Know-your-Neighbors-Efficient-Author-Profiling-via-Follower-Tweets
User profiling based on social media data is becoming an increasingly relevant task with applications in advertising, forensics, literary studies and sociolinguistic research. Even though profiling of users based on their textual data is possible, social media such as Twitter offer also insight into the data of a given user’s followers. The purpose of this work was to explore how such follower data can be used for profiling a given user, what are its limitations and whether performances, similar to the ones observed when considering a given user’s data directly can be achieved. In this work we present our approach, capable of extracting various feature types and, via sparse matrix factorization, learn a dense, low-dimensional representations of individual persons solely from their followers’ tweet streams. The proposed approach scored second in the PAN 2020 Celebrity profiling shared task, and is computationally non-demanding.
