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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

Fact-checking & verificationDatasets & evaluation Finding Streams in Knowledge Graphs to Support Fact CheckingKnowledge Stream (KS) is an unsupervised network-flow approach for computational fact-checking that treats a knowledge graph as a flow network. For a triple (subject, predicate, object), KS finds multiple paths connecting subject to object and uses edge capacities (based on relational similarity and node specificity) to compute a truth… Shiralkar et al. · arXiv · 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

Bots & coordinated campaignsDetection methods The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms RaceThis paper provides empirical evidence of a paradigm shift in spambot design on Twitter, where new "social spambots" closely mimic genuine user behavior and successfully evade detection. Neither Twitter's automated systems, human annotators, nor established academic detection tools can effectively identify these accounts; however,… Cresci et al. · WWW 2017 Companion · 2017

Bots & coordinated campaignsDatasets & evaluation Online Human-Bot Interactions: Detection, Estimation, and CharacterizationSocial media accounts controlled by software can mimic human behavior while executing coordinated campaigns. This paper develops a machine-learning framework extracting 1,150 behavioral features from Twitter account metadata, posting patterns, and social networks to classify bots with 95% accuracy on honeypot data. Varol et al. · arXiv · 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 methodsLLMs & generative AI CSI: A Hybrid Deep Model for Fake News DetectionCSI is a three-module deep learning model that detects fake news by jointly modeling article text, temporal patterns of user engagement (response), and user behavior patterns (source). The Capture module uses LSTM to learn temporal response signals; the Score module learns user suspiciousness from co-engagement patterns; the Integrate… Ruchansky et al. · CIKM'17 · 2017

Detection methodsFact-checking & verification Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-CheckingThis paper performs a linguistic analysis of fake news across four categories—satire, hoaxes, propaganda, and trusted news—using lexical features (LIWC, subjectivity, hedging, intensifiers). Experiments show that fake news articles use more first-person pronouns, exaggerative language (superlatives, modal adverbs), and hedging, while… Rashkin et al. · 2017 Conference on Empirical Methods in… · 2017

Detection methodsDatasets & evaluation A Stylometric Inquiry into Hyperpartisan and Fake NewsThis paper investigates whether writing style alone can distinguish hyperpartisan news from mainstream and fake news from real news. Using a manually fact-checked corpus of 1,627 articles from BuzzFeed, the authors find that hyperpartisan news is stylistically distinct from mainstream (F1 = 0.78), satire is easily distinguishable from… Potthast 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

Psychology & interventionsPlatforms, policy & society Inoculating the Public against Misinformation about Climate ChangeLarge-scale experiments (N=2,167) demonstrate that misinformation about scientific consensus on climate change nullifies the effect of consensus messaging. Pre-emptive inoculation—warning people about misleading tactics and providing refutations—preserves two-thirds of the consensus effect. van der Linden et al. · Global Challenges · 2017

Platforms, policy & societyFact-checking & verification Defining "Fake News": A typology of scholarly definitionsThis paper reviews 34 academic studies (2003–2017) that use the term "fake news" and proposes a unified typology based on two dimensions: level of facticity (reliance on facts) and author's intention to deceive. The typology identifies six distinct types—satire, parody, fabrication, manipulation, advertising, and propaganda—clarifying… Tandoc et al. · Digital Journalism · 2017

Bots & coordinated campaignsPlatforms, policy & society Detecting Bots on Russian Political TwitterAutomated Twitter accounts (bots) represent 50% of activity in Russian political discourse. Using supervised ensemble classifiers on 3.8M accounts and 18M tweets from 2014–2015, the authors achieve 95% precision in detecting bots while maintaining 75% recall. Stukal et al. · Big Data · 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

Psychology & interventions The Psychology of Conspiracy TheoriesPeople are drawn to conspiracy theories via three distinct psychological motives: epistemic (seeking understanding and certainty), existential (seeking control and security), and social (maintaining positive self and group image). However, experimental evidence suggests conspiracy theories ultimately frustrate rather than satisfy these… Douglas et al. · Current Directions in Psychological Science · 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

Psychology & interventionsPlatforms, policy & society Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influencePre-emptively exposing people to flawed arguments before they encounter misinformation (inoculation) reduces that misinformation's influence. Two experiments tested this on climate change: false-balance media coverage (equal weight to consensus vs. contrarian scientists) and fake-expert arguments (using unqualified scientists to… Cook et al. · PLOS ONE · 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

Spread & networksPsychology & interventions Filter Bubbles, Echo Chambers, and Online News ConsumptionThis paper examines whether technological changes (social media, search engines) increase ideological segregation in online news consumption by analyzing web-browsing data from 50,000 active news readers. They find that while social media and search are associated with higher segregation than direct browsing, the overall effects are… Flaxman et al. · Public Opinion Quarterly · 2016