The paper presents a theory-informed computational framework that converts cross-disciplinary theories of fake news into measurable features for automated detection and explanation. By reviewing theories from social sciences, psychology, economics, and more, the authors establish a broad theoretical foundation for computational modeling. Experiments on benchmark datasets demonstrate that theory-derived features are predictive, provide interpretable diagnostic signals, and that multi-feature models generally outperform individual features, though gains are modest.
By Zhaoyang Cao, Miriam Metzger, Reza Zafarani
Web domain credibility evaluation is vital for combating misinformation. It is conducted by examining factors such as domain type, transparency, and overall reputation.
FakeSpotter is a new tool that estimates the viral misinformation risk of textual content by measuring structural fingerprints of misinformation instead of directly judging truthfulness. It operates across linguistic, narrative, logical, and critical‑thinking dimensions, using repeated large language model assessments and domain‑specific logistic regression classifiers for both short and long texts. In a labeled corpus of 764 texts, FakeSpotter achieved macro F1 scores of 0.788 for short texts and 0.793 for long texts, and its interpretive layer offers explainable outputs such as feature‑based scores, signal agreement, and a caution index for social listening.
By Giovanni Spitale, Federico Germani
arXiv:2501. 14728v2 Announce Type: replace-cross Abstract: While generative artificial intelligence (GenAI) models have achieved significant success, their misuse for generating deceptive content raises growing concerns about online information security.
By Zehong Yan, Peng Qi, Wynne Hsu, Mong Li Lee
arXiv:2609.36902v1 Announce Type: new
Abstract: Existing multimodal fake news detection methods often introduce external information to assist detection. However, most of them rely on entity-level re...
By Wenbin Shen, Guoxuan Qin, Guangxu Yao, Baodong Wang, Yuanbo Rui, Zhongjie Ba, Zhichao Lian
arXiv:2608.29617v1 Announce Type: cross
Abstract: This paper introduces a hybrid fact-checking framework that integrates Knowledge Graph-based semantic memory with adversarial multi-agent reasoning f...
By Amelia Petrenciuc, Alexandru Lecu, Adrian Groza
arXiv:2608.30311v1 Announce Type: cross
Abstract: Social media platforms increasingly use AI-based credibility indicators to help users judge misinformation. Unlike individual human-AI decision-makin...
By Zhuoran Lu, Weilong Wang, Yangyang Yu, Xinru Wang, Zhuoyan Li, Zhiwei Liu, Sophia Ananiadou
The paper presents the Temporal Coherence Score (TCS), a continuous, interpretable metric for detecting temporal inconsistencies in political news. TCS is computed through a four‑stage pipeline that extracts temporal facts, builds a temporal knowledge graph, verifies consistency using internal rules and external references, and aggregates scores with explanations. On a benchmark of 100 political articles with injected errors, TCS achieves 0.909 precision, providing detailed explanations for each flagged inconsistency.
By Marius Nicusor Pantea, Adrian Groza
The paper presents an agentic framework for detecting conspiratorial content in social media by inferring the speaker’s intent rather than merely identifying explicit claims. It leverages social context and adaptive tool use, demonstrating superior performance over text-only and non-agentic models on a large Hebrew tweet dataset spanning election cycles and the COVID pandemic. The study highlights the importance of context-aware, reasoning-driven approaches for accurate conspiracy detection.
By Lior Biton, Oren Tsur
arXiv:2609.20838v1 Announce Type: new
Abstract: In this study, we examine how modern LLMs generate and detect fake news under controlled settings across four manipulation scenarios. These are open-en...
By Zeynep \"Ozdemir, Murat Osmano\u{g}lu, Sevgi Yi\u{g}it-Sert, \"Omer \"Ozg\"ur Tanr{\i}\"over, Y{\i}lmaz Ar
arXiv:2607. 11894v1 Announce Type: cross Abstract: Detecting disinformation narratives on social media is challenging due to the scale of amplification, rapid evolution, and linguistic variability of online content.
By Yuliia Vistak, Viktoriia Makovska, Vera Schmitt, Veronika Solopova
OpenAI researchers collaborated with Georgetown University’s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and culminated in a co-authored report building on more than a year of research.