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In recent years, microblogs such as Twitter have emerged as a new communication channel. Twitter in particular has become the target of a myriad of content-based applications including trend analysis and event detection, but there has been little fundamental work on the analysis of word usage patterns in this text type. In this paper — inspired by the one-sense-perdiscourse heuristic of Gale et al. (1992) — we investigate user-level sense distributions, and detect strong support for “one sense per tweeter”. As part of this, we construct a novel sense-tagged lexical sample dataset based on Twitter and a web corpus.

One Sense per Tweeter ... and Other Lexical Semantic Tales of Twitter