280 characters to the White House: predicting 2020 U.S. presidential elections from twitter data.

Abstract

This nation-shaping election of 2020 plays a vital role in shaping the future of the U.S. and the entire world. With the growing importance of social media, the public uses them to express their thoughts and communicate with others. Social media have been used for political campaigns and election activities, especially Twitter. The researchers intend to predict presidential election results by analyzing the public stance toward the candidates using Twitter data. Previous researchers have not succeeded in finding a model that simulates well the U.S. presidential election system. This manuscript proposes an efficient model that predicts the 2020 U.S. presidential election from geo-located tweets by leveraging the sentiment analysis potential, multinomial naive Bayes classifier, and machine learning. An extensive study is performed for all 50 states to predict the 2020 U.S. presidential election results led by the state-based public stance for electoral votes. The general public stance is also predicted for popular votes. The true public stance is preserved by eliminating all outliers and removing suspicious tweets generated by bots and agents recruited for manipulating the election. The pre-election and post-election public stances are also studied with their time and space variations. The influencers' effect on the public stance was discussed. Network analysis and community detection techniques were performed to detect any hidden patterns. An algorithm-defined stance meter decision rule was introduced to predict Joe Biden as the President-elect. The model's effectiveness in predicting the election results for each state was validated by the comparison of the predicted results with the actual election results. With a percentage of 89.9%, the proposed model showed that Joe Biden dominated the electoral college and became the winner of the U.S. presidential election in 2020.