Top 10 hashtags, by density, in echo chambers (left) and overall (right) for misinformation topics. Category | Topic | Coefficient | Std. Error |  T-score |  p-value
---|---|---|---|---|---
Misinformation | Election Fraud | 0.162 | 0.049 | 3.272 | 0.001**
Antivax | 0.010 | 0.038 | 0.266 | 0.523
QAnon | 0.116 | 0.045 | 2.339 | 0.019*
Hyper-polarized | MeToo | 0.026 | 0.044 | 0.593 | 0.453
BLM | -0.058 | 0.046 | -1.265 | 0.206
Neutral | Monday Motivation | -0.186 | 0.056 | -3.307 | 0.001**
Table 3. Pairwise zero-inflated beta model median statistics for group
identity language using bootstrap sampling. Positive coefficient represents
positive association between echo chamber membership and increased group
identity language.

####  5.2.1. Methodology.

Building on our observations of increased explicit group identity formation
within echo chambers for misinformation-related topics in particular, we
explore differences in ambient affiliation between echo chambers and the
remainder of interaction network during discussions of misinformation-related
topics.

_Top hashtags._ We extract all hashtags present inside the dataset. From
these, we select the 10 with highest density, standardized over the total
number of tweets within each category. We exclude hashtags identical to the
keywords used for data collection.

_Hashtag networks._ We construct undirected common hashtag networks to model
ambient affiliation within echo chambers, for all three topics of
misinformation. In these networks, nodes represent users within echo chambers
and a weighted edge exists between users who share at least one common
hashtag. As before, we exclude hashtags identical to keywords utilized during
data collection.

####  5.2.2. Findings.

A conspicuous pattern emerges, complementing our findings from in-/out-group
identity analyses. Both QAnon and election fraud discussions are characterized
by methodical use of ambient affiliation relative to anti-vaccination dataset.
When conducting a comparison of hashtag usage within echo chambers versus
overall hashtag usage, we observe that echo chambers have notably higher rate
of ambient affiliation. This suggests members may be rallying around common
hashtags to amplify their message and consolidate their identity.

_Top hashtags._ It is clear that the strategic use of hashtags plays an
important role, both within and outside echo chambers, in QAnon-related
conversations (see Figure 1). Several hashtags speak directly to group
membership, such as #Mighty200 (a group of conservative or right-wing
influencers) and #wearethenewsnow (QAnon supporters use it to suggest that
mainstream media cannot be trusted, positioning the QAnon community as the
authentic purveyors of news). Some hashtags are slogan-like, e.g., #WWG1WGA
(“Where We Go One, We Go All”) and #kag (“Keep America Great”).

_Hashtag networks._ Hashtag network statistics are provided in Table 4. In the
QAnon echo chamber, the hashtag network exhibits the highest density,
suggesting a deliberate and strategic approach to hashtag bulk use, likely for
content promotion. Additionally, we see high percentage of ambient affiliation
engagement and average common hashtags per paired users for election fraud,
which is consistent with the high identity consolidation observed within
election fraud discussions. Conversely, we observe limited hashtag usage
within anti-vaccination discourse, which is also mirrored in the network
structure. These findings highlight the complex nature of online discourse and
nuance under the very broad umbrella of misinformation-related conversations.

###  5.3. Case Study 1. Echo chambers in conversations about US election fraud

We observe strongest group identity formation within US election fraud
discourse. To further explore this, we conduct a case study analyzing
interactions within, between, and outside echo chambers. We find that echo
chamber members are deeply embedded in the broader interaction network,
maintaining active engagements with individuals outside their group. This
heightened activity of echo chamber members in external interactions
challenges the simplistic view of echo chambers as entirely insular entities
and suggests a more nuanced understanding of discourse dynamics.

Figure 2 shows the internal interaction network amongst members of the largest
echo chamber (red) and other echo chamber members with which they interact. We
observe echo chambers are interconnected rather than isolated. Two nodes, in
particular, exhibit highest centrality: Node ECa, a member of the largest echo
chamber; and, ECb, a bridge between echo chambers. Deeper examination of the
content shared by these two nodes reveals distinct roles. Node ECa emerges as
a pivotal resource hub, disseminating information about the election status,
often accompanied by a URL. These URLs typically redirect to tweets conveying
similar content, supplemented by relevant hashtags.

Furthermore, we notice several indicators of group identity from ECa that
extend beyond our initial examination of first and third-person plural
pronouns. This content manifests as expressions of favoritism and support
within the group or, conversely, as extreme animosity towards those outside
the group. For instance:

> Good morning @realDonaldTrump. I’m still here, standing with you and 70
> million other freedom loving patriots. It time to expose the #voterfraud.
> #DoNotConcede #DoNotConcedeUnderAnyCircumstances #DoNotGiveUp
>
> GDMRNNG (The vowels have been stolen by Democrats) #Election2020 #VoterFraud
> #BidenHarris2020 #2020Elections (Explanation: GDMRNNG is goodmorning without
> vowels.)

We observe that ECb mainly interacts with itself. Among 111 tweets posted by
ECb, 93 are retweets or replies to its own tweets. Within this set of tweets,
we notice: heavy use of user mentioning, an average of 46.32 mentions per
tweet; use of hashtags, an average of 5.7 hashtags per tweet; and ending the
post with a URL.

Figure 2. US election fraud. Internal interaction network amongst members of the largest echo chamber (red) and other echo chamber members with which they interact. Topic |  hashtag Network Density |  Ambient Affiliation Engagement |  Average Common Hashtags per Paired Users
---|---|---|---
Election Fraud | 0.0721 | 52.34% (246/470) | 1.2327
Anti-vax | 0.0716 | 8.37% (1523/18200) | 1.0310
QAnon | 0.0949 | 33.15% (2962/8934) | 1.2123
Table 4. Hashtag network statistics for three misinformation topics.

##  6\. RQ2: Processing fluency effects

We explore the impact of processing fluency on discourse around
misinformation-related topics, with particular focus on the variance between
echo chambers and external members. This study is not intended to compare the
overall simplicity or elaboration between misinformation and control topics,
but to identify differences in processing fluency between discussions within
echo chambers and those outside of them. We utilize a series of linguistic
metrics to measure processing fluency, complemented by an analysis of URLs, to
understand these dynamics in detail.

###  6.1. Processing fluency through linguistic simplicity

####  6.1.1. Methodology

Simplicity is captured through readability and word length measurements.

_Readability._ As discussed in Preliminaries, we utilize the Flesch
readability ease score. It considers not just word-level but also sentence-
level complexity. Text processing follows as above, retaining all punctuation
marks. Calculation of the Flesch score requires text at least 100 words, so
our approach deviated from standard data-point level comparison as follows. To
address potential sampling bias, we employed a stratified sampling method
wherein we selected 100 posts each from both the echo chamber and external
post datasets and aggregated them for readability score calculation. This was
repeated for 100 iterations. Given that the datasets exhibited a near-normal
distribution, as depicted in Figure 3, we utilize the T-test for analyzing
mean differences. However, we observe non-uniform variance in certain
instances, such as with the BLM topic. To reinforce the validity of our
comparisons under these conditions, we additionally applied the non-parametric
Mann-Whitney U test. Notably, the results from both testing methods converged.

_Big words._ We employ LIWC to calculate the “bigword” score for each tweet.
To avoid biases, we apply this analysis to cleaned texts, excluding hashtags,
mentions, URLs, and similar elements that could be erroneously identified as
“big words.” Given that resulting scores are no longer bounded between 0 and
1, the zero-inflated beta model proves unsuitable for our comparative tasks.
As the datasets being compared exhibit approximately normal distribution and
similar variances, we employ an independent two-sample T-test. Given our
relatively large dataset, this test tends to yield very small p-values, hence
less informative. To mitigate this effect or potential non-normality in
distribution, we perform bootstrapping by randomly sampling 1000 data points
from both the echo chamber and non-echo chamber groups and conducting the
t-test over 1000 iterations (Lumley et al., 2002; Ahad et al., 2012; Faber and
Fonseca, 2014). We select the median as the representative outcome.

####  6.1.2. Findings

Our findings demonstrate clearly and consistently that simplicity, as a
measure of processing fluency, is amplified within echo chambers in
misinformation-related discussions.

_Readability Ease._ Outcomes are visualized in Figure 3. Content within echo
chambers consistently demonstrates higher readability ease across all three
misinformation topics when compared to external content. Conversely, in the
cases of Metoo and Monday Motivation, a contrasting pattern emerges,
indicating a higher level of complexity within echo chambers. No significant
differences were observed in discussions surrounding BLM. Here, we emphasize
the contrast between echo chambers and external members rather than making
broad comparisons of readability ease between misinformation and control
topics. The apparent lower overall readability ease of misinformation topics
is a reflection of the topics’ inherent characteristics, e.g., the complexity
of these discussions or the use of specialized vocabulary.

_Big words._ The results of all independent two-sample T-tests are presented
in Table 5. We also report the percentage of iterations having mean EC smaller
than mean EXT and the percentage of p-value$<$0.05 in the table. Our findings
consistently indicate that all misinformation topics yield a lower average
percentage of big words in echo chamber posts compared to non-echo chamber
posts, with all p-values less than 0.05. This suggests that echo chambers
exhibit less linguistic complexity, possibly due to the development of a
shared knowledge base within their community.

Figure 3. Readability ease score box plot for 100 samples of aggregated text. ***p¡0.001, +significance in the positive direction, -significance in the negative direction.  Category | Topic | Avg.EC | Avg. EXT |  T-score |  p-value |  % Avg.EC $<$Avg.EXT |  %p-value $<$0.05
---|---|---|---|---|---|---|---
Misinformation | Election Fraud | 22.973 | 26.638 | -5.440 | 5.993e-8*** | 100% | 99.8%
Antivax | 25.895 | 26.638 | -2.630 | 0.009* | 99.9% | 75.0%
QAnon | 22.044 | 23.731 | -2.692 | 0.007* | 99.9% | 75.0%
Hyper-polarized | MeToo | 19.520 | 19.043 | 0.854 | 0.351 | 19.6% | 12.4%
BLM | 18.612 | 19.482 | -1.156 | 0.120 | 95.6% | 34.6%
Neutral |  Monday Motivation | 24.133 | 19.767 | 7.239 | 6.412e-13*** | 0.0% | 94.7%
Table 5. Independent two-sample T-test median statistics for big words after
1000 iterations of bootstrap sampling. A significant p-value, coupled with a
positive T score, indicates that echo chambers exhibit a greater utilization
of big words.

###  6.2. Processing fluency through elaboration

####  6.2.1. Methodology

Elaboration is captured through the use of discourse connectives. Similar to
our analysis of in-/out-group language, we employ a zero-inflated beta
regression model with bootstrap sampling to examine the relationship between
echo chamber membership and the use of discourse connectives.

####  6.2.2. Findings

Median statistics are shown in Table 6. We observe that US election fraud
discourse shows a positive relationship between echo chamber membership and
additive and negative connectives. QAnon exhibits a positive significant
coefficient for logical and additive connectives. Conversely, in the context
of the control topics Monday Motivation and BLM, an entirely opposite behavior
is observed; echo chambers tend to exhibit a lower prevalence of discourse
connectives, aligning with our findings above that suggest greater simplicity
in these communities.

Generally speaking, we might expect fewer discourse connectives in echo
chambers due to greater preexisting consensus amongst members. However, the
nature of some misinformation-related topics requires more elaboration and
explanation, and might explain the increased use of connectives in these
contexts.

Category | Topic | Linguistic metrics | Coefficient | Std. Error |  T-score |  p-value
---|---|---|---|---|---|---
Misinformation | Election Fraud | Logical | 0.066 | 0.046 | 1.435 | 0.149
Additive | 0.171 | 0.043 | 4.040 | 5.600e-5***
Negative | 0.198 | 0.072 | 2.721 | 0.007*
Antivax | Logical | -0.043 | 0.032 | -1.340 | 0.180
Additive | 0.014 | 0.033 | 0.411 | 0.513
Negative | -0.046 | 0.040 | -1.148 | 0.248
QAnon | Logical | 0.083 | 0.042 | 1.999 | 0.046*
Additive | 0.106 | 0.040 | 2.646 | 0.008*
Negative | 0.094 | 0.066 | 1.427 | 0.154
Hyper-polarized | MeToo | Logical | -0.021 | 0.035 | -0.592 | 0.453
Additive | 0.044 | 0.048 | 0.915 | 0.340
Negative | 0.015 | 0.048 | 0.305 | 0.512
BLM | Logical | -0.126 | 0.041 | -3.060 | 0.002**
Additive | -0.085 | 0.038 | -2.264 | 0.024*
Negative | -0.118 | 0.059 | -1.997 | 0.046*
Neutral | Monday Motivation | Logical | -0.219 | 0.040 | -5.420 | 0.000***
Additive | -0.104 | 0.035 | -2.960 | 0.003**
Negative | -0.211 | 0.066 | -3.182 | 0.001**
Table 6. Pairwise zero-inflated beta model median statistics of discourse
connectives after bootstrap sampling(positive coefficient represents a
positive association between membership in echo chambers and increased use of
discourse connectives).

In line with prior work on QAnon (Marwick and Partin, 2022), we also note cues
of explanation-oriented behavior within QAnon echo chambers, co-occuring with
straightforward vocabulary. QAnon proponents often use phrases like “do the
research.” These phrases appear to steer users toward an archive of QAnon-
related information, likely intended for persuasive or reinforcing purposes.
For instance:

> There are many sides to any issue. It doesnt́ matter what others think. What
> matters is what you think. Do the research. Dig. Come to your own
> conclusion. Seek the truth knowing that you will never know what TRUTH
> really is, but come as close as you possibly can. #QAnon
> https://t.co/W68boAmWW3
>
> People no longer have the luxury of claiming ignorance is why they believe
> the things they believe. We are living in a time where the truth can be
> easily accessed via documentation, Youtube, QAnon and even if you don’t want
> to do the research their are many who do and give.

A pattern in many such tweets is the inclusion of URLs, guiding users to
external sources, e.g., news articles, videos, or other tweets to “validate”
claims. While the strategic use of archival information may vary by topic, it
complements the linguistic characteristics we have identified and can be an
asset for debunking misinformation.

###  6.3. Case Study 2: URL usage in discussions of misinformation

Including URLs or referencing news sources can appear to enhance the
credibility of information presented online without the need for extensive
explanation. We explore whether echo chamber members exhibit increased URL
usage, as compared to external nodes. To examine this, we assess URL density
in interactions within echo chambers, outside echo chambers, and in standalone
original tweets across our three misinformation topics, as shown in Figure 4.
Notably, we observe a consistently higher URL density in isolated posts across
all misinformation-related topics. Conversely, echo chamber interactions tend
to utilize URLs less frequently, perhaps suggesting that users within echo
chambers may not feel the need to persuade each other with new information,
given their synchronized beliefs.

In between-topic comparisons, QAnon discussions have most frequent URL usage,
followed by election fraud. In contrast, discussions about anti-vaccination do
not engage as much with URLs. Among QAnon tweets, 73.68% contains at least one
URL, in comparison to Election Fraud (69.81%) and Antivax (44.45%). This trend
is accentuated when considering tweets with at least two URLs. Within QAnon
discourse, a substantial 10.82% of tweets fall into this category, as compared
to 3.51% for US election fraud and 3.91% for anti-vaccination. Extensive use
of URLs in QAnon discussions suggests a deliberate strategy to strengthen main
assertions with multiple references.

Figure 4. Heatmap of URL density of echo chamber interactions, non-echo
chamber interactions (excluding isolates), and isolated tweets for
misinformation topics

##  7\. Discussion and Conclusion

Our work is motivated by longstanding theories from the socio-psychologial and
socio-linguistics literatures that describe phenomena like collective
belonging through social identity and the importance of familiarity and
fluency in information credibility. We operationalize these theories through a
suite of computational metrics and analyses, which let us test these theories
in the wild through social media with particular focus on discussions around
misinformation-related topics. This approach also allows us to differentially
explore echo chambers and understand their linguistic underpinnings.

We do not see uniform behavior across all misinformation topics. Perhaps
surprisingly, anti-vaccination discourse shows distinct linguistic patterns
from US election fraud and QAnon discussions. This may signify the
differential impact of _political_ group identities, in particular, on the
linguistic characteristics of politically-charged topics. We also note that
the neutral topic we selected (#mondaymotivation) consistently displays
behaviors contrary to those of misinformation. This underscores that echo
chamber dynamics are not uniform but are influenced by discourse.

These insights might enable identification of social communities prone to
influence through anomalous linguistic signs. Moreover, understanding the
anatomy of echo chambers offers a blueprint for designing more effective and
targeted moderation strategies. By studying the linguistic underpinning of
social communities around misinformation-related discussions, we offer a fresh
perspective on dynamics of information dissemination in the digital age.

##  8\. Limitations and Future Work

Our methodology necessitates the selection of a data collection time frame
coincident with peak popularity of the respective topics. Given our
intentional control over the total volume of tweets, it is an intrinsic
outcome that different topics span different lengths of time. Future work
should consider controlling for temporal variables to better understand the
influence of time on our findings.

Additionally, here we employed a relatively strict, network structure-based
definition of echo chambers using strongly connected components within topic-
specific discourse. It is possible that other approaches to defining echo
chambers would yield different insights.

Finally, our study selected the zero-inflated beta model to analyze the
relationship between echo chamber membership and linguistic patterns as it is
suitable for the data distribution. The extensive size of our dataset results
in the models being hyper-sensitive, detecting even the slightest deviations
in values. This heightened sensitivity is not unique to this model but is
observed in other tests we employed, such as permutation tests. As a remedy,
we adopted bootstrapping across 1,000 iterations for all tests, using the
median as a representative measure. Nevertheless, it is the direction and
magnitude of the coefficients that offer deeper insights into the interplay
between echo chamber dynamics and linguistic underpinnings in discussions.

##  9\. Ethical Considerations

This study primarily involves the comprehensive analysis of data collected
from social media users on a large scale. The data was obtained from the
Twitter Developer API is solely utilized for the purposes of this research. We
ensure that the analyses conducted do not display any identifiable information
of users. Tweet IDs are shared with the public for reproducibility of the
analyses in this work. Our findings are expected to support ongoing
conversations within data ethics about misinformation and polarization on
social media, and potential interventions.

###### Acknowledgements.

This work was partially supported by NSF award #2318460.

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