Extracting interest tags from twitter user biographies

Abstract

Twitter, one of the most popular social media platforms, has been studied from different angles. One of the important sources of information in Twitter is users’ biographies, which are short self-introductions written by users in free form. Biographies often describe users’ background and interests. However, to the best of our knowledge, there has not been much work trying to extract information from Twitter biographies. In this work, we study how to extract information revealing users’ personal interests from Twitter biographies. A sequential labeling model is trained with automatically constructed labeled data. The popular patterns expressing user interests are extracted and analyzed. We also study the connection between interest tags extracted from user biographies and tweet content, and find that there is a weak linkage between them, suggesting that biographies can potentially serve as a complimentary source of information to tweets.