Selected Publications

Beyond Throughput: Objectives and Tradeoffs in Volunteer Moderation

Tanvi Bajpai, Eshwar Chandrasekharan

ICWSM 2027 · Preprint (forthcoming)

Reddit relies on volunteer moderators to enforce community rules, configure tools, and review flagged content. This labor is substantial, worth millions in unpaid effort, and increasingly hard to sustain as communities grow. While recent updates to Reddit's modqueue emphasize efficiency and reducing redundancy, recent research shows that moderators use the interface in varied ways, value objectives beyond throughput (such as fairness and accuracy), and often resist features that disrupt workflows. In this paper, we survey 106 active Reddit moderators to examine the objectives they bring to their modqueue work and the kinds of interventions they consider helpful. Our findings highlight wide variation in values and workflows, with no single objective beyond accuracy dominating, and different perspectives on which interventions are useful. To address this diversity, we introduce a simulation-based approach that can complement empirical findings by probing tradeoffs and testing potential interventions, and provide design recommendations based on our findings.

VASTU: Language Models Struggle to Recognize Online Community Values

Agam Goyal, Xianyang Zhan, Charlotte Lambert, Koustuv Saha, Eshwar Chandrasekharan

EMNLP 2026 (main conf.) · Preprint (forthcoming)

Detecting what content communities value is a foundational challenge for social computing systems—from feed curation and content ranking to moderation tools and personalized recommendation systems. Yet existing approaches remain fragmented across methodological paradigms, and it remains unclear which methods best capture community-specific notions of value. We introduce VASTU (Value-Aligned Social Toolkit for Online Content Curation), a benchmark and evaluation framework for systematically comparing approaches to detecting community-valued content. VASTU includes a dataset of 75,000 comments from 15 diverse Reddit communities, annotated with community approval labels and rich linguistic features. Using VASTU, we evaluate feature-based models, transformers, prompted and fine-tuned language models under global versus community-specific training regimes. We find that community-specific models consistently outperform global approaches, with fine-tuned transformers achieving the strongest performance (0.72 AUROC). Notably, fine-tuned SLMs (0.65 AUROC) substantially outperform prompted LLMs (0.60 AUROC) despite being 100 times smaller. Counterintuitively, chain-of-thought prompting provides no benefit, and reasoning models perform the worst (0.53 AUROC), suggesting this task requires learning community norms rather than test-time reasoning. By releasing VASTU, we provide a standardized benchmark to advance research on value-aligned sociotechnical systems.

LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback

Jiwon Kim, Maya Ajit, Sherry Gong, Soorya Ram Shimgekar, Dong Whi Yoo, Eshwar Chandrasekharan, Koustuv Saha

EMNLP 2026 (main conf.) · Preprint (forthcoming)

Large language models (LLMs) show promise in generating supportive responses for mental health queries, but improving their usefulness, empathy, and safety often requires substantial compute, expert input, and labeled data. At the same time, deploying proprietary, cloud-based models for mental health-related interactions raises important privacy and data-governance concerns, given the sensitivities. To address this challenge, we introduce LLUMI setup that can be hosted in-house within protected environments. LLUMI consists of two complementary components: a generation model (GM), which drafts supportive responses to mental health queries, and an improvement model (IM), which revises an initial human-crafted response. We leverage feedback signals from Reddit mental health communities, using community endorsement patterns such as upvotes and downvotes to construct chosen–rejected response pairs for Supervised Fine Tuning (SFT) and Direct Preference Optimization (DPO). We further align LLUMI using human evaluation across five dimensions: readability, empathy, connection, actionability, and safety. Our results show that, despite relying on smaller open-source models rather than proprietary cloud-based GPT models, LLUMI achieves comparable performance across linguistic analyses and human evaluations. These findings suggest that open-source models, when trained with community-derived preference signals, can support high-quality mental health support assistance while offering a more privacy-preserving alternative for sensitive support contexts.

The Hidden Toll of Social Media News on Psychosocial Wellbeing

Olivia Pal, Agam Goyal, Eshwar Chandrasekharan, Koustuv Saha

EMNLP 2026 (main conf.) · Preprint (forthcoming)

News consumption on social media has become ubiquitous, yet how different forms of engagement shape psychosocial outcomes remains unclear. To address this gap, we leveraged a large-scale dataset of ∼26M posts and ∼45M comments on the Bluesky platform, and conducted a quasi-experimental study, matching 81,345 Treated users exposed to News feeds with 83,711 Control users using stratified propensity score analysis. We examined psychosocial wellbeing, in terms of affective, behavioral, and cognitive outcomes. Our findings reveal that news engagement produces systematic trade-offs: increased depression, stress, and anxiety, yet decreased loneliness and increased social interaction on the platform. Regression models reveal that News feed bookmarking is associated with greater psychosocial deterioration compared to commenting or quoting, with magnitude differences exceeding tenfold. These per-engagement effects accumulate with repeated exposure, showing significant psychosocial impacts. Our work extends theories of news effects beyond crisis-centric frameworks by demonstrating that routine consumption creates distinct psychological dynamics depending on engagement type, and bears implications for tools and interventions for mitigating the psychosocial costs of news consumption on social media.

Facts Without Rules: Boundary Metadata Collapse in Multi-Agent LLM Handoffs

Yian Wang, Agam Goyal, Eshwar Chandrasekharan, Hari Sundaram

EMNLP 2026 (Findings) · (forthcoming)

Abstract forthcoming; will be updated after the camera-ready version is available.

Answer Bubbles: Information Exposure in AI-Mediated Search

Michelle Huang*, Agam Goyal*, Koustuv Saha, Eshwar Chandrasekharan

EMNLP 2026 (Findings) · Preprint (forthcoming)

Generative search systems are increasingly replacing link-based retrieval with AI-generated summaries, yet little is known about how these systems differ in sources, language, and fidelity to cited material. We examine responses to 11,000 real search queries across four systems—vanilla GPT, Search GPT, Google AI Overviews, and traditional Google Search—at three levels: source diversity, linguistic characterization of the generated summary, and source-summary fidelity. We find that generative search systems exhibit significant source-selection biases in their citations, favoring certain sources over others. Incorporating search also selectively attenuates epistemic markers, reducing hedging by up to 60% while preserving confidence language in the AI-generated summaries. At the same time, AI summaries further compound the citation biases: Wikipedia and longer sources are disproportionately overrepresented, whereas cited social media content and negatively framed sources are substantially underrepresented. Our findings highlight the potential for answer bubbles, in which identical queries yield structurally different information realities across systems, with implications for user trust, source visibility, and the transparency of AI-mediated information access.

The Language of Approval: Identifying the Drivers of Positive Feedback Online

Agam Goyal, Charlotte Lambert, Eshwar Chandrasekharan

CHI 2026 · Preprint (forthcoming)

Positive feedback via likes and awards is central to online governance, yet which attributes of users’ posts elicit rewards—and how these vary across authors and communities—remains unclear. To examine this, we combine quasi-experimental causal inference with predictive modeling on 11M posts from 100 subreddits. We identify linguistic patterns and stylistic attributes causally linked to rewards, controlling for author reputation, timing, and community context. For example, overtly complicated language, tentative style, and toxicity reduce rewards. We use our set of curated features to train models that can detect highly-upvoted posts with high AUC. Our audit of community guidelines highlights a “policy-practice gap”—most rules focus primarily on civility and formatting requirements, with little emphasis on the attributes identified to drive positive feedback. These results inform the design of community guidelines, support interfaces that teach users how to craft desirable contributions, and moderation workflows that emphasize positive reinforcement over purely punitive enforcement.

Needling Through the Threads: A Visualization Tool for Navigating Threaded Online Discussions

Yijun Liu, Frederick Choi, Eshwar Chandrasekharan

CHI 2026 · Preprint (forthcoming)

Navigating large-scale online discussions is difficult due to their rapid pace and high volume of content. Platforms like Reddit employ “threads” to visually organize parallel discussions, but deep nesting obscures conversation flow. For moderators, this fragmentation compounds the difficulty of following evolving conversations and maintaining context across threads, which limits timely and effective moderation. In this paper, we present Needle, an interactive system that applies visual analytics to summarize key conversational metrics: activity, toxicity, and voting trends over time. Needle provides both high-level overviews and detailed breakdowns of threads, enabling moderators to identify priority areas without reading through entire nested conversations. Through a user study with ten Reddit moderators, we find that Needle provides a practical solution to maintain contextual understanding when navigating threaded discussions. Based on these findings, we propose design guidelines for future visualization-based tools that shape how people consume, interpret, and make sense of large-scale online discussions.

"Think about it like you're a firefighter": Understanding How Reddit Moderators Use the Modqueue

Tanvi Bajpai, Eshwar Chandrasekharan

CHI 2026 · Preprint (forthcoming)

On Reddit, the moderation queue (modqueue) is the platform’s primary interface for reviewing user-reported and automatically flagged content. Despite its central role in Reddit’s community reliant moderation model, little is known about how moderators actually use it in practice. To address this gap, we surveyed 110 moderators, who collectively oversee more than 400 subreddits, to understand how the modqueue fits into their workflows and what its design enables or constrains. We find substantial variation in modqueue use: some moderators treat it as a daily checklist, others use it to identify patterns or emerging issues, and many routinely leave the interface to gather additional context or coordinate with teammates. Respondents also described persistent challenges, including coordination issues such as collisions, incomplete or noisy information signals, and friction created by fragmented interface versions and reliance on third-party tools. Taken together, we show the modqueue is neither a one-size-fits-all solution nor sufficient on its own for supporting moderator review. We outline opportunities for more modular, better-integrated moderation infrastructures that support both item-level review and broader governance activities, and that better align with the collaborative and value-driven nature of volunteer moderation on Reddit.

Masking or Mitigating? Deconstructing the Impact of Query Rewriting on Retriever Biases in RAG

Agam Goyal, Koyel Mukherjee, Apoorv Saxena, Anirudh Phukan, Eshwar Chandrasekharan, Hari Sundaram

ACL 2026 (Findings) · Preprint (forthcoming)

Dense retrievers in retrieval-augmented generation (RAG) systems exhibit systematic biases—including brevity, position, literal matching, and repetition biases—that can compromise retrieval quality. Query rewriting techniques are now standard in RAG pipelines, yet their impact on these biases remains unexplored. We present the first systematic study of how query enhancement techniques affect dense retrieval biases, evaluating five methods across six retrievers. Our findings reveal that simple LLM-based rewriting achieves the strongest aggregate bias reduction (54%), yet fails under adversarial conditions where multiple biases combine. Mechanistic analysis uncovers two distinct mechanisms: simple rewriting reduces bias through increased score variance, while pseudo-document generation methods achieve reduction through genuine decorrelation from bias-inducing features. However, no technique uniformly addresses all biases, and effects vary substantially across retrievers. Our results provide practical guidance for selecting query enhancement strategies based on specific bias vulnerabilities. More broadly, we establish a taxonomy distinguishing query-document interaction biases from document encoding biases, clarifying the limits of query-side interventions for debiasing RAG systems.

Uncovering the Internet’s Hidden Values: An Empirical Study of Desirable Behavior Using Highly-Upvoted Reddit Content

Agam Goyal, Charlotte Lambert, Yoshee Jain, Eshwar Chandrasekharan

ICWSM 2026 · Preprint (forthcoming)

A major task for moderators of online spaces is norm-setting, essentially creating shared norms for user behavior in their communities. Platform design principles emphasize the importance of highlighting norm-adhering examples and explicitly stating community norms. However, norms and values vary between communities and go beyond content-level attributes, making it challenging for platforms and researchers to provide automated ways to identify desirable behavior to be highlighted. Current automated approaches to detect desirability are limited to measures of prosocial behavior, but we do not know whether these measures fully capture the spectrum of what communities value. In this paper, we use upvotes, which express community approval, as a proxy for desirability and examine 16,000 highly-upvoted comments across 80 popular sub-communities on Reddit. Using a large language model, we extract values from these comments across two years (2016 and 2022) and compile 64 and 72 macro, meso, and micro values for 2016 and 2022 respectively, based on their frequency across communities. Furthermore, we find that existing computational models for measuring prosociality were inadequate to capture on average 82% of the values we extracted. Finally, we show that our approach can not only extract most of the qualitatively-identified values from prior taxonomies, but also uncover new values that are actually encouraged in practice. Our findings highlight the need for nuanced models of desirability that go beyond preexisting prosocial measures. This work has implications for improving moderator understanding of their community values and provides a framework that can supplement qualitative approaches with larger-scale content analyses.

Examining Algorithmic Curation on Social Media: An Empirical Audit of Reddit’s r/popular Feed

Jackie Chan, Frederick Choi, Koustuv Saha, Eshwar Chandrasekharan

ICWSM 2026 · Preprint (forthcoming)

Platforms are increasingly relying on algorithms to curate the content within users' social media feeds. However, the growing prominence of proprietary, algorithmically curated feeds has concealed what factors influence the presentation of content on social media feeds and how that presentation affects user behavior. This lack of transparency can be detrimental to users, from reducing users' agency over their content consumption to the propagation of misinformation and toxic content. To uncover details about how these feeds operate and influence user behavior, we conduct an empirical audit of Reddit's algorithmically curated trending feed called r/popular. Using 10K r/popular posts collected by taking snapshots of the feed over 11 months, we find that recent comments help a post remain on r/popular longer and climb the feed. We also find that posts below rank 80 correspond to a sharp decline in activity compared to posts above. When examining the effects of having a higher proportion of undesired behavior—i.e., moderator-removed and toxic comments—we find no significant evidence that it helps posts stay on r/popular for longer. Although posts closer to the top receive more undesired comments, we find this increase to coincide with a broader increase in overall engagement—rather than indicating a disproportionate effect on undesired activity. The relationships between algorithmic rank and engagement highlight the extent to which algorithms employed by social media platforms essentially determine which content is prioritized and which is not. We conclude by discussing how content creators, consumers, and moderators on social media platforms can benefit from empirical audits aimed at improving transparency in algorithmically curated feeds.

Does Positive Reinforcement Work?: A Quasi-Experimental Study of the Effects of Positive Feedback on Reddit

Charlotte Lambert, Koustuv Saha, Eshwar Chandrasekharan

CHI 2025 · Paper

Social media platform design often incorporates explicit signals of positive feedback. Some moderators provide positive feedback with the goal of positive reinforcement, but are often unsure of their ability to actually influence user behavior. Despite its widespread use and theory touting positive feedback as crucial for user motivation, its effect on recipients is relatively unknown. This paper examines how positive feedback impacts Reddit users and evaluates its differential effects to understand who benefits most from receiving positive feedback. Through a causal inference study of 11M posts across 4 months, we find that users who received positive feedback made more frequent (2% per day) and higher quality (57% higher score; 2% fewer removals per day) posts compared to a set of matched control users. Our findings highlight the need for platforms and communities to expand their perspective on moderation and complement punitive approaches with positive reinforcement strategies.

Creator Hearts: Investigating the Impact Positive Signals from YouTube Creators in Shaping Comment Section Behavior

Fred Choi, Charlotte Lambert, Vinay Koshy, Sowmya Pratipati, Tue Do, Eshwar Chandrasekharan

CHI 2025 · Paper

Much of the research in online moderation focuses on punitive actions. However, emerging research has shown that positive reinforcement is effective at encouraging desirable behavior on online platforms. We extend this research by studying the “creator heart” feature on YouTube, quantifying their primary effects on comments that receive hearts and on videos where hearts have been given out by creators. We find that creator hearts increased the visibility of comments, and increased the amount of positive engagement they received from other users. We also find that the presence of a creator-hearted comment soon after a video is published can incentivize viewers to comment, increasing the total engagement with the video over time. We discuss how creators can use hearts to shape behavior in their communities by highlighting, rewarding, and incentivizing desirable behaviors from users. We discuss avenues for extending our study to understanding positive signals from moderators on other platforms.

SLM-Mod: Small Language Models Surpass LLMs at Content Moderation

Xianyang Zhan*, Agam Goyal*, Yilun Chen, Eshwar Chandrasekharan, Koustuv Saha

NAACL 2025 (main conf.) · Paper

Large language models (LLMs) have shown promise in many natural language understanding tasks, including content moderation. However, these models can be expensive to query in real-time and do not allow for a community-specific approach to content moderation. To address these challenges, we explore the use of open-source small language models (SLMs) for community-specific content moderation tasks. We fine-tune and evaluate SLMs (less than 15B parameters) by comparing their performance against much larger open- and closed-sourced models in both a zero-shot and few-shot setting. Using 150K comments from 15 popular Reddit communities, we find that SLMs outperform zero-shot LLMs at content moderation—11.5% higher accuracy and 25.7% higher recall on average across all communities. Moreover, few-shot in-context learning shows only a marginal increase in the performance of LLMs, still lacking compared to SLMs. We further show the promise of cross-community content moderation, which has implications for new communities and the development of cross-platform moderation techniques. Finally, we outline directions for future work on language model based content moderation.

The Chilling: Identifying Strategic Antisocial Behavior Online and Examining the Impact on Journalists

Yian Wang, Mukhilshankar Umashankar, Eshwar Chandrasekharan, Hari Sundaram

CSCW 2025 · Preprint (forthcoming)

Two key challenges in identifying strategic online behavior are the complex structure of online conversations and the hidden nature of potential strategies that drive user behavior. To address these, we develop a new tree-structured Transformer model that categorizes replies based on their hierarchical conversation structures, offering insights into the latent strategies underlying these interactions. Extensive experiments demonstrate that our proposed classification model can effectively detect different user groups—namely attackers, supporters, and bystanders—and their latent strategies. To demonstrate the utility of our approach, we apply this classifier to real-time Twitter data and conduct a series of quantitative analyses on the interactions between journalists with diverse cultural backgrounds and different groups of users—attackers, supporters, and bystanders. Our classification approach allows us to not only explore strategic behaviors of attackers but also those of supporters and bystanders who engage in online interactions. When examining the impact of online attacks, we find a strong correlation between the presence of attackers’ interactions and chilling effects, where journalists tend to slow their subsequent posting behavior. Additionally, we find that attackers tend to negatively influence the posting behavior of other users within these conversations. As conversations deepen, replies often deviate from original posts and get more toxic.

Venire: A Machine Learning-Guided Panel Review System for Community Content Moderation

Vinay Koshy, Frederick Choi, Yi-Shyuan Chiang, Hari Sundaram, Eshwar Chandrasekharan, Karrie Karahalios

CSCW 2025 · Preprint

Research into community content moderation often assumes that moderation teams govern with a single, unified voice. However, recent work has found that moderators disagree with one another at modest, but concerning rates. The problem is not the root disagreements themselves. Subjectivity in moderation is unavoidable, and there are clear benefits to including diverse perspectives within a moderation team. Instead, the crux of the issue is that, due to resource constraints, moderation decisions end up being made by individual decision-makers. The result is decision-making that is inconsistent, which is frustrating for community members. To address this, we develop Venire, an ML-backed system for panel review on Reddit. Venire uses a machine learning model trained on log data to identify the cases where moderators are most likely to disagree. Venire fast-tracks these cases for multi-person review. Ideally, Venire allows moderators to surface and resolve disagreements that would have otherwise gone unnoticed. We conduct three studies through which we design and evaluate Venire: a set of formative interviews with moderators, technical evaluations on two datasets, and a think-aloud study in which moderators used Venire to make decisions on real moderation cases. Quantitatively, we demonstrate that Venire is able to improve decision consistency and surface latent disagreements. Qualitatively, we find that Venire helps moderators resolve difficult moderation cases more confidently. Venire represents a novel paradigm for human-AI content moderation, and shifts the conversation from replacing human decision-making to supporting it.

MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance

Agam Goyal, Xianyang Zhan, Yilun Chen, Koustuv Saha, Eshwar Chandrasekharan

EMNLP 2025 (main conf.) · Preprint (forthcoming)

Large language models (LLMs) have shown great potential in flagging harmful content in online communities. Yet, existing approaches for moderation require a separate model for every community and are opaque in their decision-making, limiting real-world adoption. We introduce Mixture of Moderation Experts (MoMoE), a modular, cross-community framework that adds post-hoc explanations to scalable content moderation. MoMoE orchestrates four operators—Allocate, Predict, Aggregate, Explain—and is instantiated as seven community-specialized experts (MoMoE-Community) and five norm-violation experts (MoMoE-NormVio). On 30 unseen subreddits, the best variants obtain Micro-F1 scores of 0.72 and 0.67, respectively, matching or surpassing strong fine-tuned baselines while consistently producing concise and reliable explanations. Although community-specialized experts deliver the highest peak accuracy, norm-violation experts provide steadier performance across domains. These findings show that MoMoE yields scalable, transparent moderation without needing per-community fine-tuning. More broadly, they suggest that lightweight, explainable expert ensembles can guide future NLP and HCI research on trustworthy human-AI governance of online communities.

ArgCMV: An Argument Summarization Benchmark for the LLM-era

Omkar Gurjar, Agam Goyal, Eshwar Chandrasekharan

EMNLP 2025 (main conf.) · Preprint (forthcoming)

Key point extraction is an important task in argument summarization which involves extracting high-level short summaries from arguments. Existing approaches for KP extraction have been mostly evaluated on the popular ArgKP21 dataset. In this paper, we highlight some of the major limitations of the ArgKP21 dataset and demonstrate the need for new benchmarks that are more representative of actual human conversations. Using SoTA large language models (LLMs), we curate a new argument key point extraction dataset called ArgCMV comprising of around 12K arguments from actual online human debates spread across over 3K topics. Our dataset exhibits higher complexity such as longer, co-referencing arguments, higher presence of subjective discourse units, and a larger range of topics over ArgKP21. We show that existing methods do not adapt well to ArgCMV and provide extensive benchmark results by experimenting with existing baselines and latest open source models. This work introduces a novel KP extraction dataset for long-context online discussions, setting the stage for the next generation of LLM-driven summarization research.

Understanding Community Resilience: Quantifying the Effects of Sudden Popularity via Algorithmic Curation

Jackie Chan, Charlotte Lambert, Frederick Choi, Stevie Chancellor, Eshwar Chandrasekharan

ICWSM 2024 · Paper

The sudden popularity communities receive via algorithmically-curated "trending" or "hot" social media feeds can be beneficial or disruptive. On one hand, increased attention often brings new users and promotes community growth. On the other hand, the unexpected influx of newcomers can burden already overworked moderation teams. To examine the impact of sudden popularity, we studied 6,306 posts that reached Reddit's front page—a feed called r/popular that millions of users browse daily—and the effects of sudden popularity within 1,320 subreddits. We find that on average, r/popular posts have 45 times the comments, 42 times the removed comments, and 70 times the number of newcomers compared to posts that did not reach r/popular from the same community. Additionally, r/popular posts led to a peak 85% median increase in the subreddit's comment rate, and these effects lingered for about 12 hours. Our regression analysis shows that stricter moderation and previous r/popular appearances were associated with shortened and less intense effects on the community. By quantifying the differential effects of sudden popularity, we provide recommendations for moderators to promote stability and resilience in the face of unexpected disruptions.

"Positive reinforcement helps breed positive behavior": Moderator Perspectives on Encouraging Desirable Behavior

Charlotte Lambert, Frederick Choi, Eshwar Chandrasekharan

CSCW 2024 · Paper

The role of a moderator is often characterized as solely punitive, however, moderators have the power to not only execute reactive and punitive actions but also create norms and support the values they want to see within their communities. One way moderators can proactively foster healthy communities is through positive reinforcement, but we do not currently know whether moderators on Reddit enforce their norms by providing positive feedback to desired contributions. To fill this gap in our knowledge, we surveyed 115 Reddit moderators to build two taxonomies: one for the content and behavior that actual moderators want to encourage and another taxonomy of actions moderators take to encourage desirable contributions. We found that prosocial behavior, engaging with other users, and staying within the topic and norms of the subreddit are the most frequent behaviors that moderators want to encourage. We also found that moderators are taking actions to encourage desirable contributions, specifically through built-in Reddit mechanisms (e.g., upvoting), replying to the contribution, and explicitly approving the contribution in the moderation queue. Furthermore, moderators reported taking these actions specifically to reinforce desirable behavior to the original poster and other community members, even though many of the actions are anonymous, so the recipients are unaware that they are receiving feedback from moderators. Importantly, some moderators who do not currently provide feedback do not object to the practice. Instead, they are discouraged by the lack of explicit tools for positive reinforcement and the fact that their fellow moderators are not currently engaging in methods for encouragement. We consider the taxonomy of actions moderators take, the reasons moderators are deterred from providing encouragement, and suggestions from the moderators themselves to discuss implications for designing tools to provide positive feedback.

Measuring epistemic trust: Towards a new lens for democratic legitimacy, misinformation, and echo chambers

Dominic Zaun Eu Jones, Eshwar Chandrasekharan

CSCW 2024 · Paper

Trust is crucial for the functioning of complex societies. Testimony, from one speaker to another, underlies many social systems. Epistemic trust, or testimonial credibility, is the likelihood to accept a speaker's claim due to belief on their competence or sincerity. Epistemic trust is closely related to several `pathological epistemic phenomena': democratic (il)legitimacy, the spread of misinformation, and echo chambers. To the best of our knowledge, this theoretical contribution is novel in the field of social computing. We further argue that epistemic trust is no philosophical novelty: it is measurable. Weakly supervised text classification approaches achieve F1 scores of around 80 to 85 per cent on detecting epistemic distrust. This is also, to the best of our knowledge, a novel task in natural language processing. We measure expressions of epistemic distrust across 954 political communities on Reddit. We find that expressions of epistemic distrust are relatively rare, although there are substantial differences between communities. Conspiratorial communities and those focused on controversial political topics tend to express more distrust. Communities with strong epistemic norms enforced by moderation are likely to express low levels. While we find users to be an important potential source of contagion of epistemic distrust, community norms appear to dominate. It is likely that epistemic trust is more useful as an aggregated risk factor. Finally, we argue that policymakers should be aware of epistemic trust considering their reliance on legitimacy underwritten by testimony.

Opportunities, Tensions, and Challenges in Computational Approaches to Addressing Online Harassment

Evey Huang, Abhraneel Sarma, Sohyeon Hwang, Eshwar Chandrasekharan, Stevie Chancellor

DIS 2024 · Paper (Honorable Mention!)

Given the scale at which online harassment occurs, researchers and practitioners alike have turned to computationally driven approaches to address it. However, because harassment is highly contextual and personal, designing effective solutions to this problem can be extremely challenging. This paper examines how harassment-mitigation systems studied in human-computer interaction (HCI) consider victim-centered principles in their design. Through a scoping literature review and close reading of 17 papers, we contribute: (1) a characterization of how novel and existing systems consider victims' identity characteristics, definitions of harassment, and preferred strategies for dealing with harassment; (2) challenges faced by the systems along these dimensions to surface limitations, gaps, and tensions; (3) practical recommendations for researchers, designers, and practitioners to overcome these challenges. In doing so, we offer potential new directions to positively design computational approaches to addressing online harassment with victim-centered principles in mind.

ConvEx: A Visual Conversation Exploration System for Discord Moderators

Frederick Choi, Tanvi Bajpai, Sowmya Pratipati, Eshwar Chandrasekharan

CSCW 2023 · Paper

Moderators are at the core of maintaining healthy online communities. For these moderators, who are often volunteers from the community, filtering through content and responding to misbehavior on time has become increasingly challenging as online communities continue to grow. To address such challenges of scale, recent research has looked into designing better tools for moderators of various platforms (e.g. Reddit, Twitch, Facebook, and Twitter). In this paper, we focus on Discord, a platform where communities are typically involved in large, synchronous group chats, creating an environment with a faster pace and a lack of structure compared to previously studied platforms. To tackle the unique challenges presented by Discord, we developed a new human-AI system called ConvEx for exploring online conversations. ConvEx is an AI-augmented version of the standard Discord interface designed to help moderators be proactive in identifying and preventing potential problems. It provides visual embeddings of conversational metrics, such as activity and toxicity levels, and can be extended to visualize other metrics. Through a user study with eight active moderators of Discord servers, we found that ConvEx supported several high-level strategies in monitoring a server and analyzing conversations. ConvEx allowed moderators to obtain a holistic view of activity across multiple channels on the server while guiding their attention towards problematic conversations and messages in a channel, helping them identify important contextual information to obtain reliable information from the AI analysis while also being able to pick up on contextual nuances which the AI missed. We conclude with design considerations for integrating AI into future interfaces for moderating synchronous, unstructured online conversations.

Measuring User-Moderator Alignment on r/ChangeMyView

Vinay Koshy, Tanvi Bajpai, Eshwar Chandrasekharan, Hari Sundaram, Karrie Karahalios

CSCW 2023 · Paper (Best Paper Award!)

Social media sites like Reddit, Discord, and Clubhouse utilize a community-reliant approach to content moderation. Under this model, volunteer moderators are tasked with setting and enforcing content rules within the platforms' sub-communities. However, few mechanisms exist to ensure that the rules set by moderators reflect the values of their community. Misalignments between users and moderators can be detrimental to community health. Yet little quantitative work has been done to evaluate the prevalence or nature of user-moderator misalignment. Through a survey of 798 users on r/ChangeMyView, we evaluate user-moderator alignment at the level of policy-awareness (does users know what the rules are?), practice-awareness (do users know how the rules are applied?) and policy-/practice-support (do users agree with the rules and how they are applied?. We find that policy-support is high, while practice-support is low -- using a hierarchical Bayesian model we estimate the correlation between community opinion and moderator decisions to range from .14 to .45 across subreddit rules. Surprisingly, these correlations were only slightly higher when users were asked to predict moderator actions, demonstrating low awareness of moderation practices. Our findings demonstrate the need for careful analysis of user-moderator alignment at multiple levels. We argue that future work should focus on building tools to empower communities to conduct these analyses themselves.

Conversational Resilience: Quantifying and Predicting Conversational Outcomes Following Adverse Events

Charlotte Lambert, Ananya Rajagopal, Eshwar Chandrasekharan

ICWSM 2022 · Paper

Online conversations, just like offline ones, are susceptible to influence by bad actors. These users have the capacity to derail neutral or even prosocial discussions through adverse behavior. Moderators and users alike would benefit from more resilient online conversations, i.e., those that can survive the influx of adverse behavior to which many conversations fall victim. In this paper, we examine the notion of conversational resilience: what makes a conversation more or less capable of withstanding an adverse interruption? Working with 11.5M comments from eight mainstream subreddits, we compiled more than 5.8M comment threads (i.e., conversations). Using 239K relevant conversations, we examine how well comment, user and subreddit characteristics can predict conversational outcomes. More than half of all conversations proceed after the first adverse event. Six out of ten conversations that proceed result in future removals. Comments violating platform-wide norms and those written by authors with a history of norm violations lead to not only more norm violations, but also fewer prosocial outcomes. However, conversations in more populated subreddits and conversations where the first adverse event's author was initially a strong contributor are capable of minimizing future removals and promoting prosocial outcomes after an adverse event. By understanding factors that contribute to conversational resilience we shed light onto what types of behavior can be encouraged to promote prosocial outcomes even in the face of adversity.

Harmonizing the Cacophony with MIC: An Affordance-aware Framework for Platform Moderation

Tanvi Bajpai, Drshika Asher, Anwesa Goswami, Eshwar Chandrasekharan

CSCW 2022 · Paper

Online social platforms are evolving at a rapid pace. With the addition of new features like real-time audio, the landscape of online communities and moderation work on these communities is being out-paced by platform development. In this paper, we present a novel framework that allows us to represent the dynamic moderation ecosystems of social platforms using a base-set of 12 platform-level affordances, along with inter-affordance relationships. These affordances fall into the three categories: Members, Infrastructure, and Content. We call this the MIC framework, and apply MIC to analyze several social platforms in two case studies. First we analyze individual platforms using MIC and demonstrate how MIC can be used to examine the effects of platform changes on the moderation ecosystem and identify potential new challenges in moderation. Next, we systematically compare three platforms using MIC and propose potential moderation mechanisms that platforms can adapt from one another. Moderation researchers and platform designers can use such comparisons to uncover where platforms can emulate established, successful and better-studied platforms, as well as learn from the pitfalls other platforms have encountered.

Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit

Eshwar Chandrasekharan, Shagun Jhaver, Amy Bruckman, Eric Gilbert

TOCHI 2021 · Paper (Editor's pick for Notable Paper!)

Should social media platforms override a community's self-policing when it repeatedly break rules? What actions can they consider? In light of this debate, platforms have begun experimenting with softer alternatives to outright bans. We examine one such intervention called quarantining, that impedes direct access to and promotion of controversial communities. Specifically, we present two case studies of what happened when Reddit quarantined the influential communities r/TheRedPill (TRP) and r/The_Donald (TD). Using over 85M Reddit posts, we apply causal inference methods to examine the quarantine's effects on TRP and TD. We find that the quarantine made it more difficult to recruit new members: new user influx to TRP and TD decreased by 79.5% and 58%, respectively. Despite quarantining, existing users' misogyny and racism levels remained unaffected. We conclude by reflecting on the effectiveness of this design friction in limiting the influence of toxic communities and discuss broader implications for content moderation.

Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations

Jiajun Bao, Junjie Wu, Yiming Zhang, Eshwar Chandrasekharan and David Jurgens

WWW 2021 · Paper

Online conversations can go in many directions: some turn out poorly due to antisocial behavior, while others turn out positively to the benefit of all. Research on improving online spaces has focused primarily on detecting and reducing antisocial behavior. Yet we know little about positive outcomes in online conversations and how to increase them—is a prosocial outcome simply the lack of antisocial behavior or something more? Here, we examine how conversational features lead to prosocial outcomes within online discussions. We introduce a series of new theory-inspired metrics to define prosocial outcomes such as mentoring and esteem enhancement. Using a corpus of 26M Reddit conversations, we show that these outcomes can be forecasted from the initial comment of an online conversation, with the best model providing at relative 24% improvement over human forecasting performance at ranking conversations for predicted outcome. Our results indicate that platforms can use these early cues in their algorithmic ranking of early conversations to prioritize better outcomes.

Still out there: Modeling and Identifying Russian Troll Accounts on Twitter

Jane Im, Eshwar Chandrasekharan, Jackson Sargent, Paige Lighthammer, Taylor Denby, Ankit Bhargava, Libby Hemphill, David Jurgens, Eric Gilbert

WebSci 2020 · Paper (Best Paper Runner Up!)

There is evidence that Russia's Internet Research Agency attempted to interfere with the 2016 U.S. election by running fake accounts on Twitter—often referred to as "Russian trolls". In this work, we: 1) develop machine learning models that predict whether a Twitter account is a Russian troll within a set of 170K control accounts; and, 2) demonstrate that it is possible to use this model to find active accounts on Twitter still likely acting on behalf of the Russian state. Using both behavioral and linguistic features, we show that it is possible to distinguish between a troll and a non-troll with a precision of 78.5% and an AUC of 98.9%, under cross-validation. Applying the model to out-of-sample accounts still active today, we find that up to 2.6% of top journalists' mentions are occupied by Russian trolls. These findings imply that the Russian trolls are very likely still active today.

Synthesized Social Signals: Computationally-Derived Social Signals from Account Histories

Jane Im, Sonali Tandon, Eshwar Chandrasekharan, Taylor Denby, Eric Gilbert

CHI 2020 · Paper

In this paper, we propose a new idea called synthesized social signals (S3s): social signals computationally derived from an account's history, and then rendered into the profile. To demonstrate and explore the concept, we built Sig, an extensible Chrome extension that computes and visualizes S3s. Results from field deployments show that Sig reduced receiver costs, added important signals beyond conventionally available ones, and that a few users felt safer using Twitter as a result.

Combatting Abusive Behavior in Online Communities Using Cross-Community Learning

Georgia Tech · Thesis

Defended PhD thesis on March 3, 2020. Graduated with a PhD in CS from Georgia Tech on May 1, 2020.

Crossmod: A Cross-Community Learning-based System to Assist Reddit Moderators

Eshwar Chandrasekharan, Chaitrali Gandhi, Matthew Wortley Mustelier, Eric Gilbert

CSCW 2019 · Paper

In this paper, we introduce a novel sociotechnical moderation system for Reddit called Crossmod. Through formative interviews with 11 active moderators from 10 different subreddits, we learned about the limitations of currently available automated tools, and build a new system that extends their capabilities. To the best of our knowledge, Crossmod is the first open source, AI-backed sociotechnical moderation system to be designed using participatory methods.

A Just and Comprehensive Strategy for Using NLP to Address Online Abuse

David Jurgens, Eshwar Chandrasekharan, Libby Hemphill

ACL 2019 · Paper

Online abusive behavior affects millions and the NLP community has attempted to mitigate this problem by developing technologies to detect abuse. However, current methods have largely focused on a narrow definition of abuse to detriment of victims who seek both validation and solutions. In this position paper, we argue that the community needs to make three substantive changes: (1) expanding our scope of problems to tackle both more subtle and more serious forms of abuse, (2) developing proactive technologies that counter or inhibit abuse before it harms, and (3) reframing our effort within a framework of justice to promote healthy communities.

Prevalence and Psychological Effects of Hateful Speech in Online College Communities

Koustuv Saha, Eshwar Chandrasekharan, Munmun De Choudhury

WebSci 2019 · Paper

We employ a causal-inference framework to study the psychological effects of hateful speech in these college subreddits, particularly in the form of individuals’ online stress expression. Our findings suggest that exposure to hate leads to greater stress expression. However, everybody exposed is not equally affected; some show lower psychological endurance to hate than others. Low endurance individuals are more vulnerable to emotional outbursts, and are more neurotic than those with higher endurance.

Hybrid Approaches to Detect Comments Violating Macro Norms on Reddit

Eshwar Chandrasekharan, Eric Gilbert

(under submission) · Paper on arXiv · Dataset

In this dataset paper, we present a three-stage process to collect Reddit comments that are removed comments by moderators of several subreddits, for violating subreddit rules and guidelines. Working with over 2.8M removed comments collected from 100 different communities on Reddit, we identify 8 macro norms (i.e., norms that are widely enforced on most parts of Reddit). We extract these macro norms by employing a hybrid approach (classification, topic modeling, and open-coding), on comments identified to be norm violations within at least 85 out of the 100 study subreddits. Finally, we label over 40K Reddit comments removed by moderators according to the specific type of macro norm being violated, and make this dataset publicly available.

The Internet’s Hidden Rules: An Empirical Study of Reddit Norm Violations at Micro, Meso, and Macro Scales

Eshwar Chandrasekharan, Mattia Samory, Shagun Jhaver, Hunter Charvat, Amy Bruckman, Cliff Lampe, Jacob Eisenstein, Eric Gilbert

CSCW 2018 · Paper

In this paper, we study community norms on Reddit in a large-scale, empirical manner. Via 2.8M comments removed by moderators of 100 top subreddits over 10 months, we use both computational and qualitative methods to identify three types of norms: macro norms that are universal to most parts of Reddit; meso norms that are shared across certain groups of subreddits; and micro norms that are specific to individual, relatively unique subreddits. Given the size of Reddit’s user base we argue this represents the first large-scale study of norms across disparate online communities. In other words, these findings shed light on what Reddit values, and how widely-held those values are.

You Can't Stay Here: The Efficacy of Reddit's 2015 Ban Examined Through Hate Speech

Eshwar Chandrasekharan, Umashanthi Pavalanathan, Anirudh Srinivasan, Adam Glynn, Jacob Eisenstein, Eric Gilbert

CSCW 2017 · Paper

In 2015, Reddit closed several subreddits—foremost among them r/fatpeoplehate and r/CoonTown—due to violations of Reddit's anti-harassment policy. However, the effectiveness of banning as a moderation approach remains unclear: banning might diminish hateful behavior, or it may relocate such behavior to different parts of the site. We study the ban of r/fatpeoplehate and r/CoonTown in terms of its effects on both participating users and affected subreddits. Working from over 100M Reddit posts and comments, we generate hate speech lexicons to examine variations in hate speech usage via causal inference methods. We find that the ban worked for Reddit. More accounts than expected discontinued using the site; those that stayed drastically decreased their hate speech usage—atleast by 80%. Though many subreddits saw an influx of r/fatpeoplehate and r/CoonTown "migrants", those subreddits saw no significant change in hate speech usage. In other words, other subreddits did not inherit the problem.

The Bag of Communities: Identifying Abusive Behavior Online with Preexisting Internet Data

Eshwar Chandrasekharan, Mattia Samory, Anirudh Srinivasan, Eric Gilbert

CHI 2017 · Paper

We introduce a novel computational approach to address this problem called Bag of Communities (BoC)—a technique that leverages large-scale, preexisting data from other Internet communities. Using this conceptual and empirical work, we argue that the BoC approach may allow communities to deal with a range of common problems, like abusive behavior, faster and with fewer engineering resources.

Situated Anonymity: Impacts of Anonymity, Ephemerality, and Hyper-Locality

Ari Schlesinger, Eshwar Chandrasekharan, Christina Masden, Amy Bruckman, W Keith Edwards, Rebecca Grinter

CHI 2017 · Paper

We conducted an interview-based study to examine the factors that were integral to the success and popularity of Yik Yak during its initial deployment, by interviewing 18 Yik Yak users on an urban university campus.

Footprints on Silicon: Explorations in Gathering Autobiographical Content

Eshwar Chandrasekharan, Sutanu Chakraborti

CICLing (IJCLA) 2015 · Paper

We built a system that can identify emails containing autobiographical content for aiding the autobiographical summarization of a user’s mail Inbox, over the years. This data can be used to generate a story about the user’s life or an autobiography of sorts.