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pro vyhledávání: '"Bontcheva, Kalina"'
Autor:
Srba, Ivan, Razuvayevskaya, Olesya, Leite, João A., Moro, Robert, Schlicht, Ipek Baris, Tonelli, Sara, García, Francisco Moreno, Lottmann, Santiago Barrio, Teyssou, Denis, Porcellini, Valentin, Scarton, Carolina, Bontcheva, Kalina, Bielikova, Maria
In the current era of social media and generative AI, an ability to automatically assess the credibility of online social media content is of tremendous importance. Credibility assessment is fundamentally based on aggregating credibility signals, whi
Externí odkaz:
http://arxiv.org/abs/2410.21360
This work introduces EUvsDisinfo, a multilingual dataset of disinformation articles originating from pro-Kremlin outlets, along with trustworthy articles from credible / less biased sources. It is sourced directly from the debunk articles written by
Externí odkaz:
http://arxiv.org/abs/2406.12614
Large language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling and closed-set topic classification approaches. As zero-shot topic extractors, LLMs are expected to understan
Externí odkaz:
http://arxiv.org/abs/2405.00611
Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic topic modelling approaches (e.g., LDA) have certain drawbacks, such as
Externí odkaz:
http://arxiv.org/abs/2403.16248
As Large Language Models (LLMs) become more proficient, their misuse in large-scale viral disinformation campaigns is a growing concern. This study explores the capability of ChatGPT to generate unconditioned claims about the war in Ukraine, an event
Externí odkaz:
http://arxiv.org/abs/2402.08467
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks. Typically, when annotating such tasks annotators are only asked to provide a single label for each sample an
Externí odkaz:
http://arxiv.org/abs/2311.05265
Publikováno v:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5729-5741
This paper analyses two hitherto unstudied sites sharing state-backed disinformation, Reliable Recent News (rrn.world) and WarOnFakes (waronfakes.com), which publish content in Arabic, Chinese, English, French, German, and Spanish. We describe our co
Externí odkaz:
http://arxiv.org/abs/2310.14032
The use of abusive language online has become an increasingly pervasive problem that damages both individuals and society, with effects ranging from psychological harm right through to escalation to real-life violence and even death. Machine learning
Externí odkaz:
http://arxiv.org/abs/2309.14146
A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors. Past research has indicated that content-based (i.e., using solely source posts as input) rumor detectio
Externí odkaz:
http://arxiv.org/abs/2309.11576
Credibility signals represent a wide range of heuristics typically used by journalists and fact-checkers to assess the veracity of online content. Automating the extraction of credibility signals presents significant challenges due to the necessity o
Externí odkaz:
http://arxiv.org/abs/2309.07601