140 papers. Citation counts refresh weekly from Google Scholar.
Can LLMs do stance detection on tweets without any fine-tuning? Using FlanT5-XXL with SemEval 2016 and P-Stance datasets, we find zero-shot prompting matches or beats fine-tuned baselines. Performance varies with prompt phrasing, decoding strategy, and a measurable positivity bias, and we surface cases where the LLM over-commits to a stance when none is actually present.
Innocuous online preferences (the bands you follow, the slang you use) can reveal sensitive traits. We show that LLMs reliably infer hidden political alignment from DebateOrg and Reddit conversations, outperforming traditional ML. Accuracy improves further when text-level inferences aggregate to the user level and when we use politics-adjacent domains, underscoring a serious privacy risk.
LLMs show cognitive biases in behavior, but where are those biases encoded internally, and can they be removed? We introduce CogBias, a benchmark covering four bias families (Judgment, Information Processing, Social, Response), and show via linear probes that biases occupy separable directions in activation space. Activation steering reduces bias 26 to 32% with minimal capability loss.
Clinical research finds Western depression patients tend to report psychological symptoms while Eastern patients report somatic ones. We test whether LLMs (now used in mental health) reproduce this cultural pattern. They mostly don't in English; prompting in Chinese, Japanese, or Hindi helps somewhat, but a strong culturally-invariant symptom hierarchy overrides the cultural cues.
How consistently do LLMs apply ethical frameworks across reasoning steps? We introduce moral reasoning trajectories (sequences of framework invocations) and analyze them across six models. Over half of consecutive steps switch frameworks; only ~17% of trajectories stay consistent. Unstable trajectories are more vulnerable to persuasive attacks, and linear probes localize framework encoding to specific layers.
Mental-health symptom datasets are slow and expensive to build, limiting how well models generalize to real, diverse user posts. SynSym is an LLM-based framework that generates synthetic symptom training data by expanding each symptom into sub-concepts, varying linguistic style, and modeling co-occurrence. Models trained on SynSym data match or exceed those trained on real data.
When AI joins a creative collaboration, does behaving cooperatively erase identity-based bias against it? In a turn-based collaborative drawing study (N=30), participants worked with a human confederate believed to be either human or AI. Cooperative behavior improved overall perceptions, but creativity ratings consistently favored human partners despite identical behavior. Identity bias persists.
When LLM agents settle into cooperation, have they formed a norm or merely converged? We measure what agents expect of each other, not just what they do, and isolate social learning and social selection across four LLM families. Similar cooperative outcomes turn out to rest on different social processes that behavior alone cannot tell apart.
Different online communities have different rules: what gets you banned from one subreddit may be the norm in another. PluRule is a multimodal, multilingual benchmark with 13,371 rule violations across 1,989 Reddit communities, 2,885 rules, and 9 languages. Even GPT-5.2 performs only slightly better than a trivial baseline, exposing pluralistic moderation as a fundamental challenge.
How does public discourse around COVID-19 vaccines differ between the United States and South Korea? Analyzing nearly 8,000 question-answer pairs from Quora and Naver Knowledge-iN, we apply NLP to surface topical and sentiment differences. Both countries share concerns about side effects and efficacy, but diverge sharply on which questions get most attention.
Did 1945 change how Japan talked about gender? We train a word embedding for every year from 1900 to 1998 and trace stereotype strength across Home, Work, and Politics. Work and Politics reverse direction around 1945, turning female-associated after decades of drifting the other way, while Home stays flat throughout.
Deepfake harm depends on the moment a viewer perceives the synthetic face as a specific victim, but where that threshold sits is undefined. With 102 participants and 162 manipulated stimuli, we find eyes are the dominant identity cue, that pairing eyes with one more feature triggers identity perception, and that human-AI misalignment peaks exactly at that threshold.
Does a team of LLM agents need a leader? We turn transactional, transformational, and situational leadership into explicit controllers over a shared action set, tested across four task regimes and three model families. No controller dominates. Control pays off only when the round-0 majority is wrong and the task is recoverable.
How much interpretive room do a society's stories leave open? Narrative Entropy Theory treats narrative environments as ecosystems with more or less possibility for meaning. Across 475,716 news articles from 65 countries, our entropy measures track national indices of expressive freedom and explain over a third of the cross-national variation.
Urban 'vibrancy' (the dynamic human activity in a place) is well-measured by floating-population data but rarely fed into traffic models. We use VAEs to compress floating-population data into Urban Vibrancy embeddings, then forecast future embeddings with LSTMs and feed them into traffic models (RNN, DCRNN, GTS, GMAN), improving accuracy and responsiveness across the board.
AlphaGo was a spectacle, but what happens once AI becomes ordinary? Across a decade of Korean Go commentary on YouTube, roughly 1,900 hours, winrate graphs fill 98 percent of late broadcast time while only 2.63 percent of sentences mention AI. The metric stays, the source label goes unsaid, and with it the hooks for questioning it.
Riot Games runs the League of Legends World Championship and also patches the game just before it. Who checks where those late changes land? Across ten Worlds and 1,090 champion changes, nerfs fell hardest on teams built around the prevailing meta, with a median 23-point gap between the most and least affected teams each year.
AI-human alignment is usually measured by outcome agreement (accuracy, F1). XChoice fits a mechanism-based decision model to both human and LLM choices, recovering interpretable parameters: which factors matter, how sensitive each is to constraints, what trade-offs get made. Applied to U.S. time-use data, XChoice reveals systematic misalignment concentrated in specific demographic subgroups.
How do beliefs interconnect, and what drives a person to adopt new ones? We fine-tune large language models on online debate data to map thousands of beliefs into a semantic space where proximity reflects coherence. Position in that space predicts which beliefs an individual is likely to adopt next and quantifies cognitive dissonance via distance between existing and new beliefs.
Personalized AI agents must access private data (emails, chats, purchases) to be useful, but they can leak it. PrivacyBench is a benchmark with conversational scenarios containing embedded secrets. RAG assistants leak secrets in up to 26.6% of conversations; a privacy-aware prompt cuts this to 5.1%, but retrieval still pulls sensitive data indiscriminately.
Can LLMs realistically simulate public opinion across different cultures? Comparing DeepSeek-R1/V3 with Qwen2.5, GPT-4o, and Llama-3.3 against survey data from the U.S. (ANES) and China (Zuobiao), we find performance varies sharply by topic and demographic, and that all models over-generalize within demographic groups, defaulting to a 'representative' voice.
What does team science say about multi-agent LLM systems? We test three core team-science variables (structure, diversity, interaction dynamics) across four reasoning tasks. Flat teams beat hierarchical ones, diversity has nuanced effects, and agents are overconfident before tasks but more reflective afterward, with both appreciation for collaboration and difficulty integrating.
When an LLM predicts someone's beliefs, what matters more: who they are (demographics) or what else they believe (prior stances)? Testing open-weight LLMs on online-debate data under four conditions, we find both signals improve prediction, but their relative value varies sharply across belief domains.
How do Airbnb listings shift month-to-month across a city's neighborhoods? Using public datasets covering Seoul from 2017 to 2019, we model monthly variation as a function of housing supply, lodging density, tourist-attraction proximity, and retail activity, achieving strong fit (avg pseudo R² of 0.79) and identifying which factors drive short-term rental dynamics in each district.
Identity fusion (the psychological merging of self with a group, ideology, or brand) has historically been measured with surveys. CLIFS uses cognitive linguistics and LLMs to detect implicit metaphors in text and score fusion automatically, matching survey instruments while scaling. As a proof of concept, it improves violence risk assessment by more than 240%.
Forecasting regional Airbnb activity matters for housing policy, but typical models look at single listings at single time points. We forecast Revenue, Reservation Days, and Number of Reservations at the regional level by combining listing features with LLM-generated embeddings of urban accessibility and human mobility, cutting RMSE and MAE by roughly 48% versus standard baselines.
Do social-media check-ins reshape urban land value? Modeling three Seoul neighborhoods (Yeonnam-dong, Seongsu-dong, Gyeongridan-gil) that gentrified after going viral on Instagram, we show that adding online-popularity features to a spatial capital model outperforms physical-layout-only baselines, quantifying how Instagram visibility now factors into urban property prices.
What does the public actually think of generative AI? We analyze 3M tweets from 2019 to 2023 and find broad interest across occupations, not just tech. Sentiment is generally positive and tracks exposure, with one exception: illustrators are notably negative, reflecting concerns over training-data ethics.
Fact-checking every story is impossible. Instead, this survey reviews how to profile entire news outlets, so any article can be flagged the moment it appears. We argue that factuality and political bias should be modeled jointly rather than separately, and survey state-of-the-art across text, social context, and beyond.
As AI explains its own decisions in natural language, who should grade those explanations? We compare ChatGPT and human judgments on informativeness and clarity across binary, ternary, and 7-point scales, finding ChatGPT aligns well with humans on coarse-grained ratings, with paired comparison and dynamic prompting further improving alignment.
Anti-vaccine YouTube comments are often dismissed as background noise, but do they actually push viewers toward hesitancy? We find that highly-liked toxic comments measurably increase fear in later comments, with contagion running in both directions. The result has implications for public health messaging and how moderators handle 'just trolling.'
What happened to hate speech on Twitter after Elon Musk's takeover and the relaxation of moderation? On 10 million tweets, we document significant increases in hate targeting LGBTQ+ users and liberals, with cohesive hate communities forming around influential 'bridge' users. Ironically, embracing free-speech principles enabled hate against the very concept of free speech itself.
Detectors of AI-generated text are quietly competing against attacks that paraphrase or swap words. ToBlend goes further: it samples tokens randomly from an ensemble of LLMs, producing text that drops most detectors' performance significantly while human raters confirm quality holds. We also release a fine-tuned Llama-3.1 detector tuned to catch ToBlend output.
Stance detection usually looks at message text alone. We add Moral Foundations Theory features (theoretically-derived dimensions of an individual's moral concerns) on top of standard semantic features, using both traditional ML and LLMs. Encoding moral foundations improves classification and illuminates which moral foundations associate with which online stances.
Comparing knowledge graphs that represent sentence meaning is harder than it sounds: existing metrics are slow and miss semantic nuance. Rematch is a new AMR similarity metric that ranks first on semantic benchmarks (1 to 5 percentage points over state of the art) and runs five times faster.
Short-term transportation demand prediction guides scheduling, fleet sizing, and transit-network design. Most models use one or two areal features; cities need many (land use, sociodemographics, POI distribution). Our ST-MFGCRN model fuses multiple areal features via sentinel attention, beating state-of-the-art baselines by 7 to 8% on BusDJ and TaxiBJ datasets.
Mid-term road traffic prediction (a few hours out) matters for car-sharing and ride-hailing, but existing models focus on short-term effects between adjacent roads. We embed real-time regional knowledge (POIs, satellite images, LTE access traces) into a road-level attention model via dynamic convolution and bipartite spatial-transform attention, beating baseline approaches.
Implicit hate speech is hard for both detectors and explainers. We ask whether ChatGPT can produce useful natural-language explanations of why a subtle hateful post is hateful, and compare its output to human annotators through user studies. Promising for routine cases, but with consistent limitations on harder ones.
Most ChatGPT evaluations claim impressive numbers, but how trustworthy are they? Through a stance-detection case study, we show that training-set contamination silently inflates ChatGPT's apparent performance. We argue that fair evaluation of closed, continuously-trained models needs new conventions.
Generating clear explanations of why a post is hateful is hard, especially when hate is implicit. Our Chain of Explanation prompting method feeds the model heuristic words and the targeted group up front, lifting BLEU scores from 44.0 to 62.3 and producing more informative explanations on human evaluation.
Conspiracy theories are widely propagated on YouTube, yet the data needed to study them at scale is limited. YouNICon is a dataset of videos from channels previously identified as suspicious, supporting both conspiracy-theory detection and topic classification. The dataset enables studying trends in conspiracy theories and how individuals interact with such channels.
Fact-checkers can't keep up with COVID-19 misinformation. We study spontaneous debunking by regular Twitter users: which fake tweets attract corrections, and who corrects them. Building an annotated dataset and a classifier, we find most fake tweets are never debunked, spontaneous corrections are slower than other replies, and political topics show a clear partisan asymmetry.
If you know someone's view on masks, can you predict their view on Trump? Across three seemingly unrelated COVID-era topics, we train stance-detection models on Twitter and find that opinions cluster predictably: past stances on one issue strongly forecast future stances on another.
Traffic prediction usually relies on sensor networks that miss the human side of urban movement. We add human-activity data from the National Household Travel Survey to graph-convolutional traffic models, achieving state-of-the-art accuracy with minimal architectural changes.
Newly-elected leaders typically enjoy a brief 'honeymoon' of high approval. We test this on social media by analyzing 6.6M tweets across 8 Japanese prime-minister transitions over 15 years. The honeymoon does appear, but the user base flips dramatically with each change, and sentiment patterns vary by topic and PM.
What makes a news post go viral, and on which platform? Analyzing 3M posts from 53 outlets and 50M comments across 5 platforms over 8 months, we trace how sentiment and topic shape four levels of engagement (views, likes, comments, cross-platform sharing) and predict cross-platform diffusion with up to 83% F1.
How does offline political speech translate into online mobilization? We combine the text of Trump's speeches leading up to January 6, 2021 with Twitter data from QAnon hashtags over the same three days, using mixed quantitative and qualitative analysis. The results show measurable links between specific addresses and online participation.
How do emotions in news posts ripple into audience reactions? Across 133K posts and 2.8M comments from 8 outlets on 4 platforms over 8 months, we score 9 emotions (Plutchik's wheel) and predict reader emotional engagement before and after a post goes live, reaching over 70% F1 for some emotions and revealing systematic platform differences.
Most toxicity research focuses on Western online communities. We extend trigger detection to an Asian context using Reddit conversations from Singapore, showing both that detection is feasible and that the triggers themselves can differ markedly from Western patterns. A reminder that 'toxic' is culturally specific.
Can social media reveal who will engage politically on contested issues? On the U.S. gun debate, we use Twitter content and network structure to classify user stance, predict state-level gun policy, and forecast individual offline political action. Network features (especially the relative size of each side) emerge as strong signals alongside content and demographics.
Conversation corpora power most interactive AI, but who actually contributed to them? Analyzing a Korean nationwide daily conversation corpus from the National Institute of Korean Language, we characterize the participation of different demographic groups (age and sex) and identify the voices that are over- and under-represented in this widely-used resource.
PlayStation 'trophies' are one of gaming's most-discussed achievement systems, but no large-scale study exists. We build the first complete dataset of PlayStation games and their trophies, then characterize them from both the designer's and the player's perspectives, uncovering patterns in how trophies are designed and which ones players actually complete.
When Facebook became Meta, Twitter users spontaneously started proposing new acronyms for Big Tech ('MAANG,' 'MANGA,' and many more). We trace how candidates were proposed, spread, and judged, finding that early posters had fewer followers than later spreaders, rankings shifted slowly, and replies introduced most new ideas at the first depth.
Framing nudges readers toward conclusions without making explicit arguments. FrameAxis identifies the semantic axes ('microframes') that a document leans on, using word embeddings rather than manual annotation. The method scales to large datasets and exposes bias and intensity from restaurant reviews to political news.
Health campaigns usually broadcast one message to everyone. We propose a five-stage framework for precision public health: define goal and metrics, segment the audience, tailor a message per segment, pilot, run, and evaluate using social-media ad-targeting tools. Two case studies demonstrate the framework in practice.
Can we tell, before COVID-19 began, who would later post anti-Asian slurs on Twitter? Comparing users who did and didn't post such slurs after the pandemic's onset, we identify pre-pandemic features that predict later hate behavior, pointing to the role of news media and polarized communication networks in priming hostility.
If digital media empower populist movements, can we see it in distinct usage patterns? Comparing /r/The_Donald and /r/hillaryclinton during the 2016 election, we find Trump supporters expressed more anti-elitism and exclusion (but not 'people-centrism'), posted more, used distinctive vocabulary, and built tighter community ties.
When media outlets share an article on Twitter, the edited tweet often departs sharply from the original headline. Does that editing pay off? Using a parallel corpus of articles and tweets from 8 outlets, we apply propensity-score matching to estimate the causal effect of editing styles on engagement, surfacing both common and outlet-specific patterns.
Personas are widely used but hard to evaluate because no validated measurement instrument existed. We develop the Persona Perception Scale (PPS), drawing on social psychology, persona studies, and HCI, and validate it with 412 respondents. The scale lets researchers measure how individuals perceive personas (credibility, willingness to use) and improve persona adoption.
Framing is hard to measure at scale because frames usually need hand-coding. We trained a state-of-the-art frame classifier and applied it to 1.5 million New York Times articles spanning 2000 to 2017. Short-term frame fluctuations track major events; long-term, the 'cultural identity' frame rises sharply, and a case study on mass shootings reveals three distinct patterns.
Fact-checking every claim is impossible, so a complementary approach is to profile entire news outlets and flag everything they publish. We argue that bias and factuality should be modeled jointly using both the text the outlet produces and the social context it lives in, and we review approaches that combine the two.
Toxic replies often have an innocent-looking trigger somewhere up the thread. We define a trigger as a non-toxic comment that provokes a toxic reply, then train a neural model on shifts in sentiment, topic, and conversational context. The model identifies triggers with a 0.87 ROC-AUC, suggesting a way to head off escalation before it happens.
Many political-bias datasets rest on three common assumptions: labelers' politics don't bias their labels, articles follow their outlet's leaning, and an outlet's leaning is stable across topics. We test all three on a manually annotated dataset and find each can fail even at small scale, a warning for how bias datasets are constructed and trusted.
Does showing your offline credentials affect how people respond to you online? On two Reddit science communities that adopted 'degree flairs,' we find the halo effect varies by community culture: in the strict peer-reviewed subreddit, any degree boosts votes and discussion; in the casual one, only PhD or MD flairs do.
We propose a graph-based semi-supervised method to measure the political bias of Facebook news pages across many countries, separating alternative media, mainstream media, and public-figure pages. Validated on a U.S. dataset and applied to Brazil, where right-wing pages dominate generally but alternative news media break the pattern.
Social-network studies often estimate homophily from predicted attributes (gender or ethnicity from names), but high prediction accuracy doesn't guarantee accurate homophily estimates. We show estimation works only when dyad-level residuals cancel out, and propose an 'ego-alter' approach that beats standard node and dyad classifiers.
Today's news ecosystems mix legacy outlets, platforms, influencers, and amateurs in ways that resist easy comparison. We propose 'discursive power' as a measurable concept: the ability to introduce, amplify, and sustain topics, frames, and speakers in interconnected communication spaces, enabling empirical comparative work on hybrid media systems.
Different platforms surface different topics: how does that shape engagement? Across 3.16M posts from 53 news organizations on Facebook, Instagram, Twitter, YouTube, and Reddit over 8 months, we map topic distribution by platform and topic-driven engagement at four levels, finding that cross-platform sharing of any given article is predictable with around 80% precision.
Online political talk happens in two very different settings: groups of like-minded users, and mixed spaces where opposing views meet. We use Reddit to develop an analytical template comparing interaction and linguistic patterns within and between politically homogeneous and cross-cutting communication spaces, finding markedly different behaviors in each.
What sparks a toxic exchange in online discussions? We define toxicity 'triggers' formally, then build an LSTM model combining textual features with measures of topic and sentiment shift across interactions. Trained on diverse Reddit communities, the model detects triggers with 82.5% average accuracy.
What images do top global brands actually share on Instagram and Facebook, and who shows up in them? Analyzing 85,957 advertising images from 73 international brands, we measure gender and racial diversity at scale, working toward an automated 'watchdog' for diversity in online advertisements.
Among other findings, results show that content with high engagement on one platform does not guarantee high engagement on another platform, even when news outlets use similar cross-platform posts; however, for some content, cross-sharing posts on a platform will increase overall audience engagement on another platform.
Creating 'engaging' content across social-media platforms is a daily grind for editors at serious news organizations, with no clear playbook. Through a focus-group study at a major news org, we find engagement work is effort- and time-intensive across three platforms, and we sketch a system design that would help editors select and adapt engaging passages from articles.
Social media now captures and announces breaking news faster than traditional outlets, reshaping the journalistic landscape. We survey how social-media mining is being adopted by professional journalists for newsgathering (drawing on citizen reporting and updates) and as an additional channel for news dissemination, opening both opportunities and challenges for newsrooms.
Behind a news story sits a much larger context: the outlet's general factuality, partisanship, propaganda style, ideology, framing, and stance on key topics. Tanbih is a news aggregator that groups stories by event and generates these media profiles automatically, plus per-article propaganda detection and stance with respect to specific controversial claims.
We analyze ten common stylistic features indicative of user engagement for news postings on multiple social media platforms. We display the stylistic features usage differences of news posts from various news sources. Results show that there are differences in the usage of stylistic features across social media platforms (Facebook, Instagram, Twitter, and YouTube).
Gab launched in 2016 as a free-speech alternative to Twitter, attracting users banned from other platforms. We analyze 22M posts from 336K users (Aug 2016 to Jan 2018) and find Gab is mostly used for news and world events, attracts alt-right users, conspiracy theorists, and trolls, with hate-speech prevalence much higher than Twitter but lower than 4chan's /pol/.
Automatic hate-speech detection is hard partly because 'hate' covers many distinct types and targets. We hand-labeled 5,143 hateful YouTube and Facebook comments, built a granular taxonomy capturing both the kind and target of hate, and trained classifiers to detect and categorize new comments. A linear SVM with TF-IDF reaches 0.79 average F1.
Marketing personas are hand-crafted, slow, and quickly outdated. We automate them from social-media data through behavioral segmentation, demographic grouping, and auto-generated names and photos. Validated on 30M viewer interactions across 198 countries and 4,200 videos, the personas predict content preference and remain stable over time.
Word meanings shift across communities and contexts, but most NLP tools collapse that nuance into a single sentiment dimension. SemAxis is a lightweight framework that characterizes words along arbitrary semantic axes in vector space, capturing community-specific meaning. On sentiment specifically, SemAxis beats state-of-the-art methods for building domain-specific lexicons.
When computational social scientists infer demographics from photos, which face-recognition tool can they trust? Testing Face++, IBM Bluemix, AWS Rekognition, and Microsoft Azure across multiple datasets, we find gender inference works (above 90%, except IBM), race inference is offered by only one tool, and age is consistently inaccurate. Triangulation and manual checks recommended.
Does adding photos to a marketing persona make it more informative? Eye-tracking and interviews compare a single headshot, a headshot plus contextual scenes of the persona, and a headshot plus images of different people. Contextual images help; mixed people confuse readers, and viewers' own preconceptions shape what they see.
Do people care about the same countries their national news covers? Across 193 countries over 14 months, we find that media and public attention align regionally but diverge by topic. In 63% of countries, the two attend to entirely different countries, suggesting audiences seek information their local outlets are not providing.
What confuses users of persona profiles? In a 29-participant eye-tracking and think-aloud study with three persona layouts, we measure where users fixate, what they say, and how the two relate. Inconsistencies between persona elements (especially between photos and quoted social-media voices) drive most confusion. We propose a 'confusion ratio' to flag problematic profile elements.
Avatars are a quiet form of self-expression, but the values built into avatar systems are rarely studied. Using deep learning and clustering on roughly 100,000 avatars from a major creation platform, we surface regional trends: realistic (US, mirroring demographics), idealistic (Japan, reflecting media tropes), and creative (community-spread), and link each to user activity.
We demonstrate a real-time persona-generation pipeline on a YouTube channel of a global media corporation: 30M+ interactions, 181 countries, 4,200+ digital products. The system identifies distinct, impactful user segments and decorates each with auto-generated names, photos, and personal attributes, producing believable personas from real-time online data.
Why do players stay or leave a game? Analyzing in-game logs of 51,104 players in an online multiplayer game across their virtual 'life phases,' we find what predicts retention shifts with progression: achievement features matter most early and mid-game, but at the highest level, social ties become the strongest predictor of who keeps playing.
We adapt automatic persona generation for Middle Eastern social-media users, blending computational analysis of millions of YouTube interactions with interview data to enrich personas. The result: culturally adapted personas that capture how different demographic groups in the region actually engage with content, and a methodology that respects cultural context.
Content creators distributing on online platforms need to understand their audiences. We generate personas from online user data using non-negative matrix factorization to identify user segments, then add names and photos for personality. The result is data-driven personas that represent real audience groups, replacing manually gathered data.
Who shares news on Twitter, and what does that mean for what most readers see? In the first detailed study of news sharers' demographics, content, and audience, we find that male and white users dominate news sharing, biasing the visible mix toward those groups' interests, with implications for personalized news design.
Personas are supposed to align cross-team user understanding, but do they? Through 16 qualitative interviews with media producers at Al Jazeera English, we find understandings are highly varied, built from a mix of personal experience, self-projection, assumptions, and company data, and only sometimes match the data-driven personas.
Why do some news topics get sustained media attention while others fade? Most prior work was limited to a few countries or topics for lack of global longitudinal data. We use large-scale data from 196 countries to provide empirical analyses of how media attention forms, peaks, and dissipates over time.
How do MENA-region users (whose cultures rarely shape platform design) creatively adapt social media to express identity? Combining computational analysis of 42,000+ Qatari social profiles with 255 qualitative profile analyses and interviews, we identify five distinct values driving virtual-identity creativity, and propose guidelines for systems that respect those values.
What do people do when they know their work will be erased? Using 270 million records from the final days of the ArcheAge MMO closed beta (where all character data was about to be deleted), we find no broad antisocial collapse, but a sharp drop in quest completion, leveling, and ability changes. Most players stopped planting their proverbial apple trees.
How differently do men and women, young and old, consume news? Examining 103,133 most-popular articles for different demographic groups in Daum News (Korea's second-largest portal) over 2015, we measure differences at four levels: items, sections, topics, and subtopics. Demographic differences appear most sharply at the subtopic level, not at the broader section or topic level.
The Press Freedom Index, published annually by Reporters Without Borders, is invaluable but expensive and slow because it's survey-based. We propose an alternative: measure press freedom from the diversity of attention an outlet pays, computed automatically from large-scale news data. The result tracks the official PFI while updating in near-real time.
Are news outlets across countries converging on the same stories or going their separate ways? Using daily data on the 100 most over-reported topics in each country over 7 months, we find two patterns coexisting: globalization (shared attention across regions) and domestication (locally distinct coverage), shaped by geography, history, time, and topic.
Which countries pay attention to each other in their news coverage, and which deliberately ignore? We build a multiplex Media Attention and Disregard Network across 129 countries over 212 days. The network is skewed, hierarchical, and asymmetric, but suggests a 'global village' overall, with persistent attention blocks around the Middle East/North Africa and Russia's neighbors.
Can web searches and social-media chatter predict an emerging-disease outbreak? During the 2015 MERS outbreak in Korea, daily Google searches and tweets for four Korean keywords correlated strongly (above 0.7) with new and quarantined cases over the next three days, suggesting that simple digital signals can complement traditional epidemiological surveillance.
Demographics matter for online behavior, but most social-media studies skip them for lack of data. We use face-analysis software to infer gender, age, and race from profile images of 350K New York Twitter users, then track which hashtags each demographic group uses over a year. Top hashtags overlap broadly, but each group also has its own distinctive set.
How did the world respond to the Charlie Hebdo shootings on Twitter? We test whether cultural background alone predicts stance on free speech (Huntington's 'Clash of Civilizations') or whether local social context matters too. Both: 'civilization' explains broad patterns, but non-Arabs embedded in mixed networks used #JeSuisAhmed five times more often.
GDELT and Event Registry are the two big repositories researchers use to study global news. We compare them across scale, news sources, and news geography. They differ sharply in scale and sources but agree closely on news geography, which is both a warning and a reassurance for anyone choosing between them.
What kinds of objects appear in news photos, and do they tell the same story as the article text? Analyzing over 2 million news photos from January 2016 using deep-learning vision APIs, we measure object frequency, photo sentiment, alignment with text tone, gender representation, and visual portrayal of political candidates. The first study of its scale.
Toxic behavior in team-based competitive games is widely felt but poorly understood empirically. Using over 10 million player reports on 1.46 million toxic players in League of Legends (with crowdsourced verdicts attached), we test hypotheses drawn from theories of toxic behavior at unprecedented scale, providing a foundation for systems to detect, prevent, and counter such acts.
News headlines are the first impression in the click economy. Analyzing 69,907 headlines from four major media corporations over 8 months in 2014, we extract sentiment features and find a strong link between a headline's sentiment polarity and both the popularity of the news and the dynamics of its comments. Sentiment shapes how news travels.
Social media 'now-casting' (predicting flu activity, unemployment) usually counts every tweet equally, but social-media users don't equally represent the offline population. We show that filtering by user characteristics (profile completeness, group membership) substantially improves now-casting accuracy, even when most users are excluded the right subset can outperform the whole.
News-geography research usually relies on what outlets publish, but what about what audiences actually read and react to? We pair 20,000 Aljazeera articles with 2M+ comments by 90,000 readers (countries inferred from IP) to contrast the producer's lens (coverage) with the consumer's lens (attention), exposing where the two diverge.
League of Legends crowdsources toxicity judgements through the 'Tribunal,' but human review is slow and expensive. We train classifiers on 10M+ player reports covering 1.46M toxic players and the corresponding crowdsourced verdicts. The model captures strong-majority cases well and ports across regions, suggesting a practical way to reduce moderation cost.
Instant messaging silently signals 'last seen,' creating social pressure to reply fast. We show those signals are also weak predictors of actual attentiveness. Tracking 24 users over 2 weeks, we train a model on phone signals (notifications, screen activity, proximity, ringer mode) that predicts whether a user will view a message within minutes at 70% accuracy.
What does toxic player chat actually look like, linguistically? Using crowdsourced data on 590,000+ reported toxic players in League of Legends, we trace the language patterns that distinguish toxic players from typical ones, and find empirical support for how a player gradually slides from typical into toxic behavior. The findings enable earlier detection and warning.
Even if Facebook users see politically diverse articles, do they only share ones that match their views? We test for 'partisan sharing' across individuals, time, and topic importance, and find strong evidence it exists. A controlled experiment with 70+ BBC viewers shows partisan sharing distorts perception of reality, but is also associated with more political knowledge.
Is there really such a thing as a unique team style in modern soccer? We introduce 'flow motifs', statistically significant patterns in pass sequences, to compare teams' play. Most teams converge on similar structures, but FC Barcelona's tiki-taka proves a genuinely distinctive, tightly built pattern, not a flurry of random passes.
Most Kickstarter projects fail because creators can't reach enough investors. We propose ways to recommend Twitter accounts likely to pledge to a given project, translating hypothesis-driven analyses of pledging behavior into recommendation strategies. The best strategy predicts a list of plausible investors with 84% accuracy.
Why do people share certain political articles and not others? We propose a Political News Sharing model integrating gratification, selective exposure, socialization, and trust/intimacy. On Twitter data, sharing is driven by what one agrees with (selective exposure) but also by source credibility and informativeness, and users sometimes share opposing articles when those match their followers' leanings.
What determines whether the world's news media notice a disaster? Using the GDELT dataset that monitors news in over 100 languages, we fit a hierarchical regression and find population, political stability, and damage all matter as expected, but also expose strong regionalism in news geography that's only visible at truly global scale.
Cellular networks already log subscribers' coarse location continuously. We ask whether such sparse, irregular data is enough to reconstruct each device's actual daily path. If so, the same approach extends from smartphones to the millions of future connected 'things,' enabling continuous mobility tracking without GPS or dedicated apps.
What determines whether a disaster gets global news coverage? Using the GDELT dataset that monitors news in over 100 languages worldwide, we fit a hierarchical mixed-effect regression model and find that population, political stability, and damage all matter (as prior research suggested), but also expose strong regionalism that shows up only when the dataset is genuinely global.
Can Twitter measure a paper's scientific impact? We track how 75,677 research papers from the top 100 journals get shared on Twitter (403,165 tweets by 142,743 users over 135 days). Twitter's reach for scholarly content is real, but attention is highly skewed toward a small subset of top journals, limiting its value as a fair impact signal.
Selective exposure has been theoretically promising but hard to test empirically. Looking at news articles shared on Facebook, we find concrete evidence that users predominantly share like-minded articles and avoid conflicting ones, with partisans especially so. Building tools to counter this would require identifying partisan users, but the subgroup is demographically indistinguishable from the average user.
What makes a Twitter user stop following another? Using actor-oriented modeling (SIENA) on tightly-knit user groups, we find relational properties dominate: mutual following and shared followees reduce unfollow likelihood, and unfollows tend to be reciprocal (being unfollowed prompts unfollowing back). Surprisingly, homophily of interests and informativeness of interactions show no measurable effect.
What makes a tweet get retweeted? Combining network structure with user behavior, we find that a message's visibility in a user's incoming flow strongly predicts whether they pass it on, but latent traits of the original poster also matter. We turn these findings into a propagation model whose simulated cascades match the empirical ones.
Sentiment analysis needs language-specific lexicons, but non-English languages often lack them. Tower of Babel is a language-independent crowdsourcing game that builds sentiment lexicons through play. A 135-participant study finds the game produces accurate classifications, faster than manual annotation and rated as more enjoyable.
Tools that expose people to diverse opinions are pitched as a fact-checking aid, but does anyone want them? Through surveys of 60 people and in-depth interviews of 10 in the UK and South Korea, we find people don't equally value opinion diversity, and even the same person benefits only sometimes, depending on prior convictions, emotional state, and social context.
Twitter exposes who follows which news outlet and how outlets interact through their audiences. Building a map of 77 media outlets from Twitter subscription overlap, we can answer questions previously locked in media databases: how much do NYT and WSJ readers overlap? Are outlets competing or complementary? Do friends subscribe to similar outlets?
Tracking political bias in news outlets matters for media literacy. We map media sources onto a left-right political spectrum using Twitter co-subscription (who follows which outlet). Analyzing 7M follow links shows the political split naturally emerges from direct subscription data, and we demonstrate a real-time application visualizing the ideological landscape.
Why do people unfollow on Twitter? We fit a logistic regression with structural and behavioral features (ego, dyad, and triad properties) on large-scale data, finding 5 structural and 7 actional variables that significantly predict unfollows. One striking pattern: people are less likely to unfollow accounts that have acknowledged them, even in lopsided communications.
Who you follow on Twitter shapes which media you see, but indirect exposure (friends who follow outlets, retweets from outside your circle) matters too. Using public follow data, we show that indirect exposure expands the political diversity of news a typical user encounters by 60 to 98%, a finding not visible to traditional media research.
How often do Twitter users actually unfollow each other? Tracking 1.2M Korean-speaking users daily for 51 days, we find unfollowing is common. Quantitative analysis identifies key factors (reciprocity, relationship duration, followees' informativeness, overlap of relationships); 22 interviews reveal users unfollow those who tweet too often, about boring topics, or about mundane life details.
Community-detection algorithms tend to return different communities each time you change the input node ordering. We propose two metrics (pairwise membership probability, consistency) and use the first as link weights to drive a procedure that converges to consistent communities within six cycles or fewer, without sacrificing modularity.
Twitter exploded from a nascent microblogging service to over 41M users in just a few years. We crawl the full Twitter graph (1.47B follow relationships, 4,262 trending topics, 106M tweets) and characterize its topology and information dynamics, asking whether Twitter behaves more like a social network or a news medium. The finding: more news than network.
Many methods identify influential Twitter users from follower or retweet counts, but ignore when information actually gets adopted. We propose a method that incorporates both link structure and the temporal order of adoption, surfacing a distinct class of influentials that previous methods miss.
Community-detection algorithms often disagree on which nodes belong where. Building on an iterative-reinforcement approach across 12 real networks, we find about 40% of nodes are grouped inconsistently in large social graphs (Orkut, Cyworld). Using participation-coefficient analysis, we classify nodes into seven structural roles and compare two AS networks built from different sources.
YouTube reshaped how people make and watch video, and traditional video-on-demand metrics don't apply. We analyze the world's largest user-generated content video system, characterizing the popularity distributions, lifetimes, and request patterns of user-uploaded videos, with implications for caching, monetization, and platform design.
User-generated video systems differ fundamentally from traditional video-on-demand. Analyzing YouTube and Korea's Daum Videos, we characterize UGC popularity distributions, the lifetime of a video's popularity, and the relationship between requests and content age. We also surface the impact of content aliasing and illegal uploads on ranking accuracy.
Standard collaborative filtering suffers from data sparsity, cold start, and noise. We propose computing recommendations from a separate set of expert reviews (instead of the user-rating graph itself), weighted by similarity to each user. Validated on Netflix data and a user study where participants preferred our recommendations, the method preserves accuracy while avoiding CF's weaknesses.
Three popular community-detection algorithms (CNM, Wakita, Louvain) all return different communities every time you change input node ordering. We propose pairwise membership probability as a consistency metric, then use it as link weights in an iterative procedure that converges to consistent communities within six cycles without sacrificing modularity.
Online services group users with shared interests, but their group-recommendation signals stay siloed inside each service. We propose connecting users across multiple web services through their user-labeled tags, which carry less privacy risk and stay fresher than profile data. A vector-space model with intra-personomy normalization finds similar users effectively across six services.
Is a Cyworld 'friend' an active relationship or just a declaration? We compare friend-list networks against activity networks formed by actual guestbook comments. The two have similar topology, but only the activity network (with reciprocated comments as edges) behaves like a true social network: more assortative, with fewer artificially dense cores.
Do online relationships behave like real-life ones? We compare three online social networks of 10M+ users each (Cyworld, MySpace, orkut), analyzing degree distribution, clustering, and degree correlation. Cyworld's distribution shifts toward multi-scaling over time; the three networks differ in scaling exponents, and certain online behaviors diverge from real-life social patterns.
Are LLMs persuaded out of their beliefs? Using the Source–Message–Channel–Receiver (SMCR) communication framework across six mainstream LLMs and three domains (facts, medical QA, social bias), we measure how stable each model's stated beliefs are under persuasive pressure. The smallest model flips on 82.5% of attempts at the first turn. Counterintuitively, asking models to verbalize confidence makes them more vulnerable.
