Adaptive Representations for Tracking Breaking News on Twitter
release_55zfwx53kvhe3ou6xrdaiesvqi
by
Igor Brigadir, Derek Greene, Pádraig Cunningham
2014
Abstract
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.
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