@inproceedings{b16c09400d9a4451baafd589e4820043,
title = "Impact of COVID-19 on Predicting 2020 US Presidential Elections on Social Media",
abstract = "By the beginning of 2020, the world woke up to a global pandemic that changed people{\textquoteright}s everyday lives and restrained their physical contact. During those times Social Media Platforms (SMPs) were almost the only mean of individual-to-individual and government-to-individuals communications. Therefore, people{\textquoteright}s opinions were more expressed on SM. On the other hand, election candidates used SM to promote themselves and engage with voters. In this study, we investigate how COVID-19 affected voters{\textquoteright} opinions through the months of the US presidential campaign and eventually predict the 2020 US Presidential Election results using Twitter{\textquoteright}s data. Mainly two types of experiments were conducted and compared; (i) transformer-based, and (ii) rule-based sentiment analysis (SA). In addition, vote shares for the presidential candidates using both approaches were predicted. The results show that the rule-based approach nearly predicts the right winner, Joe Biden with MAE 2.1, outperforming the predicted results from CNBC, Economist/YouGov, and transformer-based (BERTweet) approach, except for RCP (MAE 1.55).",
keywords = "COVID-19, Election Prediction, Rule-based, Sentiment Analysis, Transformers, Twitter, USA Presidential Election",
author = "Asif Khan and Huaping Zhang and Nada Boudjellal and Bashir Hayat and Lin Dai and Arshad Ahmad and Ahmed Al-Hamed",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 16th International Conference on Information Technology and Applications, ICITA 2022 ; Conference date: 20-10-2022 Through 22-10-2022",
year = "2023",
doi = "10.1007/978-981-19-9331-2_14",
language = "English",
isbn = "9789811993305",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "163--173",
editor = "Sajid Anwar and Abrar Ullah and {\'A}lvaro Rocha and Sousa, {Maria Jos{\'e}}",
booktitle = "Proceedings of International Conference on Information Technology and Applications - ICITA 2022",
address = "Germany",
}