social media moderators

Abstract: Recent elections in the United States have seen online rumors become a regular feature of election discourse as voters and election officials work to navigate a sea of digitally spread claims and propaganda. For the uninitiated, these rumors can feel overwhelming and random, sowing doubt about the integrity and legitimacy of recent elections. In this paper, we reviewed and qualitatively coded a large dataset of 590 election rumors collected during the the 2020 and 2022 national elections. We found that there are tropes that recur time and time again in election rumors, highlighting how these rumors are not as random as they may first appear. We make two primary contributions in this work: first, we introduce a hierarchical framework that structures observed tropes in three tiers (Meta-category, Meso-trope, and Micro-trope). Second, combining the framework with our coded data, we identify a visible grammar of election rumors, highlighting how in the majority of cases a rumor contains an Actor, and Object, and an Action, with optional grammatical components of Suggested Remedies and Scene & Setting. The framework and grammar can be used by other researchers to act as the underlying information architecture of analyses examining similar rumors, enabling the rapid identification, analysis, and prediction of election rumors.

Abstract: Hate speech has become increasingly prevalent on social media, raising concerns about its detection, interpretation, and moderation. Persian, a relatively low-resource language, lacks sufficient annotated data and computational resources for fine-grained hate speech analysis. In this study, we introduce a fine-grained, multi-hop benchmark for explainable Persian hate speech analysis, covering content triage, target identification, hate tactic and moral foundation analysis, textual evidence extraction, harmful narrative inference, and severity assessment. By evaluating state-of-the-art large language models (LLMs), we find that performance generally declines as tasks require more fine-grained semantic and inferential reasoning. Proprietary models generally outperform open-weight models, while reasoning-oriented models show no consistent advantage. Incorporating intermediate reasoning steps substantially improves severity prediction compared with one-shot assessment. Our analysis further reveals that Incitement to Violence or Harm is most strongly associated with higher severity, while political-affiliation targets are particularly associated with subversion-based moral framing. Overall, this work provides insights into LLM-based hate speech analysis and supports more accountable moderation of Persian-language social media. The benchmark is publicly available at https://github.com/kalhorghazal/From-Triage-to-Impact.

Abstract: AI slop has flooded social media feeds with overprocessed and derivative content--Jesus fusing with a shrimp, a humanoid orange chubby cat shoplifting, and a "hero" dog rescuing a human child after a plane crash. This might be no laughing matter, as waves of media coverage have taken a distinctly negative bent, emphasizing its low quality, ethical concerns, and threats to human creativity. Despite extensive attention from politicians and technologists, the perspectives of everyday users--the very audience most exposed to this phenomenon--remain largely unexplored. This descriptive work explored how Reddit users experience, conceptualize and contest AI slop. Inductive thematic analysis of posts and comments across six slop-related subreddits identified four themes covering constitutional defects, algorithmic overreach, user resistance, and entertainment outcomes--ranging from anxieties about eroding agency, moral norms, creativity, and existential questions to defensive distinctions between quality AI content and slop. We derive a user-driven definition: AI slop is audience-contested AI-generated content characterized by constitutional defects and enabled by platform logics. We operationalize slop through a facet-theoretical framework to inform future research into this fuzzy category.

Abstract: Detecting communicative meaning in dialogue requires modeling not only lexical content but also the participant-role structures through which sentiment, emotion, politeness, and formality (SEPF) are expressed. Existing approaches rely on contextual language representations while leaving semantic roles implicit. We introduce a role-aware transformer architecture that maps the four SEPF dimensions to theory-grounded Semantic Role Labeling (SRL) configurations, incorporating these configurations directly into the transformer's self-attention mechanism. Role-aware attention models non-directional internal states (sentiment and emotion), while directional role-aware attention captures initiator-to-recipient relations for external cues (politeness and formality). The resulting role-aware representations are fused with contextual embeddings through a learned gate that adaptively weights how much role information each input contributes, rather than appending semantic roles as an auxiliary feature vector. Across four benchmark datasets, the framework yields statistically reliable gains over strong BERT-based baselines for emotion, politeness, and formality, with gains concentrated in theoretically predicted cases such as low-frequency, complex emotions and highly polite expressions; sentiment improvements are marginal and non-significant, suggesting that sentiment is conveyed more through lexical choice than participant-role structure. Additional evaluation on real-world data demonstrates that the framework improves both predictive performance and model traceability. These results demonstrate that linguistically grounded modifications to transformer self-attention improve the detection of communicative meaning, generalize beyond benchmark datasets to real-world mental-health data, and yield traceable, role-grounded explanations that extend beyond predictive performance to interpretability.

Abstract: Public vs. private universities is a debatable issue, and it creates polarization on social media in Bangladesh. Debate on quality, jobs, and prestige is passionate among the students, parents, and graduates, the majority of whom speak Bengali, a low-resource language. To measure this polarization, this paper introduces a manually annotated dataset of 4,060 Bengali comments labeled as Pro-Public, Pro-Private, or Neutral. We evaluated the quality of our annotations by Fleiss's Kappa agreement that was 0.89, corresponding to a high agreement among annotators. The classical ML (SVM, Random Forest, XGBoost), BiLSTM network, hybrid BanglaBERT+XGBoost models and the state-of-the-art zero-shot LLMs (Claude Sonnet 4, DeepSeek-V3.1, Llama 4 Maverick, Kimi K2 Thinking, Qwen3-235B Thinking) models are evaluated. The accuracy of BanglaBERT+XGBoost is 91.81% and macro F1 score is 91.70%, which is higher than all the supervised baselines. The zero-shot Llama 4 Maverick Thinking achieves a macro F1 of 0.931 (overall accuracy of 93.31%) without any fine-tuning. All machine learning (ML), deep machine learning (DL) and transformer models were outperformed by the zero-shot Llama 4 Maverick model. Polarization also is evident, in some ways more clearly in the Pro-Private comments, which emphasize modern facilities and timely graduation, versus the Pro-Public comments, which emphasize affordability and government jobs. Our findings open new directions for analyzing social media polarization in low-resource languages.

Abstract: We investigate the extent to which the Hamas attacks on Israel of October 7, 2023, have affected German social media debates about Judaism and Israel. For this, we develop an approach to detect 26 anti-Semitic categories in user postings via large language models (LLMs). The approach is applied to Facebook and Telegram posts (N=125,718) from three months before and after the event. Methodically, we test different open-weight models in two setups---with and without user information as additional context to the post text. The best setup achieves up to 83 % F1-score for binary anti-Semitism detection on our manually coded validation set. User context provides valuable information for most LLMs and drastically reduces false positives, for example, when (critically) reporting on anti-Semitic incidents. Concerning our topic, we find that anti-Semitism is surging significantly on both platforms, while being about ten times more prevalent on Telegram compared to Facebook. Facebook users express anti-Semitic views most likely in posts about an alleged genocide in Gaza carried out by the Israeli army, whereas classic anti-Semitic stereotypes related to power and conspiracy theories are dominant on Telegram. After the attack, the discourse patterns on both platforms show signs of convergence, as classic anti-Semitism increases on Facebook, whereas Israel-related categories surge on Telegram.

Abstract: The impact of online platforms in political mobilization is widely documented, yet it remains unclear how it affects different demographic groups. To shed light on this phenomenon, we examine two contrasting cases: engagement with environmental causes on one side, and with conspiracy theories on the other. Both are well represented on Reddit, allowing us to study the different origins of these groups using a network-based method. By inferring age, gender, affluence, and partisanship of Reddit users, we construct stratified transition networks to compare the entry pathways of different demographic groups toward these communities. We find that demographic differentiation operates differently in these two domains. Entry into conspiracy communities is primarily structured by gender and partisanship: masculine users disproportionately arrive through overtly political and alt-right spaces and share right-wing media sources, whereas feminine users more often pass through esoteric and spiritual subreddits before converging on r/conspiracy. In contrast, environmental communities are differentiated mainly by age and affluence. Affluent users tend to approach environmental subreddits via discussions of individual energy management, technology, and finance, while less affluent users arrive through protest-oriented and climate movement spaces. By comparing these two issue domains, the study shows that sociodemographic sorting does not operate uniformly across polarized topics. Instead, distinct demographic groups build their own paths, shaping both the routes through which they engage with political content and the informational ecosystems that sustain their engagement. Our approach, based on the attention-flow graph, refines our understanding of how different groups become embedded in online political communities.

Abstract: ArGuard is a shared task on harmful content detection in Arabic memes and LLM prompts. It includes two tracks: Track A focuses on multimodal hate detection in Arabic memes, while Track B addresses harmful prompt detection for Arabic LLM safety evaluation. In total, 58 teams registered, 35 participated in the final evaluation, and 27 submitted system-description papers. Participating teams explored models such as AraBERT, Jais, and Qwen3-VL. The best systems achieved macro-F1 scores of 0.823 on A1, 0.419 on A2, 0.984 on B1, and 0.790 on B2. Fine-grained meme classification in A2 was the most challenging setting, partly due to sparse labels and train-test distribution shifts.

Abstract: The widespread use of social media, particularly image- and video-based platforms, has turned them into key sources of both normative and informational content related to health and diet. This may contribute to the development of disordered eating behaviors or, potentially, eating disorders. This study uses mixed-methods analysis applied to 3129 YouTube videos about diet and weight loss in order to quantify the level of risk of low-quality information and heightened focus on the body image. We adapt three quality measurement frameworks from the literature -- PRHISM, HONcode and SMEC -- to the online video context, perform manual annotation of a sample of the data, and design an LLM content characterization pipeline to score the videos on the quality of their content and the focus on the human body. Surprisingly, we find that videos around personal storytelling and mindset & motivation are associated with higher-quality content, whereas supplement reviews (arguably more medically sensitive ones) are not. Further, the body-related mentions of weight measurement and negative body image are associated with an increased viewership, whereas the mentions of positive body image are associated with an increased engagement rate in terms of likes and comments, but not viewership. Worryingly, we find a cluster of videos categorized as "music" which promote the dietary supplements Mitolyn and the injectable weight loss drug Mounjaro. As video-based platforms grow in popularity, particularly among younger audiences, studies such as the one presented here are essential for developing empirically grounded tools to enhance the detection of harmful content and inform more effective moderation practices.

Abstract: LLM-based AI systems answer political questions for hundreds of millions of people. Current audits measure what they say to an average user, but their behavior is dynamic. I argue that their political behavior is a set of policies over whom to answer, what to say, and whether to engage at all, conditional on the topic and what the system knows about the user. I call these policies the system's speech regime, which is how a developer settles the tradeoff between answering, accommodating the user, and refusing, each of which carries a cost that varies by topic. I derive a typology of five regimes from two dimensions, engagement and stance. I test six AI systems (OpenAI, Anthropic, xAI, Google, Mistral, DeepSeek) in a preregistered experiment of 7,500 multi-turn conversations that randomly assign the user's political identity across five topics: abortion, Catalan independence, climate change, Nazism, and a zero-stakes control (pineapple on pizza). Two LLM judges from different developers score every answer, validated against human coding, and refusal is treated as an outcome rather than missing data. Every system accommodates the user on the control topic, showing that political restraint is a policy. On contested topics the systems fall into different regimes: on abortion, GPT engages and mirrors every user, Gemma refuses everyone, Claude answers strongly conservative users 35 percent of the time and almost no one else, and Grok accommodates conservatives only. On settled topics such as climate change and Nazism, five systems hold firm for every user. The systems also infer the user's overall ideology, so accommodation can spill over to topics not yet discussed. A comparison of two Grok releases shows the regime changing between versions in a way current audits miss. Speech regimes matter for alignment research and for polarization, political knowledge, and the quality of democracy.

Abstract: Hostile rhetoric toward social groups can normalize exclusion and justify mistreatment, as well as contribute to rising polarization and political violence. Efforts to moderate hostile rhetoric in online speech draw on foundational theories in social and moral psychology, and political science. However, these theories were developed largely in parallel, often propose different and sometimes conflicting accounts of how hostility develops, and have rarely been tested against each other in real discourse. The result is a fragmented understanding of the rhetorical mechanisms of hostility, without a clear sense of how they appear, and relate to each other, in real-world discourse. Using 2.86 million posts from TikTok, Truth Social, and Twitter/X during the 2024 U.S. presidential election, we model the mechanisms of six foundational theories of intergroup hostility -- boundary construction, threat construction, scapegoating, negative evaluation, dehumanization, and action orientation -- within a common empirical framework to recover the broader organization of intergroup hostility rhetoric. Structurally, we find that boundary construction and threat construction anchor the system; temporally, we find that these mechanisms tend to follow a regular ordering: boundary construction, derogation, and action orientation tend to appear early; dehumanization and threat construction later; scapegoating latest. Mapping how these theoretical frameworks actually manifest in discourse bridges longstanding divisions across social science traditions and presents computational social science with a clearer empirical foundation for modeling intergroup hostility rhetoric beyond single-label detection.

Abstract: Generative AI (GenAI) is increasingly integrated into social media, raising questions about whether, where, and how it belongs. In decentralized social media (DSM), these decisions are distributed across users, developers, moderators, and administrators, making GenAI a collective governance challenge. At the same time, public discourse often flattens arguments to broad pro- or anti-AI positions that offer little insight into what people actually find (in)appropriate and why. Through 20 semi-structured interviews with people from Mastodon and Bluesky, structured around seven GenAI scenarios, we examine how people reason about GenAI's appropriateness in DSM. We find that participants drew conditional boundaries around particular GenAI configurations through distinct, salient, and weighted considerations spanning technology, integration, and use. We conceptualize this as boundary drawing and show how making such boundaries visible can support more grounded design, policy, and collective deliberation around GenAI in DSM.

Abstract: The rapid diffusion of hate speech and misinformation on social networks challenges democratic societies, since direct suppression efforts may deepen polarization, fuel public distrusts, and strengthen extremist narratives. LLM-driven counter-narratives (CNs) offer a promising way to reduce those risks, yet their effectiveness depends on rhetorical and stylistic choices that remain poorly understood. We present a multi-stage agent-based framework for generating, refining, and evaluating CNs, applied to pro-Russian hate and misinformation narratives on the war with Ukraine and adaptable to other domains. A pilot experiment with human evaluators identifies effective technique style pairings, such as repetition with emotional framing enhancing persuasiveness. Building on these insights, we introduce a multi-agent refinement process that iteratively improves CNs for persuasiveness, emotional engagement, and shareability. After human validation confirmed improvement, an automated safety analysis shows that our refined CNs match or improve on expert-written counterspeech. A simulated experiment then shows that they reduce the perceived strength of pro-Russian narratives and consistently outperform a vanilla LLM baseline, highlighting a pathway toward scalable, narrative-specific interventions against hate speech and misinformation. Code and data accompanying this work are publicly available at https://github.com/carmelkron/inlg2026-counter-narratives.