The paper introduces an unsupervised framework that identifies and characterizes competing narratives in political discourse on social media, specifically analyzing German politicians' tweets. It uses a multi‑stage pipeline incorporating topic modeling, event detection, and event linking to form coherent stories and reveal distinct user community perspectives. Two case studies on polarizing issues demonstrate the method’s effectiveness in uncovering divergent viewpoints and framing conflicts around trending political topics.
By Sergej Wildemann, Erick Elejalde
The study examines how language influences AI chatbot responses to questions about the war in Ukraine, revealing that the same AI systems (GPT, Claude, Gemini) produce varying political stances across 112 languages. By evaluating 20 statements in 112 languages, the researchers found that Russia‑leaning versus Ukraine‑leaning answers differ by language, mirroring global political attitudes such as public support for Russia, UN voting patterns, and aid levels. This pattern persists across all three models and even when specific statement pairs are removed, suggesting that information warfare could embed geopolitical biases into AI training data.
By Maxim Chupilkin
The paper introduces TSN4PI, a unified framework for tracking the evolution of political ideologies on social media. It combines a PIDN module that uses large language models, style transfer, and unsupervised domain adaptation to detect ideologies and filter noise, with a PIPN module that employs temporal graph neural networks to predict future ideological shifts. The authors release two large-scale datasets and validate the approach on platforms such as X and Truth Social, offering empirical insights into political polarization and ideology evolution.
The paper introduces TSN4PI, a unified framework for tracking the evolution of political ideologies on social media. It combines a PIDN module that uses large language models, style transfer, and unsupervised domain adaptation to detect ideologies and filter noise, with a PIPN module that employs temporal graph neural networks to predict future ideological shifts. The authors release two large-scale datasets and validate the approach on platforms such as X and Truth Social, offering empirical insights into political polarization and online ideology evolution.
By Yijie Xu, Chao Wang, Hui Xiong
arXiv:2405. 17838v3 Announce Type: replace-cross Abstract: Socio-linguistic indicators of affectively-relevant phenomena, such as emotion or sentiment, are often extracted from text to better understand features of human-computer interactions, including on social media.
By Keith Burghardt, Daniel M. T. Fessler, Chyna Tang, Anne Pisor, Kristina Lerman
arXiv:2609.36290v1 Announce Type: cross
Abstract: We investigate the extent to which the Hamas attacks on Israel of October 7, 2023, have affected German social media debates about Judaism and Israel...
By Gregor Wiedemann, Daniel Wehrend
arXiv:2608. 05155v1 Announce Type: cross Abstract: Traditional sentiment analysis (SA) models, while effective for polarity classification, provide limited insight into the rhetorical, ideological, and framing dimensions of political discourse -- dimensions that are central to research in the social sciences and humanities (SSH).
By Maryam Fooladi, Federico Bottino
arXiv:2608.21385v1 Announce Type: cross
Abstract: Social media has become a central arena in which armed conflicts are contested, yet the pro-Israel and pro-Palestine communities on Telegram, whose b...
By Michail Zafeiropoulos, Despoina Antonakaki, Sotiris Ioannidis
arXiv:2607. 21842v1 Announce Type: cross Abstract: Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content.
By Fathima Ameen, Christopher G. Healey
arXiv:2607.04962v2 Announce Type: replace
Abstract: Conspiracy theories commonly attribute important events to the actions of powerful and secretive actors. While computational research has largely f...
By Helena Mihaljevi\'c, Jolanda Beer, Mareike Lisker, Katharina Soemer
arXiv:2609.38256v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's...
By Olivia Macmillan-Scott, Michael Jacobs, Nils Metternich, Mirco Musolesi
arXiv:2512. 15792v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have rapidly become indispensable tools for acquiring information and supporting human decision-making.
By Xulang Zhang, Rui Mao, Erik Cambria