The utility of social and topical factors in anticipating repliers in Twitter conversations
Proceedings of the 5th Annual ACM Web Science Conference
How people describe themselves on Twitter
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
Finding news curators in twitter
Proceedings of the 22nd international conference on World Wide Web companion
Deep Twitter diving: exploring topical groups in microblogs at scale
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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One of the key challenges for users of social media is judging the topical expertise of other users in order to select trustful information sources about specific topics and to judge credibility of content produced by others. In this paper, we explore the usefulness of different types of user-related data for making sense about the topical expertise of Twitter users. Types of user-related data include messages a user authored or re-published, biographical information a user published on his/her profile page and information about user lists to which a user belongs. We conducted a user study that explores how useful different types of data are for informing human's expertise judgements. We then used topic modeling based on different types of data to build and assess computational expertise models of Twitter users. We use We follow directories as a proxy measurement for perceived expertise in this assessment. Our findings show that different types of user-related data indeed differ substantially in their ability to inform computational expertise models and humans's expertise judgements. Tweets and retweets--which are often used in literature for gauging the expertise area of users--are surprisingly useless for inferring the expertise topics of their authors and are outperformed by other types of user-related data such as information about users' list memberships. Our results have implications for algorithms, user interfaces and methods that focus on capturing expertise of social media users.