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.
By Manuel Tonneau, Abhinav Dubey, Farhan Shaikh, Ilaria Vitulano, Martha Stolze, Hale Dedeoglu, Clara Riechert, Ella Kuka, Maryna Sydorova, Mykola Makhortykh, Elizaveta Kuznetsova
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:2606. 11898v1 Announce Type: cross Abstract: Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenarios, such as citation networks, e-commerce platforms, social media, and web pages.
By Hengyi Feng, Zeang Sheng, Meiyi Qiang, Meiyi Qiang, Wentao Zhang
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...
By Sandrine Chausson, Bj\"orn Ross
arXiv:2610.17469v1 Announce Type: cross
Abstract: Social media platforms generate vast volumes of user-generated content that often obscure meaningful discourse, a phenomenon conceptualized as social...
By Manar Alsaid
The study examines how political narratives formed and spread on X, Facebook, and Reddit during the 2024 Moldovan presidential election. Using multilingual stance detection, network analysis, and causal inference, it finds distinct cross‑platform differences in positions on European integration, Russia, and the candidates, and shows that amplification and peer exposure significantly reinforce these narratives. This is the first comprehensive analysis of social media discourse for this election, highlighting the role of language, network dynamics, and causal effects in shaping political influence.
By Andrea Piras, Osman Rahmoun, Elena Zheleva