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Detection methodsLLMs & generative AI Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on TwitterThis paper presents neural network models combining tweet content, social network interactions, and linguistic features (bias markers, subjectivity cues, moral foundations) to classify Twitter news posts as suspicious (propaganda, hoaxes, clickbait, satire) or verified. The models achieve 95% accuracy on binary classification and 0.7… Volkova et al. · ACL 2017 · 2017

Detection methodsDatasets & evaluation Automatic Detection of Fake NewsThis paper introduces two novel fake news detection datasets covering seven news domains and analyzes linguistic properties that distinguish fake from legitimate content. Using linguistic features including readability metrics, word categories, and punctuation patterns, the authors build classifiers achieving up to 78% accuracy and… Pérez-Rosas et al. · arXiv · 2017

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

Spread & networksDetection methods This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real NewsFake news differs systematically from real news in stylistic features, especially titles. Contrary to the assumption that fake news mimics real news, it is actually more similar to satire: packed titles with fewer stop words and more proper nouns, combined with shorter, repetitive body text with simpler language. Horne & Adalı · AAAI · 2017

Detection methodsDatasets & evaluation A Survey on Hate Speech Detection using Natural Language ProcessingThis survey systematically reviews automatic hate speech detection methods, covering feature extraction techniques (surface features, embeddings, sentiment analysis, lexical resources, linguistic features, knowledge bases), classification approaches (mostly supervised learning with SVMs and neural networks), annotation practices, and key… Schmidt & Wiegand · Fifth International Workshop on Natural… · 2017

Spread & networksDetection methods Topological Recurrent Neural Network for Diffusion PredictionWang et al. propose Topo-LSTM, a novel LSTM architecture for modeling dynamic directed acyclic graphs (DAGs) to predict which node will be activated next in information cascades. The key insight is that information diffusion on networks should be modeled explicitly as diffusion topologies—graphs showing how information spreads from… Wang et al. · ICDM 2017 · 2017

Datasets & evaluationFact-checking & verification Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News DetectionThis paper introduces LIAR, a benchmark dataset of 12,836 manually labeled short statements from POLITIFACT.COM spanning 2007–2016, addressing the critical lack of labeled data for fake news detection. Each statement is annotated with fine-grained veracity labels (pants-fire, false, barely-true, half-true, mostly-true, true) and rich… Wang · arXiv · 2017

Detection methodsSpread & networks Some Like it Hoax: Automated Fake News Detection in Social NetworksHoaxes on Facebook can be detected with 99% accuracy using user interaction patterns (who "liked" posts) rather than content analysis. Two algorithms—logistic regression and harmonic boolean crowdsourcing—achieve high accuracy even with minimal labeled training data, and knowledge transfers across different Facebook communities. Tacchini et al. · Technical Report, UC Santa Cruz · 2017

Fact-checking & verificationDatasets & evaluation Finding Streams in Knowledge Graphs to Support Fact CheckingKnowledge Stream (KS) is an unsupervised network-flow approach for computational fact-checking that treats a knowledge graph as a flow network. For a triple (subject, predicate, object), KS finds multiple paths connecting subject to object and uses edge capacities (based on relational similarity and node specificity) to compute a truth… Shiralkar et al. · arXiv · 2017

Spread & networksDetection methods Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTMThis paper proposes Branch-LSTM, a sequential neural model for rumour stance classification in Twitter conversations. By decomposing conversation threads into linear branches and modeling them with LSTM layers, the approach captures conversational context while assigning stance labels (Support/Deny/Query/Comment) to each tweet. Kochkina et al. · 11th International Workshop on Semantic… · 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

Bots & coordinated campaignsDatasets & evaluation Online Human-Bot Interactions: Detection, Estimation, and CharacterizationSocial media accounts controlled by software can mimic human behavior while executing coordinated campaigns. This paper develops a machine-learning framework extracting 1,150 behavioral features from Twitter account metadata, posting patterns, and social networks to classify bots with 95% accuracy on honeypot data. Varol et al. · arXiv · 2017

Detection methodsDatasets & evaluation Fake News Detection on Social Media: A Data Mining PerspectiveThis comprehensive survey reviews fake news detection on social media through a data mining lens. It frames the problem around two key aspects—characterization (understanding the psychological and social foundations of fake news) and detection (building computational methods using news content and social context features). Shu et al. · arXiv · 2017

Detection methodsLLMs & generative AI CSI: A Hybrid Deep Model for Fake News DetectionCSI is a three-module deep learning model that detects fake news by jointly modeling article text, temporal patterns of user engagement (response), and user behavior patterns (source). The Capture module uses LSTM to learn temporal response signals; the Score module learns user suspiciousness from co-engagement patterns; the Integrate… Ruchansky et al. · CIKM'17 · 2017

Detection methodsFact-checking & verification Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-CheckingThis paper performs a linguistic analysis of fake news across four categories—satire, hoaxes, propaganda, and trusted news—using lexical features (LIWC, subjectivity, hedging, intensifiers). Experiments show that fake news articles use more first-person pronouns, exaggerative language (superlatives, modal adverbs), and hedging, while… Rashkin et al. · 2017 Conference on Empirical Methods in… · 2017

Detection methodsDatasets & evaluation A Stylometric Inquiry into Hyperpartisan and Fake NewsThis paper investigates whether writing style alone can distinguish hyperpartisan news from mainstream and fake news from real news. Using a manually fact-checked corpus of 1,627 articles from BuzzFeed, the authors find that hyperpartisan news is stylistically distinct from mainstream (F1 = 0.78), satire is easily distinguishable from… Potthast et al. · arXiv · 2017

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 & 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 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

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

Detection methodsFact-checking & verification Fact Checking: Task definition and dataset constructionThis paper introduces fact-checking as a formal computational task: assessing the truthfulness of political claims as an ordinal classification problem with five labels (TRUE, MOSTLYTRUE, HALFTRUE, MOSTLYFALSE, FALSE). The authors construct a dataset of 106 objectively-verifiable fact-checked statements from PolitiFact and Channel 4 (out… Vlachos & Riedel · ACL 2014 Workshop on Language Technologies… · 2014

Spread & networksDetection methods Can Cascades be Predicted?Information cascades on social networks can be predicted with reasonable accuracy (~80%) using temporal and structural features extracted from early observations. Performance improves substantially as more cascade stages are observed. Cheng et al. · International Conference on Web and Social Media · 2014

Detection methodsSpread & networks Information Credibility on TwitterThis paper proposes supervised learning classifiers to automatically assess the credibility of news discussed on Twitter. Using human-labeled topics from trending discussions, the authors extract message-level, user-level, topic-level, and propagation-level features to build classifiers that distinguish newsworthy from conversational… Castillo et al. · World Wide Web Conference · 2011