Title: Classifying COVID-19-related hate Twitter users using deep neural networks with sentiment-based features and geopolitical factors

Authors: Peng Zhao; Xi Chen; Xin Wang

Addresses: Big Data and AI Lab, IntelligentRabbit LLC, NJ, USA ' School of Humanity and Law, Beijing University of Civil Engineering and Architecture, Beijing, China ' Big Data and AI Lab, IntelligentRabbit LLC, NJ, USA

Abstract: Anti-Asian hate tweets caused by COVID-19 pandemic is an ongoing social problem in the USA and around the world. Although existing studies have been done by using a text classifier, little is known on how deep learning works with public sentiments of political opinions and geographical diversities. This paper provides a new method to classify the pandemic-related anti-Asian hater on Twitter. A novel dataset for tracking pandemic-related Twitter users, which contains more than 10 million tweets, is created in this study. Target users are annotated by identifying their sentiments towards the US elections with their geolocations. The empirical result indicates that the political sentiments and the county-level election results make significant contributions to the model building. By training a DNN model, over 190,000 Twitter users are classified as hate or non-hate with a 61% accuracy and a 0.63 AUC score.

Keywords: pandemic-related hate; anti-Asian tweets; sentiment analysis; GIS; deep neural network algorithm.

DOI: 10.1504/IJSSS.2021.116373

International Journal of Society Systems Science, 2021 Vol.13 No.2, pp.125 - 139

Received: 01 Dec 2020
Accepted: 15 Dec 2020
Published online: 21 Jul 2021 *

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