Demographic and Psychographic Estimation of Twitter Users Using Social Structures

Abstract

Word-of-mouth marketing on social media has become more urgent with the increasing number of users and posts, and it is important to estimate user attributes because most users on Twitter do not reveal their attributes. We propose new methods for estimating user attributes of a Twitter user from the user’s contents (a profile document and tweets) and social neighbors, i.e. those with whom the user has mentioned. This study has three contributions on the task of user attribute estimation. First, we investigate a labeling method that finds the users associated with a blog account and uses their profile attributes on blog as true labels of training tweet data. We confirm that using the blog labels achieved higher accuracy than manual labeling and pattern matching methods, with respect to four attributes (gender, age, occupation, and interests). Second, we validate the best way to combine bag-of-words features of profile documents and tweets. We evaluate nine combining methods and show that words in profile documents should be treated distinctively from those in tweets. Third, we reveal that to adjust amount of information from social neighbors affects estimation accuracy. We experiment three adjustment levels and show that our method, which utilizes the target user’s profile document and tweets and the neighbors’ profile documents (not including tweets), achieved the best accuracy. Overall experiments conducted on the estimation of the four attributes show that our method achieved higher accuracy than conventional methods that use manually-labeled tweets.