Twitinfo

TLDR

Microblogs provide vast user-generated content about world events, yet their chronological logs hinder detailed event understanding. This paper introduces TwitInfo, a system that visualizes and summarizes Twitter events. TwitInfo displays tweets on a timeline, automatically detects and labels activity peaks with a streaming algorithm, and lets users drill down by subevent, geolocation, sentiment, and URLs while offering a recall‑normalized sentiment overview. Evaluation shows users quickly reconstruct meaningful event summaries, a Pulitzer‑winning journalist cites its utility for long‑running events, and the system identifies 80–100 % of manually labeled peaks.

Abstract

Microblogs are a tremendous repository of user-generated content about world events. However, for people trying to understand events by querying services like Twitter, a chronological log of posts makes it very difficult to get a detailed understanding of an event. In this paper, we present TwitInfo, a system for visualizing and summarizing events on Twitter. TwitInfo allows users to browse a large collection of tweets using a timeline-based display that highlights peaks of high tweet activity. A novel streaming algorithm automatically discovers these peaks and labels them meaningfully using text from the tweets. Users can drill down to subevents, and explore further via geolocation, sentiment, and popular URLs. We contribute a recall-normalized aggregate sentiment visualization to produce more honest sentiment overviews. An evaluation of the system revealed that users were able to reconstruct meaningful summaries of events in a small amount of time. An interview with a Pulitzer Prize-winning journalist suggested that the system would be especially useful for understanding a long-running event and for identifying eyewitnesses. Quantitatively, our system can identify 80-100% of manually labeled peaks, facilitating a relatively complete view of each event studied.