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.

研究发现，使用社交媒体平台的用户只需发表 13 篇与社会相关的帖子即可预测其政治取向，作者通过创新的半自动化方法来发现用户的政治倾向，并公开了波兰的 POLiTweets 数据集，以研究多党制的政治倾向和领域转移等问题。