@inproceedings{9f39f8ac5b784e0a8943f1b17de9ebe1,
title = "A Comparative Study Between Rule-Based and Transformer-Based Election Prediction Approaches: 2020 US Presidential Election as a Use Case",
abstract = "Social media platforms (SMPs) attracted people from all over the world for they allow them to discuss and share their opinions about any topic including politics. The comprehensive use of these SMPs has radically transformed new-fangled politics. Election campaigns and political discussions are increasingly held on these SMPs. Studying these discussions aids in predicting the outcomes of any political event. In this study, we analyze and predict the 2020 US Presidential Election using Twitter data. Almost 2.5 million tweets are collected and categorized into Location-considered (LC) (USA only), and Location-unconsidered (LUC) (either location not mentioned or out of USA). Two different sentiment analysis (SA) approaches are employed: dictionary-based SA, and transformers-based SA. We investigated if the deployment of deep learning techniques can improve prediction accuracy. Furthermore, we predict a vote-share for each candidate at LC and LUC levels. Afterward, the predicted results are compared with the five polls{\textquoteright} predicted results as well as the real results of the election. The results show that dictionary-based SA outperformed all the five polls{\textquoteright} predicted results including the transformers with MAE 0.85 at LC and LUC levels, and RMSE 0.867 and 0.858 at LC and LUC levels.",
keywords = "Deep learning, Rule-based, Sentiment analysis, Transformers, Twitter, USA election, Vote-share",
author = "Asif Khan and Huaping Zhang and Nada Boudjellal and Lin Dai and Arshad Ahmad and Jianyun Shang and Philipp Haindl",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2022, 4th International Workshop on Machine Learning and Knowledge Graphs, MLKgraphs 2022, 2nd International Workshop on Time Ordered Data, ProTime2022, 2nd International Workshop on AI System Engineering: Math, Modelling and Software, AISys2022, 1st International Workshop on Distributed Ledgers and Related Technologies, DLRT2022 and 1st International Workshop on Applied Research, Technology Transfer and Knowledge Exchange in Software and Data Science, ARTE2022 held at 33rd International Conference on Database and Expert Systems Applications, DEXA 2022 ; Conference date: 22-08-2022 Through 24-08-2022",
year = "2022",
doi = "10.1007/978-3-031-14343-4_4",
language = "English",
isbn = "9783031143427",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "32--43",
editor = "Gabriele Kotsis and Ismail Khalil and Atif Mashkoor and Johannes Sametinger and Tjoa, {A Min} and Bernhard Moser and Jorge Martinez-Gil and Florian Sobieczky and Lukas Fischer and Rudolf Ramler and Gerald Czech and Alfred Taudes and Maqbool Khan",
booktitle = "Database and Expert Systems Applications - DEXA 2022 Workshops - 33rd International Conference, DEXA 2022, Proceedings",
address = "Germany",
}
