Topic-specific transnational political ideology detection on Twitter

Abstract

The abundance of accessible real-time user information generated from Twitter platform contributes to the utilization in multifarious research domains. Researches detecting Ideologies for Twitter users via Link analysis such as retweet network and follower network shows great results in recent years. However, there are difficulties when taking into account the link relations among countries with different language systems, with the fact that people hardly retweet or follow other people when there is a language barrier. In our research, we fine-tune a multilingual LaBSE model to create U.S. political dimension based on political activists’ user vector embeddings, and project political user vectors from other countries to clarify transnational ideologies. We also build classifier to categorize tweets into various topics to check if the change of topic alters the political ideologies of the same user. We can conclude that user political ideologies show different polarization degrees when it comes to different topics.