An Analysis of Twitter Users’ Political Views Using Cross-Account Data Mining Shivram Ramkumar, Alexander Sosnkowski, David Coffman, Carol Fung and Jason Levy 1 Introduction The 2016 US presidential election resulted in a polarized country split between two conflicting and unorthodox political views. Traditional polling methods failed to predict the outcome of the election due to the massive political affiliation change among voters. Few studies have been conducted to follow up the political view migration of a large-scale population after the election. In the USA, more than 70% of the population are social media users [1] and as one of the most popular social network Web sites, Twitter has exceeded a hundred million daily users and nearly one billion total users [1]. This diverse range on one universal platform leads to the representation of almost any political view. Garimella et al. [2] studied the behaviors of US Twitter users and found that a trend of increased political polarization has been formed in the past many years. Therefore, as a more accurate predictor for the upcoming election year and possible future elections, it could be beneficial to look at the change in the opinions of users across a relatively long period of time. In this paper, we conducted a study on the political view migration through following up a selected group of Twitter users over the period of three years. Our contribution of this paper can be summarized as follows: (1) This is the first work of the same kind to analyze the political views of Twitter users through political score computation. (2) We developed an iterative political score computation algorithm through cross-account data mining on Twitter accounts. (3) We conducted our analysis based on real data on Twitter user behaviors. S. Ramkumar · A. Sosnkowski · C. Fung (B ) Virginia Commonwealth University, Richmond, VA, USA e-mail: cfung@vcu.edu D. Coffman Duke University, Durham, NC, USA J. Levy University of Hawaii, Honolulu, HI, USA © Springer Nature Singapore Pte Ltd. 2020 J. Fiaidhi et al. (eds.), Smart Technologies in Data Science and Communication, Lecture Notes in Networks and Systems 105, https://doi.org/10.1007/978-981-15-2407-3_16 115