A Collaborative and Real-Time Model for Trusties Content in Social Media-Reference-Cited by-同舟云学术

A Collaborative and Real-Time Model for Trusties Content in Social Media

Published:2024-05-14 Issue: Volume: Page:
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Author:

HAMIMED Lyazid¹,AMAD Mourad²,BOUDRIES Abdelmalek¹

Affiliation:

1. University of Béjaïa

2. University of Bouira

Abstract

Recently, social media is becoming a stronger tool for spreading news in the world. These platforms make it easy for anyone to disseminate their ideas, flood the world by different types of information. In order to minimize the inconvenience of fake news inundation, most of the developed techniques aim at detecting fake news by exploring how they propagate on the social media. Minimizing the negative effect of this kind of information, needs stronger mechanisms to detect fake news at an early stage by focusing on their contents. This paper proposes a new model for trusties’ content in social media. Its basic idea consists of combining news content and their propagation behavior over the social network. This model simulation shows that the susceptible fake news can be accused at an early stage. The performance evaluations show that the results are globally satisfactory.

Publisher

Research Square Platform LLC

Reference27 articles.

1. Brendan, N.: Reifler Jason. Estimating fact-checking’s effects. American Press Institute, Arlington, VA (2015)

2. James, T.: Vlachos Andreas. Automated Fact Checking: Task Formulations, Methods and Future Directions, Proceedings of the 27th International Conference on Computational Linguistics. 3346–3359. (2018)

3. Thorne James, V., Andreas, C., Christos, M., Arpit: FEVER: a Large-scale Dataset for Fact Extraction and VERification, Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018. 809–819. (2018)

4. Zellers Rowan, H., Ari, R., Hannah, B., Yonatan, F., Ali: Roesner Franziska, Choi Yejin. Defending Against Neural Fake News, arXiv preprint arXiv:1905.12616. (2019)

5. Baly Ramy, K., Georgi, A., Dimitar, G., James: Nakov Preslav. Predicting Factuality of Reporting and Bias of News Media Sources, Proceedings of the Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, X-XI 2018. 3528–3539. (2018)