Machine learning study demonstrates automated topic summarization in live social media streams, suggesting practical frameworks for real-time information filtering.
This paper aims to generate digests of tweets from live trending and ongoing topics. The primary purpose is to group the tweets by importance or usefulness so that an end user can be presented with a reasonable extract of the most important content from the Twitter stream. Summarization is accomplished using a non-parametric Bayesian model applied to Hidden Markov Models and a novel observation model designed to allow ranking based on selected predictive characteristics of individual tweets.
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Wen et al. (2014) studied this question.
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