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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

Datasets & evaluationFact-checking & verification Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on TwitterThis paper compares hate speech detection systems trained on expert annotations (from feminist and anti-racism activists) versus amateur annotations (crowdsourced via CrowdFlower) on 6,909 tweets. Systems trained on expert annotations significantly outperform those trained on amateur data, and amateur annotators systematically label more… Waseem · 2016 EMNLP Workshop on Natural Language… · 2016

Spread & networksDetection methods Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media ConversationsProposes that modelling Twitter conversations as tree-structured sequences improves rumour stance classification. Introduces Linear and Tree Conditional Random Fields (CRF) to exploit the sequential structure of replies within rumour threads, showing that Tree CRF significantly outperforms non-sequential baselines on 4,519 annotated… Zubiaga et al. · arXiv · 2016

Spread & networksDetection methods DeepCas: an End-to-end Predictor of Information CascadesDeepCas predicts the future size of information cascades (e.g., how many people will eventually retweet a post or cite a paper) using an end-to-end deep learning model that represents cascade graphs as sets of random walk paths. Instead of relying on hand-crafted network features, the model learns cascade representations automatically… Li et al. · arXiv · 2016

Bots & coordinated campaignsDatasets & evaluation BotOrNot: A System to Evaluate Social BotsBotOrNot is a publicly available service and API that evaluates whether a Twitter account is controlled by a human or automated bot. Using 1,000+ features across six categories (network, user, friends, temporal, content, sentiment), the system applies Random Forest classification to achieve 0.95 AUC on a labeled dataset of 15k bots and… Davis et al. · 25th International Conference Companion on… · 2016

Bots & coordinated campaignsDetection methods DNA-Inspired Online Behavioral Modeling and Its Application to Spambot DetectionProposes a novel methodology to characterize online user behavior using DNA-inspired digital sequences, where each user action (tweet type, content features) is encoded as a character in a string. Applies standard bioinformatic DNA analysis techniques—specifically longest common substring (LCS) analysis—to detect spambots on Twitter by… Cresci et al. · IEEE Intelligent Systems · 2016

Spread & networksPlatforms, policy & society Reducing Controversy by Connecting Opposing ViewsThis paper proposes an algorithmic approach to reduce polarization on social media by recommending social connections (edges) between opposing sides. Using an endorsement graph representation and the random-walk-based controversy metric, the authors formulate the problem as k-edge-addition and develop an efficient algorithm (ROV) that… Garimella et al. · WSDM 2017 · 2016

Fact-checking & verificationPlatforms, policy & society Boundaries Not Drawn: Mapping the institutional roots of the global fact-checking movementGraves maps the institutional landscape of 64+ fact-checking organizations globally across two summits (2014–2015), using ethnographic fieldwork and ternary plots to sort organizations along three axes: journalism, academia, and politics/civil society. The movement has a journalistic core but exhibits striking institutional diversity and… Graves · Journalism Studies · 2016

Spread & networksPlatforms, policy & society Corporate funding and ideological polarization about climate changeFarrell analyzes 40,785 texts (39+ million words) from 164 climate contrarian organizations between 1993–2013, employing Structural Topic Modeling and social network analysis to demonstrate that corporate funding from entities like ExxonMobil and Koch family foundations directly influences which polarization themes are created and… Farrell · National Academy of Sciences · 2016

Fact-checking & verificationDatasets & evaluation Computational fact checking from knowledge networksThis paper proposes a computational approach to fact checking by framing it as a network problem on knowledge graphs. The method finds the shortest path between entities in Wikipedia's knowledge graph using semantic proximity metrics that account for entity generality. Ciampaglia et al. · arXiv · 2015

Bots & coordinated campaignsDetection methods The Rise of Social BotsSocial bots—automated software agents in social media—are increasingly sophisticated and pose real threats including misinformation spread, trend manipulation, and election interference. This paper characterizes modern bot behavior, surveys detection challenges, and proposes a taxonomy of detection approaches: graph-based methods… Ferrara et al. · ACM Transactions on the Web · 2015