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Article

Patterned Radicalization: User Behavior Analysis and Antisemitic Language in QAnon Subreddits

by
Noah D. Cohen
1,*,
Peter Antonaros
2,
Dana B. Weinberg
3,*,
Meyer Levy
3 and
Jeffrey S. Kopstein
4
1
Department of Criminal Justice, John Jay College of Criminal Justice, New York, NY 10019, USA
2
Department of Applied Physics & Applied Mathematics, Columbia University, New York, NY 10027, USA
3
Department of Sociology, Queens College—CUNY, Flushing, NY 11367, USA
4
Department of Political Science, University of California, Irvine, CA 92697, USA
*
Authors to whom correspondence should be addressed.
Information 2026, 17(2), 179; https://doi.org/10.3390/info17020179
Submission received: 1 January 2026 / Revised: 30 January 2026 / Accepted: 2 February 2026 / Published: 10 February 2026
(This article belongs to the Special Issue Semantic Networks for Social Media and Policy Insights)
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Abstract

This study investigates how people in certain online communities engage with and adopt hateful rhetoric, specifically examining the escalation of antisemitic language in two deplatformed QAnon-related subreddits. Utilizing a lexicon of implicit and explicit antisemitic terms, the research analyzes over 1.26 million Reddit posts and comments. This study’s objective is to describe the process through which users become more deeply engaged with antisemitic content within hate-filled subcultures found online. Through the application of survival models, chi-square tests, and logistic regressions, the findings reveal that most users do not begin their engagement using antisemitic language and instead engage with antisemitic content before posting it on their own. Moreover, users who transition to posting antisemitic language typically commence with posts containing implicit terms, progressively transitioning from implicit to explicit hate speech. This escalation is particularly pronounced among users demonstrating higher levels of engagement and those interacting with influential community members, often referred to as hyper-posters. The results indicate increased involvement, exposure, and interaction with antisemitic language, especially with hyper-posters, significantly predict increased and more extreme antisemitic language. These findings illuminate the dynamic and socially contingent process through which users engage with and internalize antisemitic language within subcultural digital spaces. The study posits that language radicalization is a structured process shaped by both individual user behavior and the broader community’s social architecture.

Graphical Abstract

1. Introduction

Online forums, particularly those with antisocial or conspiratorial leanings, have become fertile ground for the pervasive spread and normalization of prejudiced language [1,2]. While empirical understanding of the dynamics and language used on these forums is growing [3,4,5,6], the reasons and causes by which hateful rhetoric escalates have yet to receive sufficient attention.
In response to moderation efforts and social stigma against overt hate speech, online communities have strategically shifted towards the use of implicit language, commonly referred to as “dog whistles” [2,7,8]. This form of coded communication is designed to convey specific messages to a targeted in-group possessing specialized knowledge, while keeping other groups, such as platform moderators or external observers, ignorant of the underlying hateful meaning [9]. This strategic adaptation is largely attributable to the risk of deplatforming and content removal by social media platforms and forums aiming to minimize harmful language and misinformation [10,11,12].
While implicit representations may seem innocuous, particularly in comparison to explicit ones which are readily identified as potential hate speech, they nonetheless represent a potent vehicle for spreading and promoting hate in encoded ways. This dynamic interplay between platform moderation and user adaptation highlights an ongoing “arms race”: when platforms suppress explicit hate, radicalized communities bent on hate innovate through the use of implicit and coded language or change platforms [13,14,15]. This dynamic makes the detection and countering of hate speech significantly more challenging, as the meaning is deliberately concealed from outsiders, allowing communication within the in-group while retaining both obscurity and plausible deniability. While the use of implicit language may not seem particularly significant, its use contributes to a normalization of hateful rhetoric, making the escalation of expressions to explicit hate speech and overt aggression more likely. With empirical studies supporting the notion that hate speech serves as a precursor to physical harm [16], and that hateful and radical language can serve as a significant component in mass violence through the normalization of stereotypes, dehumanization, and alienation [17,18,19,20,21], the need to further understand language escalation becomes salient.
Semantic and rhetorical analysis approaches offer a sturdy framework for understanding how collective identity and subcultural belonging emerge through patterned language use in online communities. By modeling meaning as networks of co-occurring terms rather than isolated words, semantic analysis captures how shared vocabularies, symbols, and associations stabilize in-group boundaries and encode collective worldviews [2,22]. Applied studies show that these networks reveal how individuals align themselves with group identities by adopting distinctive rhetorical repertoires, whether in political movements [23] or oppositional online communities [24]. Work on subcultural jargon further demonstrates that specialized vocabularies function as signals of membership, with dense semantic clusters marking insider status and reinforcing ideological cohesion [25]. Together, this literature underscores how semantic networks are especially well suited to identifying an individual’s adoption of subcultural rhetoric, their status or position within a group, and the ways language use both reflects and reproduces collective identity in online social groups.
In this paper, we describe the process through which users in an online community become actively engaged with hate speech, mapping the semantic progression from responding to posts containing hateful rhetoric to posting and spreading it themselves. Key to this process is participation in the escalation of rhetoric from coded or implicit to overt or explicit, both in responses to posts with such content and in creation of them.

2. Implicit Language and Antisemitism

2.1. Previous Work on Implicit Language

The strategic use of language is deeply embedded within what has been conceptualized as the “language game” in online subcultures [2,8]. Drawing from Ludwig Wittgenstein’s communitarian theory of language use, this framework posits that through inscription in language games, social agents acquire their subjectivity and form specific beliefs and desires, often driven by emotional engagement and sensational intergroup sentiment rather than purely rational argumentation [26,27]. Users may adopt hateful or radicalized language not solely due to a full internalization of the ideology, but also to gain social acceptance, status, or to perform allegiance within the group.
Within anonymous online communities like 4chan, users seek to solidify their subcultural membership and identity through memes and slang to carry nuanced meanings across various contexts [8,28,29,30,31,32,33]. The meaning of a meme or word is understood as a function of its use; thus, when adopted by mainstream and active users, subcultural participants may alter or abandon use of these signals to preserve their distinct identity, remain unseen by content moderators in online websites or forums, or to manifest social opposition or criticism [8,34,35]. This evolution of meaning would be notable when comparing the impact of submissions (top-line posts) when compared to comments (response posts), as submissions would indicate more significant contribution to the topic and lexicon while comments would build from the directive posts. Simultaneously, involvement through comments would indicate social buy-in to narratives, rhetoric, and culture proliferated within the forums, indicating involvement within the conversation and belief system.
This dynamic involves a struggle for “linguistic capital,” where mastery of in-group slang manifests as status and authority [33,36,37,38,39]. Community dynamics further shape this linguistic environment and can accelerate radicalization within online subcultures. The presence of highly active and influential users, often termed “hyper-posters,” is particularly significant [3,40]. These hyper-posters typically dominate forums, constituting a disproportionately large share of active participation, creation, and dissemination of topics and discussions [41], leading the conversation within the forums when compared to the activity of less active participants.
This trend of central nodes producing a majority of posts has been observed in various extremist groups, including Islamic extremist groups [42,43] and political extremist forums in the United States [4,6,44,45] and Europe [46]. A common thread across these studies is that hyper-posters not only contribute a disproportional number of posts but also serve as mouthpieces for some of the more extreme and persuasive rhetoric within their networks. By becoming central figures, they act as banner-bearers of polarizing topics, potentially enticing new lurkers to delve deeper or further radicalizing already involved individuals. Moreover, when a small number of highly radicalized individuals produce most posts, those with less radical views may unconsciously perceive the group’s majority as conforming to extreme language [47,48]. This perception may promote peer pressure, conformity, and an illusion of agreement with the majority in echo chambers.
The frequency of a user’s posting behavior is a robust indicator of their immersion and identification with the community [49,50,51]. Increased posting frequency may provide users with more consistent exposure to the community’s evolving norms and lexicon, thereby accelerating their understanding of the language game and increasing the likelihood of adopting more extreme forms of expression, including explicit hate speech. Additionally, individuals may utilize the hostile, group-dependent lexicon in order to gain social capital and recognition within this subgroup [52], likely decreasing inhibition and contributing to future utilization of hateful rhetoric. This heightened engagement signifies a deeper socialization within the radicalizing environment, where striving for attention through linguistic capital [33,36,40,53] and ritual opposition [37,39] can further entrench users in the community’s discourse and involvement.
Significant gaps remain in our understanding of how individuals enter the language game and escalate to the use of radicalized or explicitly hateful language. Much of the existing literature primarily focuses on content classification [4,5,54], platform moderation strategies [10,11,12,13,15], or recruitment mechanisms [44,55,56], with less attention paid to the rhetorical trajectories at the user level. While some studies have examined posting behavior in extremist forums [4,6], they have not fully accounted for coded language or how language shifts in verbiage over time. The precise mechanisms and triggers for users shifting from neutral to coded or implicit speech to explicit hate speech remain largely unexplored.

2.2. The Implicit Language Game and Antisemitism

The strategic use of implicit language can serve to facilitate community by marking in-group belonging through the use of dog whistles as a sort of internal code [57,58]. The rhetorical co-occurrence of explicit and implicit antisemitic language effectively encodes the implicit lexicon for the user’s understanding [2]. This encoding indicates a sophisticated process of linguistic socialization where implicit language functions not merely as a workaround for moderation but also as a means of training new users, gradually acclimating them to the full spectrum of the community’s hateful discourse. The consistent co-occurrence of implicit and explicit terms within posts and comments ensures that the nuanced meanings of implicit dog whistles are transmitted and understood by community members, reinforcing in-group identification while maintaining protection from social stigma.
Antisemitism often operates as a process of out-group formation grounded in perceptions of Jewish people as a powerful, conspiratorial “other.” Kofta and colleagues [59] show that feelings of political uncontrollability and uncertainty heighten the appeal of conspiracy narratives, positioning Jews as an omnipotent out-group responsible for social instability. Similarly, Bilewicz and colleagues [60] find that antisemitic beliefs cohere into a system of harmful stereotypes that cast Jews as morally deviant and collectively threatening, reinforcing rigid in-group boundaries. Online studies by Allington [61], Schwarz-Friesel [62], and Zannettou and colleagues [63] reveal how digital platforms amplify this boundary-making, transforming antisemitic tropes into networked myths that unify in-group identities through shared hostility. Extending this, Allington and colleagues [64] demonstrate that antisemitism correlates with authoritarian aggression and conspiratorial worldviews, suggesting that hostility toward Jews serves as a psychological mechanism for simplifying complex hierarchies and reaffirming in-group cohesion amid uncertainty.
Operationally, implicit and explicit language relate specifically to antisemitic rhetoric based upon distinct characterizations and functions within online discourse. Explicit language refers to direct terms categorized overtly associated with targeted rhetoric [2]. In contrast, implicit language, or “dog whistles,” comprises terms closely linked to antisemitic stereotypes and conspiracy theories that do not overtly mention the Jewish people. The primary function of implicit language is to enable communication within the in-group while maintaining plausible deniability from moderators and external authorities, thereby circumventing censorship and social stigma [7].

2.3. QAnon and Implicit Language

QAnon offers a compelling case study for examining the escalation of antisemitic language within online subcultures due to its conspiratorial framework and documented links to real-world violence [65,66]. Often described as a “big tent” conspiracy theory, QAnon incorporates elements from a swath of earlier narratives and conspiracies, enabling it to encompass a wide variety of claims and attract diverse adherents [65,66,67]. Despite numerous platform disruptions, the movement has proven resilient, migrating from 4chan to 8chan and then 8kun, and persisting even after Q drops ceased in December 2020 [65]. By early 2018, QAnon communities numbered in the tens of thousands, expanding to an estimated 4.5 million accounts on Facebook and Instagram by 2020, with networks identified in over 25 countries [65]. The FBI labeled QAnon a domestic terrorism threat in 2019, citing incidents of QAnon-inspired violence [65], and its adherents played a central role in the 6 January 2021, attack on the U.S. Capitol [66,67]. The Combating Terrorism Center at West Point further described QAnon as a “public security threat” with potential to escalate into a more impactful domestic terror movement, connecting followers to cases of assault, terrorist threats, and murder, while framing it as part of broader patterns of mistrust and radicalization [68]. Importantly, QAnon’s focus on an ill-defined enemy fosters “nebulous othering” [8,67], providing fertile ground for the implicit integration of antisemitic tropes, as Jewish people have historically been cast as the ultimate target at the core of global conspiracy [8,69].
QAnon subreddits have been found to contain significant antisemitic content, making them highly relevant for studying the escalation of antisemitic language. Weinberg and colleagues [2] examined the interplay between explicit and implicit antisemitic rhetoric within these forums, finding that implicit language was far more prevalent than explicit, with less than 0.7% of the corpus containing explicit antisemitic content compared to 8.6% containing implicit references. Nearly 28% of users employed implicit language without ever using explicit terms, while only a small minority (6.84%) used explicit rhetoric. Importantly, 85.4% of semantic co-occurrences between implicit and explicit language within posts or comments contained an antisemitic dimension, often through narratives intertwined with antisemitic conspiracies, illustrating a direct connection between the two forms of rhetoric. The authors argued that this co-occurrence functions to communicate the meaning of terms to users, demonstrating that QAnon communities operate not as passive repositories of hate but as active incubators for antisemitic beliefs through narrative framing and encoding of conspiratorial ideology. By embedding implicit antisemitism in a narratively appropriate conspiratorial framework, these communities normalize and perpetuate antisemitic tropes, effectively training new members in the grammar of antisemitism in ways that are insidious and difficult to disrupt from the outside.
What is the process through which new members engage with and ultimately begin participating actively in the language game? This study goes beyond a study of rhetorical content to examine individual user behavior in relation to such content on QAnon-affiliated subreddits. Its primary objective is to track how individual users’ language escalates over time, specifically assessing the impact of engagement and social exposure on these transformations. The research aims to understand the process through which social media users become increasingly engaged with antisemitic rhetoric and how patterns of involvement and the adoption of hate speech manifest. By focusing on user-level trajectories and the process through which social media users become increasingly engaged, the study moves beyond a static view of hate speech content to a dynamic model of the radicalization process.

3. Hypotheses

We posit a structured, multi-stage model of language escalation within online subcultures, influenced by individual engagement and community dynamics. The following hypotheses detail the expected relationships between user behavior, language type (implicit/explicit), and posting patterns, grounded in the theoretical framework of language games and observed patterns of online radicalization.
The general unacceptability and social stigma associated with explicit antisemitic rhetoric suggest that new users may initially gravitate towards less overt forms of hate speech. Implicit language provides a potentially more accessible entry point into the community’s language game [2]. Use of implicit rather than explicit language enables new participants to engage in the community’s specific lexicon and connect with the in-group identity without immediately risking exposure to social backlash. Similarly, we anticipate users will demonstrate an initial preference to engage with implicit content, reflecting a strategic adaptation to the online environment, where responding coded content carries less risk of social stigma or censure than responding to explicit content.
H1. 
New users (in the first month of participating in the forum) who engage with antisemitic content are more likely to engage with implicit content, either by posting responses to implicit submissions or to implicit comments, than with explicit content.
As previous research has shown, increased frequency of posting and social involvement directly correlates with greater immersion and identification with the online community as active involvement shapes positive perceptions of the in-group while vilifying opposition [70,71]. This heightened engagement provides users with more consistent and intensive exposure to the community’s evolving norms, shared lexicon, and the nuances of the language game. High posting frequency is thus a strong indicator of deeper socialization within the radicalizing environment. We therefore anticipate that high posting frequency increases the likelihood of users adopting more extreme forms of expression, including explicit hate speech.
H2. 
More frequent posters—those more engaged with/self-identified with the group—are more likely than others to use explicit language in their posts.
Engagement with explicit antisemitic content through commenting may represent a key escalation within extremist subcultural forums that reduces inhibition and validates perceptions of acceptable discourse. This initial alignment with overtly hateful language is expected to facilitate a deeper desensitization and integration into the forum’s radicalized language system and ideology. As a result, we anticipate that users who comment on posts explicit content are more likely to shift from reactive engagement to proactive participation. By contributing to other users’ explicit submissions, users will be more likely to escalate their involvement within the forum to the posting of original submissions containing explicit antisemitic language.
H3A. 
Users who engage with explicit content by commenting on explicit submissions or comments are more likely to post explicit submissions themselves.
The contribution towards explicit antisemitic conversation by posting explicit content indicates increased involvement and identification with the beliefs proliferated within the subcultural forum. Active participation of the highest degree of the subcultural lexicon, even when building on the conversation initiated by another user, indicates significant buy-in into the collective identity for individuals and thus an increased willingness to utilize subcultural, antisocial rhetoric. Therefore, we anticipate that users will shift from contributing to other authors’ explicit submissions with explicit comments, to engaging with their own explicit submissions. This transition would showcase active belief and initiative with explicit antisemitism in a forum with which they are familiar with and involved.
H3B. 
Users who post explicit comments are more likely to post explicit submissions.
Hyper-posters, determined by the disproportionate amount of content generated, are recognized as dominant and highly influential users who frequently set the tone and push ideological boundaries within online communities [3]. Within subcultural forums, the content produced by these actors typically operates and extends the forum’s subcultural language system, normalizing antisocial and exclusionary ideology through both coded and overt forms of expression. Therefore, engagement with hyper-posters’ content signals buy-in within the forum’s hateful core ideology. As users respond to these influential figures, they are increasingly drawn into the subcultural language game, which lowers normative barriers to reproducing both implicit and explicit antisemitic content. Accordingly, we hypothesize that responding to hyper-posters increases a user’s likelihood of engaging with both implicit and explicit antisemitic language.
H4A. 
A user who responds to hyper-posters (top 1% of users) are more likely to engage with both implicit and explicit antisemitic content.
Hyper-posters’ direct engagement with another user’s content, such as through replies or comments, may serve as a powerful form of validation or implicit instruction just as the reinforcement of outrage can lead to the normalization and increased use of outrage filled rhetoric [72]. This interaction may signal approval for certain types of rhetoric and encourage the recipient user to escalate their own language to align with the community’s radicalized norms as they receive attention from key figureheads. Therefore, we anticipate a direct social influence mechanism, where the endorsement from influential community members reinforces and accelerates a user’s progression into both implicit and explicit antisemitic language.
H4B. 
Hyper-posters (top 1% of users), by responding to posts published by a given user, will increase the likelihood that the user posts implicit and explicit antisemitic content.

4. Methods

4.1. Data

This study analyzes content from two QAnon-related subreddits: r/greatawakening and r/CBTS_Stream. These subreddits were selected due to their known association with antisemitic content and their status as deplatformed communities, providing a unique environment to study the evolution of hate speech under moderation pressure [2]. r/CBTS_Stream was established in December 2017 and subsequently removed in March 2018, while r/greatawakening was created in March 2018 and deplatformed in September 2018. The fact that these subreddits were deplatformed highlights the severity of their content and the challenges inherent in content moderation. This context is crucial because it suggests that the language game and the strategic use of implicit language were likely intensified within these communities due to the constant threat of platform removal, making them particularly valuable for observing adaptive hate speech strategies.
The data for this study, encompassing both submission and comment posts, was obtained through the Pushshift API. All data collection procedures adhered strictly to Reddit’s terms of service. The research protocol received approval from the Institutional Review Board (IRB) at Queens College-CUNY, which classified the study as exempt from full review under human subject research guidelines.

4.2. Sample

The Reddit dataset used in this study comprises over 1.26 million posts made between December 2017 and September 2018. The corpus includes two primary types of user-generated content: submissions, which are original posts initiating discussion threads, and comments, which are responses within these threads. Comments may directly reply to the original submission or to other comments within the same thread.
The dataset initially comprised 128,269 submissions and 1,132,998 comments. Following data cleaning and preprocessing, the final dataset contained 111,036 submissions and 1,048,706 comments from 34,484 users, meaning individual accounts actively posting on the subreddit. Up and down votes to submissions and comments were not collected. It is important to note that data for comments posted in March 2018 were unavailable via the Pushshift API. While this missing month is not substantial enough to impact the study’s overall findings about patterns of escalation, we may have missed transitionary posts that occurred during this time and under-reported the number of users posting implicit or explicit comments. Table 1 provides a general overview of the data used for the study.
Alongside the textual content, the dataset includes pertinent metadata such as timestamps, submission titles, available user identifiers, and linkage data that illustrate the hierarchical relationships between comments and their parent posts or other comments. The substantial numerical difference between comments and submissions, with comments outnumbering submissions by approximately a 9:1 ratio. This difference indicates that, while submissions initiate topics, the bulk of user interaction and potential radicalization occurs within the comment sections. These subreddits were not simply bulletin boards of lone posters, but interactive communities. This observation reinforces the importance of analyzing user engagement, particularly commenting on others’ posts, as a primary pathway for language escalation, rather than solely focusing on original content creation.

4.3. Defining Implicit and Explicit Language

We utilized the methodology described by Weinberg and colleagues [2] to identify and classify antisemitic speech within the corpus as explicit or implicit language.
Explicit Language: Explicit antisemitic language was defined as direct terms categorized as antisemitic or additional language overtly associated with antisemitic rhetoric. The primary resource for identifying explicit terms was the hate speech lexicon developed by Siegel et al. [1], which synthesizes content from multiple well-established hate speech resources, including Hatebase [73] and the Racial Slur Database [74]. This lexicon also incorporates terms from the Anti-Defamation League’s collection of white nationalist slogans, symbols, and terminology [75]. The analysis specifically focused on the “Anti-Semitic” category within this lexicon. To enhance coverage and ensure comprehensiveness, the dictionary was expanded to include both singular and plural variations of terms not originally present. Additionally, we supplement the hate-speech dictionary with the explicit terms contained in the expert-generated list developed by Weinberg et al. [2]. This supplemental list included direct references to Jewish people, Judaism, and Zionism, as well as variations and alternative spellings of known slurs or terms used in overt antisemitic contexts.
Implicit Language: The expert-generated list from Weinberg et al. [2] also includes implicit antisemitic language, emphasizing terms linked to antisemitic stereotypes and conspiracy theories that do not overtly mention Jewish people. The expert-generated list for implicit terms included:
•
Historically and Politically Loaded Names: Such as “Soros,” “Bloomberg,” and “Rothschild,” along with their various misspellings. The “triple parentheses” meme, for example, often-encapsulated names like “(((Soros)))” or “(((Lazarus)))” [8].
•
Indirect Terms: Including “elite,” “globalist,” and “cosmopolitan.”
•
Widely Recognized Conspiracy Expressions: Such as “New World Order,” “Deep State,” and “Great Replacement.”
•
Holocaust-Related References: Including names of concentration camps, references to Nazis, and Holocaust-denialist terms like “holohoax.”
•
Terms Related to Israel or Israeli Politics: Such as “Israeli,” “Knesset,” and “Palestinian.”
This approach recognizes that antisemitism often cloaks itself in broader conspiratorial narratives, making it harder to detect without domain expertise. It highlights the strategic obfuscation inherent in the language game and the challenge of automated hate speech detection, as seemingly neutral terms are repurposed with hateful connotations within specific subcultural contexts.
Each item in the expert-generated list was classified as either explicit or implicit, with all antisemitic terms from the hate speech dictionary categorized as explicit. All dictionaries and data referenced in this analysis are publicly accessible via a GitHub (https://github.com/) repository. (All data, dictionaries, and code have been uploaded to a github repository. While all of the data is available directly on Reddit, the data could not be included in full due to the platform’s terms of use. For purposes of reliability, we have included a file in the github repository that contains all of the post ID’s used in our data. Our repository URL is: https://github.com/e2unlimitedtech/QAnonSubreddits accessed on 31 December 2025.).
Before analysis, the posts within the corpus underwent a series of text preparation procedures to ensure data quality and analytical precision. First, many terms within both the hate-speech dictionary and the expert-curated antisemitic term lists were context-dependent; their meaning could vary outside of an antisemitic context. To verify the intended hateful usage, two independent coders manually reviewed up to five randomly selected posts per term (or fewer if instances were limited) to assess whether the context aligned with the dictionary definition. This process achieved a high inter-rater reliability (IRR) of over 99.8%. Terms were excluded from the final detection list if fewer than half of the sampled instances aligned with the intended antisemitic usage. The resulting list for detection included 892 explicit and 278 implicit terms.
Following context verification, the textual data was further processed. Terms were standardized by converting all text to lowercase and stripping out non-alphanumeric characters. To enhance interpretability and consolidate references, terms referring to the same entity were grouped under a single label (e.g., “Senator Chuck Schumer,” “Chuck Schumer,” and “Schumer” were all grouped under “Schumer”). This approach reflects the “supernodes” concept used in previous works [2,76]. These co-references were identified through network graphs of high-frequency co-occurring terms, followed by manual verification.
Subsequently, the text was lemmatized using the WordNet Lemmatizer via the NLTK Python 3.9 package, reducing words to their base form (e.g., “rocks” and “rocking” to “rock”). Common English stop words (e.g., “a,” “an,” “is”) were also removed using NLTK’s standard list. Finally, consistent with common practices in text analysis, terms were filtered out if they were fewer than three characters long, appeared in fewer than 10 posts, or were present in more than 30% of the corpus. The remaining terms were then broken into unigrams, bigrams, and trigrams for analysis.

4.4. Measures: Operationalizing User Engagement and Escalation States

We coded comments and submissions as having implicit language, explicit language, or neither. If a post contained both implicit and explicit language, it was coded as containing explicit language, reflecting the higher language escalation. We then used the post classifications to create dummy variables related to user behavior. The measures are described in Table 2.
To examine the hypothesized language escalation, user behavior was categorized into distinct transition states for both comments and submissions: {neither_to_neither, neither_to_implicit, neither_to_explicit, implicit_to_implicit, implicit_to_explicit, explicit_to_explicit}. The models used for this study assumes a unidirectional progression from “neither” → “implicit” → “explicit”. We model this flow as irreversible, meaning that once a user transitions to a higher category (e.g., from “neither” to “implicit,” or from “implicit” to “explicit”), they are permanently categorized at that level or higher, even should their language use de-escalate in subsequent posts:
•
Neither: Standard content, containing no antisemitic messaging.
•
Implicit: Includes implicit antisemitic language in either comments or submissions.
•
Explicit: Includes explicit antisemitic language in either comments or submissions.
Although some individuals may reduce or even abandon hateful rhetoric, the lack of reliable indicators of whether such de-escalation reflects a durable ideological change necessitates a theoretically grounded assumption about radicalization processes. Specifically, we treat linguistic escalation as the crossing of a rhetorical threshold, after which users are embedded within the ideological identity and communicative norms of the extremist subculture. From this perspective, escalation reflects a point of no return in terms of category assignment, even if surface-level, self-imposed language moderation may occur. To contextualize this assumption and provide additional insight, we include an exploratory visualization (Figure 1) tracking each author’s first, highest, and final linguistic states. While these trajectories do not capture every intermediate transition and final posts do not necessarily indicate genuine de-escalation, the relatively small number of observed reversals supports the appropriateness of a unidirectional modeling approach. This exploratory analysis adds nuance to our understanding of linguistic trajectories and highlights the need for further research on deradicalization, without undermining the core unidirectional assumptions of our models.

5. Analytical Strategies

To examine the language escalation patterns and test the proposed hypotheses, we employ a combination of analytical tools. These methods were selected to provide a comprehensive understanding of time-to-event patterns, the influence of various covariates, and the statistical significance of observed behavioral shifts.

5.1. Survival Modeling

Survival analysis is used to investigate and describe time-to-event patterns in user behavior and provide insights into how user language evolves over time.
The non-parametric Kaplan–Meier estimator is used to estimate the survival function for time-based data. This method is applied to examine survival probabilities for four distinct user actions: posting an implicit comment, posting an implicit submission, posting an explicit comment, and posting an explicit submission. For each scenario, the survival function S(t), representing the likelihood that a user has not yet engaged in a given behavior by time t, was calculated. Kaplan–Meier models were chosen for their simplicity. They do not require the inclusion of covariates, allowing for a straightforward analysis of time-to-event distributions. By analyzing the timing of the first occurrence of implicit and explicit language, these models provide insights into the rate of radicalization. A shorter “survival time” before explicit language implies a faster radicalization pathway for some users, while longer times suggest a more gradual process.

5.2. Hypothesis Testing (Chi-Square Tests and Logistic Regression Models)

For Hypotheses 1, 2, and 3A/B, standard statistical tests, specifically the Chi-Square test, are used to evaluate significance and determine variations between groups of users within the corpus. We test Hypotheses 4A/B which concern the probability of adopting explicit language after initial use, the influence of posting frequency, and the impact of hyper-posters, these were incorporated collectively using logistic regression models.

6. Results

6.1. Descriptive Statistics of Language Use and User Transitions

The analysis of descriptive statistics provides initial evidence for the prevalence of different language types and the patterns of user transitions within the QAnon subreddits.
Table 3 describes the distribution of comments and submissions across authors and language states. Across the corpus, the proportion of implicit comments (~8%) and submissions (~13%) significantly dwarfed that of explicit comments (<1%) and submissions (<1%), indicating that coded language serves as the dominant mode of antisemitic expression.
Table 4 shows descriptive statistics for the average number of comments and submissions per user. The average number of comments per user was 35.03, with a median of 3.0 and a mode of 1.0, indicating a long tail of highly active users. For submissions, the mean was 7.88, with a median of 2.0 and a mode of 1.0. Together with the high proportion of comments among all posts, these findings show that most user communication takes place in response to posts by others rather than generation of new content. Moreover, they suggest that most users post relatively infrequently, while a small proportion of users may post an outsized number of comments and submissions relative to the average user.
Figure 2 and Figure 3 shows the distribution of irreversible transition states for each of the users in the study in comments and submissions, respectively, with state categories accounting for the content of any submissions or comments a user posted.
A majority of users remained in the “neither” category, indicating that most participants refrained from using implicit or explicit antisemitic language throughout the measured period. This suggests that while the subreddits contained antisemitic content, not all users actively engaged in its dissemination.
The transition from “neither” to using implicit language was observed as the second most common transition for both submissions and comments, accounting for 23.3% and 25.1% of the samples, respectively. In terms of submissions alone, a mere 3% of users transitioned from neither to explicit. Very few users entered the subreddits initially using either implicit (5.4%) or explicit language (~0%) in their submissions. Together, these findings suggest that users learn the language game through their involvement in the forum and that implicit language usage rather than explicit usage is the primary entry point for users into the language game.
The Kaplan–Meier models, shown in Figure 4, revealed a significant difference in the survival rate of users before their escalation to implicit and explicit involvement within the forums. The survival rates for users regarding explicit content were substantially higher than those for implicit content, indicating that users are significantly more likely to adopt implicit language earlier and more frequently than explicit language.
When examining the comment graphs, the survival model estimates that users active in the QAnon subreddit community for one month (T = 1) had approximately a 16% predicted probability of posting an implicit comment, compared to only about a 2% predicted probability of posting an explicit comment. Over an eight-month involvement period, the model predicts that nearly 60% of active users would be expected to post an implicit comment, while only about 15% would be expected to post an explicit comment (if they remained active for that duration). A similar pattern was observed for implicit and explicit submissions, where the model-estimated probability of adopting implicit language (63%) was much higher, and spread across a larger proportion of users, than for explicit language (6.5%) over a comparable ten-month timeline.
Importantly, these estimates do not indicate that these percentages of users produced such content; rather, they represent predicted probabilities from the survival model, conditional on continued participation in the community. This substantial disparity in survival-based estimates reinforces the idea of implicit language as a lower barrier to entry and a primary mechanism for initial radicalization, while explicit language remains a more exclusive, later-stage phenomenon among a smaller, more committed subset of users.

6.2. Hypothesis Test Outcomes

The statistical tests conducted provide strong support for the hypothesized relationships between user engagement, posting frequency, influential users, and language escalation.
To test Hypotheses 1, 2, and 3A/B, Chi-Square models were employed to determine significant variation between groups of users. The results, summarized in Table 5, indicate strong support for each while Table 6, Table 7 and Table 8 provide additional insight.
We find that new users who engage with antisemitic content are indeed more likely to engage with implicit content than explicit content. This finding suggests implicit language serves as the entry point into the community’s language game, allowing users to engage the lexicon and connect with the in-group identity without immediate exposure to overt social or platform-level consequences.
With regard to H2, namely that more frequent posters are more likely than others to use explicit language, we find that the top 1% of users, based on content created, post 12.04% of explicit antisemitic content, while the top 10% posts a full 60.32% of that content. This finding builds upon previous literature, showcasing how hyper-posters, and more active posters, take an active role in shaping the language game by posting a higher proportion of explicit language than the rest of the community members.
The findings also provide support for H3A and H3B, showing that users who comment on explicit content (χ2 = 6578.25) and those whose comments contain explicit content (χ2 = 1571.63), whether in response to explicit content or not, are more likely to progress to posting explicit submissions. Of those who engage with explicit content, 45.09% posted their own explicit content compared to only 3.66% of those who never engaged with such content. Similarly, among users who posted explicit comments, more than half (51.58%) posted explicit submissions. In comparison, of those who did not post explicit comments, only 5.63% posted explicit submissions. These results demonstrate that users transition from reactive engagement to proactive dissemination of hateful or radicalizing rhetoric, indicating a deeper integration into the community’s radicalized discourse and a higher level of commitment.
The logistic regression models shown in Table 9 further elucidate the factors influencing the utilization of antisemitic responses, providing support for H4A and H4B, shining additional light on the progressive role of language escalation.
We model predictors for the likelihood of a user posting implicit and explicit content, respectively. Users who engage with implicit content are more likely to post implicit content (OR = 6.89, ρ < 0.05) and explicit content (OR = 3.00, ρ < 0.05). Similarly, engagement with explicit content increases the probability of posting implicit content (OR = 4.66, ρ < 0.05) and explicit content (OR = 6.05, ρ < 0.05).
Two predictor variables capture interactions with hyper-posters, namely whether a user engages posts from a hyper-poster (User responded to hyper-poster) or whether someone in the top 1% of posters responds to a user (Hyper-poster responded to user). Users who respond to hyper-posters show highly significant effects for increased likelihood of posting implicit content (OR = 9.98 × 1042, ρ < 0.05). For explicit content, we obtain OR = 4582.50 with ρ ≈ 0.11. Although the results of the model fall slightly outside conventional significance thresholds, the direction and magnitude of the effect remain consistent with the broader pattern, suggesting that engagement with hyper-posters may also elevate the likelihood of producing explicit antisemitic content, albeit with weaker statistical confidence. This effect indicates that directly engaging with hyper-posters dramatically increases the likelihood of producing antisemitic content, especially in the implicit case. Conversely, receiving a response from a hyper-poster shows an even stronger positive association with posting behavior than responding to hyper-posters. Receiving a response from a hyper-poster is associated with significantly higher odds of posting implicit content (OR = 1.26 × 1060, ρ < 0.05) and explicit content (OR = 45,251.90, ρ < 0.05). This complementary relationship helps develop the broader narrative of social influence: users who seek out and engage with hyper-posters as well as those who receive recognition or validation from hyper-posters are further integrated into the social structure that sustains radical discourse. The bidirectional dynamic between attention-seeking and attention-receiving behaviors thus reinforces participation and ideological alignment, amplifying the use of antisemitic communication within the network. Together, these findings support a social model in which engagement with highly active, influential users facilitates the diffusion and normalization of hate speech across the community.
Finally, examining the association between posting implicit and explicit content demonstrates a process of progressive radicalization. Posting implicit antisemitic content increases the likelihood of a user posting explicit antisemitic content (OR = 5.10, ρ < 0.05). The results of these models reveal that online radicalization is a highly structured acceleration of collective identity and rhetorical strategies. The findings show that, while individual journeys may vary, there are discernible, patterned pathways that users follow.

7. Discussion

This study demonstrates the way that individuals interact with and contribute to a subcultural community’s semantic network. We find that antisemitic language in QAnon subreddits follows a structured, multi-stage process shaped by both individual behavior and community dynamics. Across models, the results confirm that users typically enter the community through implicit “dog whistles” before escalating to explicit hate speech. In this study, collective identity and radicalization manifest in relation to an underlying semantic network through patterned transitions from neutral language to implicit and then explicit rhetoric. The findings of the study indicate that users learn and internalize group identities via shared, increasingly extreme linguistic associations rather than isolated expressions. This escalation reflects a semantic normalization process in which repeated exposure to, and participation in, subcultural vocabularies cements in-group belonging and normalizes radicalized meaning structures. By empirically capturing rhetorical escalation, this study provides a useful framework for semantic analysis that enables scholars to more effectively map rhetorical escalation, trace the evolution of meaning within subcultures, and identify the mechanisms through which online communities produce and sustain radicalized semantic networks.
Previous research on semantic networks has found that explicit antisemitic language is rare compared to implicit language in both submissions and comments within QAnon subreddits [2]. Looking specifically at user behavior, we corroborate this finding at the user rather than the post level. We find that coded speech is the dominant method users employ for expressing antisemitic beliefs in these subreddits, likely due to a combination of the social taboo associated with explicit language and the reduced social and platform-level consequences it incurs. In addition, the dominance of implicit rhetoric demonstrates that users are more likely to engage with (comment on) posts containing implicit language than with those containing explicit language, more likely to post implicit language than explicit language to the extent they post any hateful rhetoric, and more likely to post such rhetoric when they frequently engage with it by responding to posts that contain it. We find evidence of a process of escalation of rhetoric, working in the form of a funnel, moving from engaging with and posting implicit content, to that of explicit content creation. In short, building from previous work demonstrating the social significance of this subcultural community’s antisemitic semantic network linking implicit and explicit language, we uncover the process through which individuals enter and actively contribute to this network of antisemitic discourse.
This study has shown that general involvement and self-identification with the group are significant predictors of increased utilization of antisemitic rhetoric. Specifically, the more frequently an active user posts and engages with the subgroup, the more likely they are to use explicit language. Hypotheses 1–3 underscore that consistent immersion in the community’s discourse accelerates the adoption of more extreme linguistic forms. Active contribution, showcased by individuals who engage both in the forum and with explicit rhetoric, leads to a proactive position in advancing and disseminating harmful rhetoric within the community. The difference in engagement levels, with a vast majority of active users utilizing implicit comments or submissions over an 8-to-10-month period, while only a small minority engage in explicit rhetoric, further underscores the funnel-like nature of this escalation.
The influence of hyper-posters, who represent central nodes within the group’s social network, suggests a social contagion model of radicalization, where these active and influential users act as vectors for the spread of hate speech. Direct engagement with a hyper-poster’s content, such as through replies or comments, and attention by the hyper-poster to a user, both serve as a form of validation or implicit instruction that may serve to boost perceived in-group identification and linguistic capital within the subcultural community [33,36] by reinforcing in-group/out-group dynamics outlined in the language game. This bidirectional relationship signals approval for certain types of rhetoric and encouragement of the recipient to escalate their own language use to engage more fully in the language game and the community’s identity construction. This dynamic creates a powerful feedback loop where influential users reinforce and accelerate the radicalization of others, making the community a dynamic system of social influence and collective identification. Together, our findings indicate a strong relationship between a subcultural community’s social network, the identity built within online subcultures, and its semantic network, giving further support to the important role language games play in establishing insider status and group identity.
This study offers important insights into rhetorical escalation within online, subcultural communities; however, several limitations should be noted. First, the study primarily examines rhetorical escalation and, while Figure 1 showcases the observable transition states, these analyses remain exploratory. Although these transition states provide some insight into possible rhetorical de-escalation, the current study does not systematically analyze these processes. Future research is necessary to determine whether de-radicalization follows structured, identifiable patterns comparable to those observed in escalation. Future work should build on this capability to better understand how and when users reduce or abandon extremist language within online communities.
Second, the dataset may be incomplete or skewed due to deleted, abandoned, or auto-moderated accounts and posts, which could have influenced the detected antisemitic language, potentially biasing the sample towards more visible or persistent users or users with multiple accounts over the studied timeline. However, this limitation is a documented challenge in online forum and social media research [77] and is not considered a fundamental drawback to the study’s primary objectives. Additionally, while the dataset used in this study is slightly dated and while there have been concerns over a lack of moderation in online communities in recent years, we believe that this does not impact the findings of this study, the role that dog-whistles play within both subcultural and online communities, nor change the dynamics of social capital in these communities.
Future research should address these limitations and expand beyond them to more fully understand the complex pathways and processes of radicalization and potential disengagement. Expanding this approach to investigate possible pathways for de-escalation would be crucial. Furthermore, future studies could explore the specific mechanisms through which hyper-posters exert their influence, perhaps through network analysis of user interactions. By pursuing these avenues, the field would continue to move closer to understanding not only how antisemitic rhetoric proliferates, but also how it might be effectively countered within the ever-changing landscape of digital communities.

8. Conclusions

This study has provided empirical evidence for a patterned and socially contingent process of language radicalization within QAnon subreddits. Like many subcultural communities, these subreddits feature a language game, an underlying semantic network that in this case links implicit and explicit antisemitic language, used to socialize users by educating them in the meaning of terms in the coded lexicon and to build a sense of group membership. This study has demonstrated that both engagement with and use of antisemitic rhetoric—entry into and contribution to the group’s semantic network—often begin with implicit language, serving as a strategic entry point for users to navigate social and platform-level constraints while learning the community’s language game. This study also shows that interaction with the group’s central nodes in their social network, the hyper posters, promotes contribution of implicit and explicit content to the semantic network. Combined, these results suggest a logical and useful theoretical and methodological framework for understanding the structured growth of this antisemitic semantic network in relation to a community’s social network, which may be generalizable to understanding the growth and spread of other forms of online hate speech and radicalized collective identity. The study’s contribution to understanding rhetorical socialization implies that online communities are not just passive platforms but active agents in shaping individual ideology and behavior, suggesting a generalizable model for how digital subcultures can cultivate and amplify harmful narratives.
Ultimately, this work underscores the dynamic and adaptive nature of online hate speech and the limitations of simplistic content moderation approaches. To effectively counter the proliferation of such rhetoric, a deeper understanding of these complex user-level escalation patterns and community-driven mechanisms is essential. Continued research, particularly into pathways of de-escalation and the nuanced social dynamics of extremist online communities, remains a pressing necessity in the ongoing effort to address the challenges posed by online radicalization.

Author Contributions

Conceptualization, N.D.C. and D.B.W.; Methodology, N.D.C., P.A., D.B.W. and M.L.; Software, P.A. and M.L.; Validation, N.D.C., P.A., D.B.W. and M.L.; Formal analysis, N.D.C., D.B.W. and M.L.; Investigation, D.B.W.; Resources, D.B.W.; Data curation, D.B.W. and M.L.; Writing—original draft, N.D.C., P.A., D.B.W. and J.S.K.; Writing—review & editing, N.D.C., P.A., D.B.W. and J.S.K.; Visualization, N.D.C., P.A. and M.L.; Supervision, N.D.C., D.B.W., M.L. and J.S.K.; Project administration, N.D.C. and D.B.W.; Funding acquisition, D.B.W. All authors have read and agreed to the published version of the manuscript.

Funding

This material is based on research sponsored by The United States Air Force under agreement number FA88650-22-2-6438. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the United States Air Force or the U.S. Government.

Institutional Review Board Statement

The Queens College-CUNY IRB classified this study as exempt from human subject research because it involved analysis of publicly available, de-identified Reddit posts and comments. Under these criteria, the research did not involve interaction with individuals, collection of private information, or access to identifiable data, and therefore did not require additional IRB approval.

Informed Consent Statement

The Queens College-CUNY IRB classified this study as exempt from human subject research because it involved analysis of publicly available, de-identified Reddit posts and comments. Under these criteria, the research did not involve interaction with individuals, collection of private information, or access to identifiable data, and therefore did not require additional participant informed consent.

Data Availability Statement

All data, dictionaries, and code have been uploaded to a github repository QAnonSubreddits: https://github.com/e2unlimitedtech/QAnonSubreddits (accessed on 31 December 2025). While all of the data is available directly on Reddit, the data could not be included in full due to the platform’s terms of use. For purposes of reliability, we have included a file in the github repository that contains all of the post ID’s used in our data.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. All transition states of the users in the corpus.
Figure 1. All transition states of the users in the corpus.
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Figure 2. User Comments Transition States.
Figure 2. User Comments Transition States.
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Figure 3. User Submissions Transition States.
Figure 3. User Submissions Transition States.
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Figure 4. Kaplan–Meier Survival Models.
Figure 4. Kaplan–Meier Survival Models.
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Table 1. Dataset Overview.
Table 1. Dataset Overview.
DatasetTotal UsersTotal PostsTotal SubmissionsTotal CommentsTime Period Covered
Reddit Corpus34,5021,261,267128,2691,132,998December 2017–September 2018
Reddit Corpus (Preprocessed)34,4841,159,742111,0361,048,706December 2017–September 2018
Table 2. Variable Operationalization.
Table 2. Variable Operationalization.
Covariate NameCovariate DescriptionCovariate Type
Engagement with implicit contentThe user has commented on implicit content (submissions or comments)Binary (0–1)
Engagement with explicit contentThe user has commented on explicit content (submissions or comments)Binary (0–1)
User responded to hyper-posterThe number of times a user has responded to a post (submissions or comments) made by hyper-posters (top 1% of users)Integer
Hyper-poster responded to userThe number of times a hyper-poster (top 1% of users) has responded to a post (submissions or comments) made by the userInteger
User has posted implicit content
(Response for implicit model)
(Covariate for explicit model)		The user has made an implicit post (submissions or comments)Binary (0–1)
User has posted explicit content
(Response for explicit model)		The user has made an explicit post (submissions or comments)Binary (0–1)
Table 3. Distribution of Post Types Across Authors and Language States.
Table 3. Distribution of Post Types Across Authors and Language States.
Dataset (Preprocessed)Unique AuthorsNumber of ElementsNumber of NeitherNumber of ImplicitNumber of Explicit
Comments29,9351,048,706954,81886,4357453
Submissions14,234111,03695,76414,593679
Table 4. Average Number of Comments and Submissions Per User.
Table 4. Average Number of Comments and Submissions Per User.
Dataset
(Preprocessed)		MeanMedianModeSD (Rounded)
Comments35.033.01.0151.70
Submissions7.802.01.031.20
Table 5. Statistical Tests for Hypotheses 1–3.
Table 5. Statistical Tests for Hypotheses 1–3.
HypothesisChi-Squared Valuep-Value
H1.   New users (in the first month of participating in the forum) who engage with antisemitic content are more likely to engage with implicit content, either by posting responses to implicit submissions or to implicit comments, than with explicit content763.22<0.001
H2.  More frequent posters—those more engaged with/self-identified with the group—are more likely than others to use explicit language in their posts.7109.35<0.001
H3A. Users who engage explicit content by commenting on explicit submissions or comments are more likely to post explicit submissions themselves.6578.25<0.001
H3B.  Users who post explicit comments are more likely to post explicit submissions, taking an active role in shaping the explicit conversation in the group rather than merely responding to it1571.63<0.001
Table 6. New Users (within the first month of activity) who engage with antisemitic content.
Table 6. New Users (within the first month of activity) who engage with antisemitic content.
Did Not Engage ExplicitEngaged Explicit
Did Not Engage Implicit19,696 (68.36%)322 (28.72%)
Engaged Implicit9118 (31.64%)799 (71.28%)
Table 7. Proportion of Posters engaged with Explicit Antisemitic Content (Top 1%).
Table 7. Proportion of Posters engaged with Explicit Antisemitic Content (Top 1%).
ExplicitNot Explicit
Top 1% of Users284 (12.04%)61 (0.19%)
Other 99% of Users2075 (87.96%)32,064 (99.81%)
Table 8. Proportion of Posters engaged with Explicit Antisemitic Content (Top 10%).
Table 8. Proportion of Posters engaged with Explicit Antisemitic Content (Top 10%).
ExplicitNot Explicit
Top 10% of Users1423 (60.32%)2029 (6.32%)
Other 90% of Users936 (39.68%)30,096 (93.68%)
Table 9. Antisemitic Post Logit Models.
Table 9. Antisemitic Post Logit Models.
VariablePosts Implicit ContentPosts Explicit Content
OR(SE)OR(SE)
Engagement with
implicit content		6.89 (0.03) *3.00 (0.08) *
Engagement with
explicit content		4.66 (0.065) *6.05 (0.06) *
User responded to
hyper-poster		9.98 × 1042 (21.2) *4582.50 (5.28)
Hyper-poster responded
to user		1.26 × 1060 (15.6) *45,251.90 (3.97) *
User has posted implicit content-5.10 (0.07) *
cons. 0.16 (0.02) *0.01 (0.07) *
N29,93529,935
Pseudo-R20.250.31
* ρ < 0.05.
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Cohen, N.D.; Antonaros, P.; Weinberg, D.B.; Levy, M.; Kopstein, J.S. Patterned Radicalization: User Behavior Analysis and Antisemitic Language in QAnon Subreddits. Information 2026, 17, 179. https://doi.org/10.3390/info17020179

AMA Style

Cohen ND, Antonaros P, Weinberg DB, Levy M, Kopstein JS. Patterned Radicalization: User Behavior Analysis and Antisemitic Language in QAnon Subreddits. Information. 2026; 17(2):179. https://doi.org/10.3390/info17020179

Chicago/Turabian Style

Cohen, Noah D., Peter Antonaros, Dana B. Weinberg, Meyer Levy, and Jeffrey S. Kopstein. 2026. "Patterned Radicalization: User Behavior Analysis and Antisemitic Language in QAnon Subreddits" Information 17, no. 2: 179. https://doi.org/10.3390/info17020179

APA Style

Cohen, N. D., Antonaros, P., Weinberg, D. B., Levy, M., & Kopstein, J. S. (2026). Patterned Radicalization: User Behavior Analysis and Antisemitic Language in QAnon Subreddits. Information, 17(2), 179. https://doi.org/10.3390/info17020179

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