2022/06/14 by Joanna Baran, Michał Kajstura, Baran, Joanna +5 · 1 citation
Computer Science · Social Sciences · #Hate Speech and Cyberbullying Detection #Misinformation and Its Impacts #Social Media and Politics #cs.CL #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.2207.07586
arxiv created 2022/06/14 · arxiv updated 2022/07/18
Every day, the world is flooded by millions of messages and statements posted on Twitter or Facebook. Social media platforms try to protect users' personal data, but there still is a real risk of misuse, including elections manipulation. Did you know, that only 13 posts addressing important or controversial topics for society are enough to predict one's political affiliation with a 0.85 F1-score? To examine this phenomenon, we created a novel universal method of semi-automated political leaning discovery. It relies on a heuristical data annotation procedure, which was evaluated to achieve 0.95 agreement with human annotators (counted as an accuracy metric). We also present POLiTweets - the first publicly open Polish dataset for political affiliation discovery in a multi-party setup, consisting of over 147k tweets from almost 10k Polish-writing users annotated heuristically and almost 40k tweets from 166 users annotated manually as a test set. We used our data to study the aspects of domain shift in the context of topics and the type of content writers - ordinary citizens vs. professional politicians.