Mining Hot Topics from Twitter Streams

Mining hot topics from twitter streams has attracted a lot of attention in recent years. Traditional hot topic mining from Internet Web pages were mainly based on text clustering. However, compared to the texts in Web pages, twitter texts are relatively short with sparse attributes. Moreover, twitter data often increase rapidly with fast spreading speed, which poses great challenge to existing topic mining models. To this end, we propose, in this paper, a flexible stream mining approach for hot twitter topic detection. Specifically, we propose to use the Frequent Pattern stream mining algorithm (i.e. FP-stream) to detect hot topics from twitter streams. Empirical studies on real world twitter data demonstrate the utility of the proposed method.