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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.
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.
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,…
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.
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,…
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…
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.
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…
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.
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…
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.
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Platforms, policy & societyPsychology & interventions
In Related News, That Was Wrong: The Correction of Misinformation Through Related Stories Functionality in Social MediaThis study tests whether Facebook's "related stories" feature can correct misinformation by exposing users to debunking information. Two experiments (students and Mechanical Turk workers) examined vaccine-autism and GMO-illness misperceptions.
Psychology & interventionsFact-checking & verification
Misinformation and Its Correction: Continued Influence and Successful DebiasingThis comprehensive review synthesizes literature on why false beliefs persist despite corrections, examining cognitive mechanisms (coherence, credibility, source confusion, fluency) that render misinformation resistant to change. The authors propose evidence-based debiasing strategies—warnings, alternative explanations, repeated…
Spread & networksDatasets & evaluation
The structure and function of complex networksThis comprehensive review surveys the properties and mathematical models of complex networks across diverse domains: social networks, the Internet, biological networks, and collaboration networks. Newman covers observed structural properties (degree distributions, clustering, small-world effects), foundational models (random graphs,…