An analysis of Twitter users’ long term political view migration using cross-account data mining

Abstract

During the 2016 US presidential election, we witnessed a polarized population and an election outcome that defied the predictions of many media sources. In this study, we conducted a follow-up on political view migration through tracking Twitter users’ account activity. The study was conducted by following a set of Twitter users over a four year period. Each year, Twitter user activities were collected and analyzed by our novel cross-account data mining algorithm. This algorithm through multiple iterations computes a numerical political score for each user based on their connection to other users and hashtags. We identified a set of seed users and hashtags using prominent political figures and movements to bootstrap the algorithm. The political score distribution demonstrates a divided population on political views. We also observed that users are more moderate in years close to elections (2017 and 2020) compared to years of none election (2018 and 2019). There is an overall migration trend from conservatives to progressives during the four years. This change in scores across the four year time frame suggests a unique political cycle exclusive to Donald Trump’s unprecedented presidential term. Our results in a broad sense portray the potential capabilities of a data collection and scoring algorithm that detected a noticeable political migration and describes the broad social characteristics of certain politically aligned users on social media platforms.