Adaptive Representations for Tracking Breaking News on Twitter
This addresses the challenge of real-time news filtering on social media for users and analysts, but it appears incremental as it builds on existing adaptive approaches.
The paper tackled the problem of tracking and summarizing breaking news stories on Twitter, where standard retrieval methods fail due to short tweets and evolving content, and found that adaptive mechanisms are best suited for this task, as indicated by ROUGE metrics.
Twitter is often the most up-to-date source for finding and tracking breaking news stories. Therefore, there is considerable interest in developing filters for tweet streams in order to track and summarize stories. This is a non-trivial text analytics task as tweets are short, and standard retrieval methods often fail as stories evolve over time. In this paper we examine the effectiveness of adaptive mechanisms for tracking and summarizing breaking news stories. We evaluate the effectiveness of these mechanisms on a number of recent news events for which manually curated timelines are available. Assessments based on ROUGE metrics indicate that an adaptive approaches are best suited for tracking evolving stories on Twitter.