Questions about questions: an empirical analysis of information needs on Twitter

Conventional studies of online information seeking behavior usually focus on the use of search engines or question answering (Q&A) websites. Recently, the fast growth of online social platforms such as Twitter and Facebook has made it possible for people to utilize them for information seeking by asking questions to their friends or followers. We anticipate a better understanding of Web users' information needs by investigating research questions about these questions. How are they distinctive from daily tweeted conversations? How are they related to search queries? Can users' information needs on one platform predict those on the other? In this study, we take the initiative to extract and analyze information needs from billions of online conversations collected from Twitter. With an automatic text classifier, we can accurately detect real questions in tweets (i.e., tweets conveying real information needs). We then present a comprehensive analysis of the large-scale collection of information needs we extracted. We found that questions being asked on Twitter are substantially different from the topics being tweeted in general. Information needs detected on Twitter have a considerable power of predicting the trends of Google queries. Many interesting signals emerge through longitudinal analysis of the volume, spikes, and entropy of questions on Twitter, which provide insights to the understanding of the impact of real world events and user behavioral patterns in social platforms.