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Spread & networksPlatforms, policy & society
Influence of fake news in Twitter during the 2016 US presidential electionUsing 171 million tweets from the five months preceding the 2016 U.S. election, this study found that 25% of news-sharing tweets contained fake or extremely biased content. Network analysis revealed distinct clusters of fake, biased, and mainstream news spreaders.
Deepfakes & multimodalDetection methods
The Emergence of Deepfake Technology: A ReviewThis review analyzes 84 news articles about deepfakes to synthesize understanding of synthetic video technology, its applications, threats, and countermeasures. Deepfakes—hyper-realistic videos created using AI (primarily GANs)—lower the barrier to video forgery and represent a significant threat to trust, elections, and security.
Detection methodsSpread & networks
Fake News in the 2016 and 2018 Campaigns: Prevalence, Detection, and EffectsNyhan presents empirical findings on fake news consumption in the 2016 and 2018 U.S. campaigns, drawing from survey data with browser tracking to measure actual article visits. While mainstream media reported on fake news as a widespread crisis, his research shows that only ~25% of Americans visited fake news websites, and consumption…
Spread & networksBots & coordinated campaigns
5 Reasons Why Social Networks Make Us Vulnerable to MisinformationMenczer presents five fundamental factors that make social networks inherently vulnerable to misinformation spread. Information and misinformation propagate through networks at equivalent rates due to structural properties of scale-free networks combined with human cognitive limitations.
Platforms, policy & society
The platform governance triangle: conceptualising the informal regulation of online contentThis paper applies Abbott and Snidal's "governance triangle" framework to analyze how state actors, technology firms, and civil society organizations informally regulate online content through voluntary standards, codes of conduct, and multistakeholder initiatives. The analysis maps the European platform governance landscape, showing how…
Spread & networksPlatforms, policy & society
Auditing Radicalization Pathways on YouTubeFirst large-scale quantitative audit of user radicalization on YouTube. The authors tracked 349 channels across three ideological communities (Intellectual Dark Web, Alt-lite, Alt-right) and found strong evidence that users systematically migrate toward more extreme content.
Datasets & evaluationDetection methods
Predicting the Type and Target of Offensive Posts in Social MediaThis paper introduces OLID (Offensive Language Identification Dataset), a large-scale dataset of 14,100 English tweets annotated with a three-level hierarchical schema for detecting offensive content. The hierarchy distinguishes offensive from non-offensive posts (Level A), categorizes offense type as targeted insults or untargeted…
Detection methodsDatasets & evaluation
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social MediaSemEval-2019 Task 6 reports on a shared task competition for identifying and categorizing offensive language in social media using the OLID dataset. Approximately 800 teams registered and 115 submitted results across three sub-tasks: offensive language detection (104 teams), offense type categorization (71 teams), and offense target…
Deepfakes & multimodalDetection methods
FakeCatcher: Detection of Synthetic Portrait Videos using Biological SignalsFakeCatcher detects deepfakes by analyzing biological signals (photoplethysmography—blood flow patterns) extracted from facial regions. The key insight is that generative models fail to preserve spatial coherence and temporal consistency of these signals in authentic videos, making biological signal analysis a powerful forensic tool…
Bots & coordinated campaignsDetection methods
Better Safe Than Sorry: an Adversarial Approach to improve Social Bot DetectionProposes GenBot, a novel genetic algorithm that synthesizes evolved versions of existing spambots by encoding behavioral timelines as "digital DNA" sequences and iteratively evolving them to resemble legitimate user behavior. Tests the evolved bots against three state-of-the-art detection systems and finds they largely evade detection…
Spread & networksDatasets & evaluation
RumourEval 2019: Determining Rumour Veracity and Support for RumoursRumourEval 2019 is a shared task for rumour stance detection and veracity prediction on social media. Extending the 2017 edition, it introduces Reddit data alongside Twitter, provides datasets annotated for SDQC stance (Support/Deny/Query/Comment) and three-way veracity labels, and received 22 system submissions showing that modern…
Detection methodsSpread & networks
Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media ConversationsProposes Tree LSTM and Binarized Constituency Tree (BCTree) LSTM architectures enhanced with convolution units to classify stance and predict rumor veracity in social media conversation threads. The approach exploits the tree structure inherent in threaded conversations (source post with nested replies) and combines it with convolutional…
Detection methodsDatasets & evaluation
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda DetectionThis paper reports findings from a shared task on fine-grained propaganda detection in news articles, featuring two subtasks: fragment-level identification and classification into 18 propaganda techniques, and sentence-level binary propaganda detection. The task attracted 90 registered teams with 39 making submissions; winning systems…
Bots & coordinated campaignsDeepfakes & multimodal
Characterizing the Use of Images in State-Sponsored Information Warfare Operations by Russian Trolls on TwitterLarge-scale analysis of 1.8M images posted by ~3.6K Russian state-sponsored Twitter accounts (IRA-controlled). Russian trolls strategically deployed images and memes to maximize political influence, with political imagery 2+ times more effective than general content.
Spread & networksDetection methods
Hierarchical Propagation Networks for Fake News Detection: Investigation and ExploitationContent-only fake news detection has limited performance because fake news is deliberately crafted to mimic real news. This paper constructs hierarchical propagation networks from both macro-level (tweet/retweet cascade) and micro-level (reply conversation) granularity, extracts structural, temporal, and linguistic features from both…
Detection methodsDatasets & evaluation
Beyond News Contents: The Role of Social Context for Fake News DetectionContent-only detection is insufficient because fake news is crafted to mimic real news. This paper argues that the tri-relationship among publishers, news pieces, and users forms rich social context for detection: partisan-biased publishers more likely publish fake news, and low-credibility users more likely share it.
LLMs & generative AIDetection methods
Defending Against Neural Fake NewsZellers et al. introduce GROVER, a conditional neural text generation model that can both generate and detect neural fake news. The paper shows that humans cannot reliably distinguish GROVER-generated propaganda from real human-written disinformation, rating machine-generated propaganda as more trustworthy (2.42/3 vs 2.19/3 for human…
Psychology & interventionsSpread & networks
Cognitive attraction and online misinformationMisinformation spreads not because it is low-quality information constrained by digital media inefficiencies, but because it taps into general evolved cognitive preferences. A content analysis of 260 articles from 26 hoax websites shows that 86% contain at least one cognitive-appeal feature: negative framing (49%), threat descriptions…
Spread & networksPlatforms, policy & society
How Many People Live in Political Bubbles on Social Media? Evidence From Linked Survey and Twitter DataEady et al. link survey data from 1,496 Americans to Twitter data from 642,345 accounts and ~1.2 billion tweets to measure political polarization in media consumption. Rather than finding evidence of extreme "filter bubbles" where most people consume ideologically homogeneous content, they find substantial overlap in the accounts…
Spread & networksPlatforms, policy & society
Fake news on Twitter during the 2016 U.S. presidential electionFake news consumption on Twitter during the 2016 election was extremely concentrated: only 1% of users saw 80% of exposures, and 0.1% shared 80% of fake news. Conservative-leaning, older, highly politically engaged users drove most consumption.
Spread & networksPsychology & interventions
Less than you think: Prevalence and predictors of fake news dissemination on FacebookFake news dissemination on Facebook during the 2016 election was a rare behavior (8.5% of users shared at least one article), with clear demographic stratification: conservatives shared more articles from fake news domains (pro-Trump), but the strongest and most robust predictor was age—users over 65 shared nearly seven times as many…
Deepfakes & multimodalDetection methods
MVAE: Multimodal Variational Autoencoder for Fake News DetectionThe paper proposes MVAE, a variational autoencoder that learns shared representations of text and images in social media posts to detect fake news. The model jointly trains an encoder-decoder (to discover correlations across modalities) with a binary classifier.
Bots & coordinated campaignsPlatforms, policy & society
Coordinating a Multi-Platform Disinformation Campaign: Internet Research Agency Activity on Three U.S. Social Media Platforms, 2015 to 2017This paper analyzes Russian Internet Research Agency activity across Facebook, Twitter, and Reddit from 2015–2017 using vector autoregression models on 3,126 Facebook ads, 1.9M tweets, and 12,603 Reddit posts. Key finding: Reddit activity influenced subsequent Twitter activity (a "trial balloon" effect), but the IRA's Twitter engagement…
Spread & networksBots & coordinated campaigns
A Study of Misinformation in WhatsApp groups with a focus on the Brazilian Presidential ElectionsStudy of 130 public WhatsApp groups during the 2018 Brazilian presidential election found that 13.1% of shared links pointed to junk news sources, with 40% of links directing to YouTube—a platform used strategically to bypass platform moderation. Videos and images dominated message content (33% of all messages) and were heavily…