The paper reports a comprehensive study of transformer-based NLP models for detecting check-worthy social media posts, covering data collection, preprocessing, architecture selection, fine‑tuning, testing, and implementation. It focuses on multilingual models that can process English and low‑resource languages such as Arabic, Bulgarian, Dutch, Polish, Czech, and Slovak, and compares their performance to state‑of‑the‑art baselines. The work introduces multi‑label multilingual classifiers that simultaneously identify harmful content and posts containing verifiable factual claims efficiently.
arXiv:2610.20370v1 Announce Type: new Abstract: Hate speech has become increasingly prevalent on social media, raising concerns about its detection, interpretation, and moderation. Persian, a relativ...
The paper introduces SWARM, a multilingual dataset of 2,183 search engine results in nine languages, annotated for support of Russian propaganda narratives. It evaluates a source-based blocklist, supervised classifiers, and zero‑shot large language models, finding that blocklists miss most propaganda and that content‑level models vary in performance, with the best LLM achieving an F1 of 0.73. The study highlights the need for per‑language, content‑level detection of search‑borne propaganda.
A new large-scale Arabic fact‑checking dataset called Arafa has been created using an automated pipeline that generates claims from Arabic Wikipedia, mutates them into counterfactuals, and validates them against supporting or refuting evidence. The dataset contains 181,976 claim‑evidence pairs labeled as supported, refuted, or not enough information, and human evaluation shows high inter‑annotator agreement and strong validation accuracy. Fine‑tuned transformer models on Arafa achieve a Macro F1‑score of 77%, demonstrating its usefulness for Arabic fact‑checking tasks.
arXiv:2610.15795v1 Announce Type: cross Abstract: Automated moderation systems are deployed at scale, yet they remain opaque to the users and moderators affected by their decisions, and their coverag...
arXiv:2608. 09510v1 Announce Type: cross Abstract: Detecting machine-generated disinformation on social media is increasingly difficult as large language models (LLMs) make it easier to generate and rewrite misleading content at scale.
arXiv:2608. 05183v1 Announce Type: cross Abstract: This dissertation analysed and discussed the differences in linguistic characteristics between pre-mortem and post-mortem social media content, and reported machine learning (ML) classifiers that achieved high performance in automatically detecting deaths of social networking site users from posts associated with their profiles.
AfrIFact is a new dataset designed to support automatic fact‑checking in ten African languages and English, covering information retrieval, evidence extraction, and fact verification. Experiments show that current embedding models struggle with cross‑lingual retrieval, and that cultural and news documents are easier to retrieve than healthcare documents. Large language models also lack robust multilingual fact‑verification, but few‑shot prompting and task‑specific fine‑tuning can significantly improve performance.
The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.
The paper presents a large‑scale study of ragebait—content designed to provoke anger—on Japanese posts on X. It introduces a labeled dataset created with a large language model, trains Japanese language models, and builds an ensemble classifier that detects ragebait. Applying this detector to a vast dataset reveals that ragebait is especially common in politically and socially contentious topics, spreads faster, and elicits stronger negative emotions than non‑ragebait posts.
arXiv:2610.05275v2 Announce Type: replace Abstract: A large part of political discourse on social media is built and expressed at a level of claims: i.e. declarative, typically single-clause statemen...
The paper introduces REGLAT, an ensemble-based model that integrates named entity recognition (GLiNER) with a fine‑tuned MARBERT classifier to detect harmful content in Arabic. It classifies user prompts into seven harm categories, achieving a macro‑F1 of 0.886 and accuracy of 0.897 on the ArGuard 2026 development set, with slightly lower scores on blind test sets. The study highlights that combining explicit entity extraction with contextual language modeling improves fine‑grained harmful content detection, though distinguishing closely related categories remains difficult.
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.