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Psychology & interventionsPlatforms, policy & society Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social MediaThis paper presents the first large-scale linguistic and psycholinguistic analysis of hate speech categorized by its target: directed (personal attacks) versus generalized (targeting groups). The authors analyze 28,318 directed and ~2,500 generalized hate tweets, revealing that directed hate is more informal, angry, and explicitly… ElSherief et al. · ICWSM · 2018

Datasets & evaluationSpread & networks FakeNewsNet: A Data Repository with News Content, Social Context and Spatiotemporal Information for Studying Fake News on Social MediaFakeNewsNet is a multi-dimensional repository for fake news research on social media, integrating news content (linguistic and visual features), social context (user engagement, network structure, response patterns), and spatiotemporal information (location, timestamps). The dataset contains both fake and real news from PolitiFact and… Shu et al. · arXiv · 2018

Spread & networksBots & coordinated campaigns Anatomy of an online misinformation networkA network analysis of misinformation and fact-checking spread during the 2016 US Presidential Election reveals stark segregation between the two communities on Twitter. The core of the network is dominated by misinformation spreaders; fact-checking nearly disappears in the densest regions. Shao et al. · arXiv · 2018

Spread & networksDetection methods Rumor Detection on Twitter with Tree-structured Recursive Neural NetworksProposes two variants of recursive neural networks (RvNN)—bottom-up and top-down—that operate on propagation tree structures of tweets to detect rumors. Rather than treating tweets sequentially, the model learns representations that respect how tweets actually propagate in response threads. Ma et al. · 56th Annual Meeting of the Association for… · 2018

Bots & coordinated campaignsSpread & networks Bots increase exposure to negative and inflammatory content in online social systemsSocial bots during the 2017 Catalan independence referendum strategically targeted influential humans to amplify negative and inflammatory content. Operating from network peripheries, bots coordinated sentiment-driven amplification that exacerbated polarization, with approximately one in three referendum-related tweets generated by bots. Stella et al. · National Academy of Sciences · 2018

Spread & networksBots & coordinated campaigns Influencers in Polarized Political Networks on TwitterThis paper identifies three types of influencers in polarized political Twitter conversations during Brazil's 2016 Dilma Rousseff impeachment: "opinion leaders" who shape discourse within ideological groups, "informational influencers" (media/institutions) drawn into one camp, and "activists" who amplify like-minded messages. The… Soares et al. · SMSociety · 2018

Spread & networksPsychology & interventions The small, disloyal fake news audience: The role of audience availability in fake news consumptionContrary to claims that fake news has reached a broad audience, this empirical analysis of comScore data shows the fake news audience is small and comprises heavy internet users. Applying the Law of Double Jeopardy from media theory, the authors show that fake news attracts disloyal audiences disproportionately from social platforms. Nelson & Taneja · New Media & Society · 2018

Spread & networksPlatforms, policy & society Exposure to opposing views on social media can increase political polarizationThis field experiment tested whether exposure to opposing political views on social media reduces polarization. Twitter users (Republicans and Democrats) were randomly offered money to follow bots retweeting messages from opposing politicians and opinion leaders for one month. Bail et al. · PNAS · 2018

Detection methodsSpread & networks "News you don't believe": Audience perspectives on fake newsUsing focus groups and surveys across the US, UK, Spain, and Finland, Nielsen and Graves find that ordinary people see fake news as a spectrum rather than a binary distinction—encompassing poor journalism, political propaganda, misleading advertising, and fabricated content. Audiences experience the "fake news" problem as one of degree,… Nielsen & Graves · Reuters Institute Factsheet · 2017

Spread & networksDetection methods This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real NewsFake news differs systematically from real news in stylistic features, especially titles. Contrary to the assumption that fake news mimics real news, it is actually more similar to satire: packed titles with fewer stop words and more proper nouns, combined with shorter, repetitive body text with simpler language. Horne & Adalı · AAAI · 2017

Spread & networksFact-checking & verification Leveraging the Crowd to Detect and Reduce the Spread of Fake News and MisinformationOnline social networks use crowd-powered flagging to identify misinformation: when users flag a story, enough flags trigger fact-checking, which disputes false claims. This paper formulates the problem of deciding which stories to fact-check and when, subject to limited fact-checking resources, as a stochastic optimal control problem. Kim et al. · arXiv · 2017

Spread & networksDetection methods Topological Recurrent Neural Network for Diffusion PredictionWang et al. propose Topo-LSTM, a novel LSTM architecture for modeling dynamic directed acyclic graphs (DAGs) to predict which node will be activated next in information cascades. The key insight is that information diffusion on networks should be modeled explicitly as diffusion topologies—graphs showing how information spreads from… Wang et al. · ICDM 2017 · 2017

Detection methodsSpread & networks Some Like it Hoax: Automated Fake News Detection in Social NetworksHoaxes on Facebook can be detected with 99% accuracy using user interaction patterns (who "liked" posts) rather than content analysis. Two algorithms—logistic regression and harmonic boolean crowdsourcing—achieve high accuracy even with minimal labeled training data, and knowledge transfers across different Facebook communities. Tacchini et al. · Technical Report, UC Santa Cruz · 2017

Spread & networksDetection methods Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTMThis paper proposes Branch-LSTM, a sequential neural model for rumour stance classification in Twitter conversations. By decomposing conversation threads into linear branches and modeling them with LSTM layers, the approach captures conversational context while assigning stance labels (Support/Deny/Query/Comment) to each tweet. Kochkina et al. · 11th International Workshop on Semantic… · 2017

Detection methodsDatasets & evaluation Fake News Detection on Social Media: A Data Mining PerspectiveThis comprehensive survey reviews fake news detection on social media through a data mining lens. It frames the problem around two key aspects—characterization (understanding the psychological and social foundations of fake news) and detection (building computational methods using news content and social context features). Shu et al. · arXiv · 2017

Spread & networksBots & coordinated campaigns The spread of low-credibility content by social botsEmpirical analysis of 14 million Twitter messages during the 2016 U.S. election finds social bots disproportionately amplify low-credibility content: though bots comprise only 6% of accounts sharing such content, they account for 31% of tweets. Bots employ two strategies—rapid early amplification and targeting of influential users—making… Shao et al. · arXiv · 2017

Detection methodsSpread & networks Detect Rumors in Microblog Posts Using Propagation Structure via Kernel LearningThis paper addresses rumor detection in microblog posts by modeling information diffusion as propagation trees and proposing a kernel-based method (PTK) that captures high-order structural patterns differentiating rumors from non-rumors. The approach extends to context-sensitive variants (cPTK) and achieves superior early detection… Ma et al. · ACL · 2017

Spread & networksDatasets & evaluation Quantifying Controversy on Social MediaThis work presents a graph-based pipeline to measure controversy in social media discussions. The authors build conversation graphs from Twitter interactions (retweets, follows, mentions), partition them to identify opposing sides, and apply multiple controversy measures—with a random-walk-based metric (RWC) emerging as the most reliable… Garimella et al. · ACM Transactions on Social Computing · 2017

Spread & networksDatasets & evaluation SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumoursRumourEval is a shared task benchmark for detecting and verifying rumours in social media. It introduces two subtasks: (a) stance classification (SDQC: Support/Deny/Query/Comment) of replies to rumourous claims, and (b) veracity prediction (true/false) of source tweets. Derczynski et al. · 11th International Workshop on Semantic… · 2017

Spread & networksPsychology & interventions Spreadable Spectacle in Digital Culture: Civic Expression, Fake News, and the Role of Media Literacies in "Post-Fact" SocietyThis paper explores how spectacle—media constructs outside ordinary routine that capitalize on polarization—spreads through digital culture during the 2016 U.S. election. Using the Pizzagate conspiracy theory as a case study, the authors argue that citizen-driven spectacle leverages homophilous networks, memes, and appropriated cultural… Mihailidis & Viotty · American Behavioral Scientist · 2017

Spread & networksPsychology & interventions Emotion shapes the diffusion of moralized content in social networksMoral-emotional language (words combining moral and emotional content) increases message diffusion in social networks by ~20% per word. This effect is stronger within political in-groups than out-groups, explaining how moral and political ideas spread within polarized "echo chambers" on platforms like Twitter. Brady et al. · National Academy of Sciences · 2017

Spread & networksPlatforms, policy & society Information Disorder: Toward an Interdisciplinary Framework for Research and Policy MakingThis Council of Europe report introduces a comprehensive framework for understanding "information disorder" via three types (mis-, dis-, mal-information) defined by falseness and intent-to-harm. It examines the agents, messages, and interpreters involved, the creation-production-distribution lifecycle, and proposes 34 recommendations… Wardle & Derakhshan · Council of Europe report · 2017

Spread & networksBots & coordinated campaigns The Spread of True and False News OnlineAnalyzes 126,000 verified true and false news cascades from Twitter (2006–2017) covering 3 million users and 4.5 million shares. False news diffuses significantly farther, faster, deeper, and more broadly than truth across all categories, with false political news spreading fastest. Vosoughi et al. · MIT Initiative on the Digital Economy… · 2017

Spread & networksPlatforms, policy & society Social Media and Fake News in the 2016 ElectionAllcott and Gentzkov provide the first comprehensive empirical evidence on fake news exposure during the 2016 US election. Drawing on a curated database of 156 fake news articles, web traffic data, and a post-election survey of 1,200 adults, they estimate the average American saw and remembered 1.14 fake news articles during the election… Allcott & Gentzkov · Journal of Economic Perspectives · 2017