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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

Fact-checking & verificationPsychology & interventions Claim Ranking in Political DebatesEarly work framing check-worthiness as claim ranking; introduces the notion of "importance weighting" for prioritizing claims for fact-checking in political debates. Foundational for understanding which claims warrant verification effort. Hassan · EMNLP · 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

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

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

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

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

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

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