Explore papers
Filter by research area, year and venue. Every filter is in the address bar, so you can share a view as a link.
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.
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.
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…
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…
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…
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.
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.
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…
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…
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…
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…
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…
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…
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.
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…
Spread & networksDetection methods
Fake News: Fundamental Theories, Detection Strategies and ChallengesA comprehensive tutorial framework addressing fake news research across three dimensions: (1) fundamental theories from psychology, social science, economics, and forensics explaining human vulnerability to misinformation and why individuals participate in fake news activities; (2) detection strategies unified across four perspectives…
Detection methodsSpread & networks
dEFEND: Explainable Fake News DetectionFake news detection on social media typically focuses on content alone, but user comments and reactions provide complementary signals. This paper proposes dEFEND, a hierarchical attention neural network that jointly encodes news sentences and user comments to both detect fake news and explain why a piece of news is detected as fake by…
Spread & networksPlatforms, policy & society
Social Media for Political Campaigns: An Examination of Trump's and Clinton's Frame Building and Its Effect on Audience EngagementThis study analyzes how Trump and Clinton's 2016 campaigns framed messages on Twitter and Facebook, and how frame type affected audience engagement. Trump relied on conflict and negative emotional frames, while Clinton used morality and positive emotional frames.
Fact-checking & verificationPlatforms, policy & society
Fighting Misperceptions and Doubting Journalists' Objectivity: A Review of Fact-checking LiteratureComprehensive literature review of 48 studies on political fact-checking, organized into three research areas: (1) fact-checking effectiveness for correcting misperceptions (mixed results, including backfire effects); (2) fact-checking as a profession (contested methods and reliability); (3) public opinion about fact-checking (mostly…
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…
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…
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.
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…
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.