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 long short-term memory (LSTM) models and variants known as bidirectional encoder representations from transformers (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.

中文翻译：

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拜登 vs 特朗普：使用 BERT 语言模型模拟美国大选


社交媒体在竞选期间塑造世界观方面发挥着至关重要的作用。社交媒体已被用作政治运动的媒介和组织抗议的工具；其中一些是和平的，而另一些则导致了骚乱。先前的研究表明，了解用户行为，特别是在选举期间表达的情绪方面，可以预示选举结果。最近，通过长短期记忆 (LSTM) 模型和称为变压器双向编码器表示 (BERT) 的变体，深度学习的语言建模取得了巨大进展。在这些创新的推动下，我们开发了一个框架来模拟美国大选。我们调查情绪分析是否可以提供预测选举结果的方法。我们使用 LSTM 和 BERT 语言模型对 2020 年美国总统大选的 Twitter 情绪进行分析。我们的结果表明，情绪分析可以为选举结果建模提供一般基础，其中 BERT 模型表明拜登赢得了选举。