Hashtag Sense Induction Based on Co-occurrence Graphs

Abstract

Twitter hashtags are used to categorize tweets for improving search categorizing topic. But the fact that people can create and use hashtags freely leads to a situation such that one hashtag may have multiple senses. In this paper, we propose a method to induce senses of a hashtag in a particular time frame. Our assumption is that for a sense of a hashtag the context words around it are similar. Then we design a method that uses a co-occurrence graph and community detection algorithm. Both words and hashtags are nodes of the co-occurrence graph, and an edge represents the relation of two nodes co-occurring in the same tweet. A list of words with a high node degree representing a sense is extracted as a community of the graph. We take Wikipedia disambiguation list page as word sense inventory to refine the results by removing non-sense topics.