Biden vs Trump: Modeling US General Elections Using BERT Language Model

Social media plays a crucial role in shaping the worldview during election campaigns. Social media has been used as a medium for political campaigns and a tool for organizing protests; some of which have been peaceful, while others have led to riots. Previous research indicates that understanding user behaviour, particularly in terms of sentiments expressed during elections can give an indication of the election outcome. Recently, there has been tremendous progress in language modelling with deep learning via <italic>long short-term memory</italic> (LSTM) models and variants known as <italic>bidirectional encoder representations from transformers</italic> (BERT). Motivated by these innovations, we develop a framework to model the US general elections. We investigate if sentiment analysis can provide a means to predict election outcomes. We use the LSTM and BERT language models for Twitter sentiment analysis leading to the US 2020 presidential elections. Our results indicate that sentiment analysis can provide a general basis for modelling election outcomes where the BERT model indicates Biden winning the elections.

Abstract licence: CC BY-NC-ND

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