SigniTrend: Scalable Detection of Emerging Topics in Textual Streams by Hashed Significance Thresholds

摘要

Social media such as Twitter or weblogs are a popular source for live textual data. Much of this popularity is due to the fast rate at which this data arrives, and there are a number of global events - such as the Arab Spring - where Twitter is reported to have had a major influence. However, existing methods for emerging topic detection are often only able to detect events of a global magnitude such as natural disasters or celebrity deaths, and can monitor user-selected keywords or operate on a curated set of hashtags only. Interesting emerging topics may, however, be of much smaller magnitude and may involve the combination of two or more words that themselves are not unusually hot at that time. Our contributions to the detection of emerging trends are threefold: first of all, we propose a significance measure that can be used to detect emerging topics early, long before they become "hot tags", by drawing upon experience from outlier detection. Secondly, by using hash tables in a heavy-hitters type algorithm for establishing a noise baseline, we show how to track even all keyword pairs using only a fixed amount of memory. Finally, we aggregate the detected co-trends into larger topics using clustering approaches, as often as a single event will cause multiple word combinations to trend at the same time.