Titlebook: Disinformation in Open Online Media; 5th Multidisciplinar Davide Ceolin,Tommaso Caselli,Marina Tulin Conference proceedings 2023 The Editor

Myocyte

El Teatro Espa?ol (Federico García Lorca)ts that introduce the misleading information, their supporting media, the nodes they use in social networks, the propaganda techniques they employ and their overall narratives and intentions. Disinformation is a challenge that must be addressed holistically: identifying and describing a disinformati

苦澀

The Commonwealth Of The Bahamas,media platforms, including text, photos, videos, and links, necessitates a multimodal analysis approach. With the emergence of Large Language Models (LLMs), there is now an opportunity to convert image content into textual descriptions, enabling the incorporation of previously text-based methods int

欄桿

Corroborate

El Teatro Espa?ol (Federico García Lorca)a possible effect might be the result of (a) the attenuation of emotional responses in a foreign language and whether (b) it affects individuals depending on their need for cognition. In an online experimental study with N = 134 participants, we presented a news article either in the participants’ n

鐵砧

https://doi.org/10.1007/978-3-031-47896-3artificial intelligence; computer networks; misinformation; computer security; disinformation; correlatio

Ordeal

否決

Disinformation in Open Online Media978-3-031-47896-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

limber

El Teatro Espa?ol (Federico García Lorca)r effectiveness. Textual features have shown their potential to help identify fake news in a transparent manner. In this paper, we survey a list of textual features, evaluate their usefulness in predicting fake news by testing them on a real-world dataset, and collect them in a Python library called “faKy”.

CLIFF

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/281340.jpg

植物群

,FaKy: A Feature Extraction Library to?Detect the?Truthfulness of?a?Text,r effectiveness. Textual features have shown their potential to help identify fake news in a transparent manner. In this paper, we survey a list of textual features, evaluate their usefulness in predicting fake news by testing them on a real-world dataset, and collect them in a Python library called “faKy”.