Evaluating Trustworthiness of Online News Publishers via Article Classification-Reference-Cited by-同舟云学术

Evaluating Trustworthiness of Online News Publishers via Article Classification

Published:2024-04-08 Issue: Volume: Page:671-678
ISSN:
Container-title:Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing
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Author:

Bianchi John¹^ORCID,Pratelli Manuel¹^ORCID,Petrocchi Marinella²^ORCID,Pinelli Fabio¹^ORCID

Affiliation:

1. IMT School for Advanced Studies Lucca, Lucca, Toscana, Italy

2. Institute of Informatics and Telematics, National Research Council, Pisa, Toscana, Italy

Funder

Project SERICS (PE00000014) under the NRRP MUR program funded by the EU - NGEU

IMT Integrated Activity Project TOFFEe (TOols for Fighting FakEs)

IIT-CNR funded Project re-DESIRE (DissEmination of ScIentific REsults 2.0)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3605098.3636044

Reference32 articles.

1. Parisa Bazmi, Masoud Asadpour, and Azadeh Shakery. 2023. Multi-view co-attention network for fake news detection by modeling topic-specific user and news source credibility. Information Processing & Management 60, 1 (2023).

2. Matyáš Boháček, Michal Bravanskỳ, Filip Trhlík, and Václav Moravec. 2022. Fine-grained Czech News Article Dataset: An Interdisciplinary Approach to Trustworthiness Analysis. arXiv preprint arXiv:2212.08550 (2022).

3. S. Bowman and C Willis. 2003. We Media: How Audiences are Shaping the Future of News and Information. The Media Center at the American Press Institute..

4. Michael Butter and Peter Knight. 2021. Routledge Handbook of Conspiracy Theories. Routledge.

5. Dallas Card et al. 2015. The Media Frames Corpus: Annotations of Frames Across Issues. In 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics 438--444. 10.3115/v1/P15-2072