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Authors: Vítor Bernardes 1 and Álvaro Figueira 2

Affiliations: 1 Faculty of Sciences, University of Porto, Rua do Campo Alegre, Porto, Portugal ; 2 CRACS / INESCTEC, University of Porto, Porto, Portugal

Keyword(s): Fake News, Social Media, Machine Learning, NLP.

Abstract: The recent proliferation of so called “fake news” content, assisted by the widespread use of social media platforms and with serious real-world impacts, makes it imperative to find ways to mitigate this problem. In this paper we propose a machine learning-based approach to tackle it by automatically identifying tweets associated with questionable content, using newly-collected data from Twitter about the 2020 U.S. presidential election. To create a sizable annotated data set, we use an automatic labeling process based on the factual reporting level of links contained in tweets, as classified by human experts. We derive relevant features from that data and investigate the specific contribution of features derived from named entity and emotion recognition techniques, including a novel approach using sequences of prevalent emotions. We conclude the paper by evaluating and comparing the performance of several machine learning models on different test sets, and show they are applicable to addressing the issue of fake news dissemination. (More)

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Paper citation in several formats:
Bernardes, V. and Figueira, Á. (2021). A Mixed Model for Identifying Fake News in Tweets from the 2020 U.S. Presidential Election. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST, ISBN 978-989-758-536-4; ISSN 2184-3252, pages 307-315. DOI: 10.5220/0010660500003058

@conference{webist21,
author={Vítor Bernardes. and Álvaro Figueira.},
title={A Mixed Model for Identifying Fake News in Tweets from the 2020 U.S. Presidential Election},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST,},
year={2021},
pages={307-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010660500003058},
isbn={978-989-758-536-4},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST,
TI - A Mixed Model for Identifying Fake News in Tweets from the 2020 U.S. Presidential Election
SN - 978-989-758-536-4
IS - 2184-3252
AU - Bernardes, V.
AU - Figueira, Á.
PY - 2021
SP - 307
EP - 315
DO - 10.5220/0010660500003058