Selecting Quality Twitter Content for Events

Abstract

Social media sites such as Twitter contain large amounts of user contributed messages for a wide variety of real-world events. While some of these "event messages" might contain interesting and useful information (e.g., event time, location, participants, opinions), others might provide little value (e.g., using heavy slang, incomprehensible language) to people interested in learning about an event. Techniques for effective selection of quality event content may therefore help improve applications such as event browsing and search.In this paper, we explore approaches for finding representative messages among a set of Twitter messages that correspond to the same event, with the goal of identifying high quality, relevant messages that provide useful event information. We evaluate our approaches using a large-scale dataset of Twitter messages, and show that we can automatically select event messages that are both relevant and useful.