The where in the tweet

Abstract

Twitter is a widely-used social networking service which enables its users to post text-based messages, so-called tweets. POI tags on tweets can show more human-readable high-level information about a place rather than just a pair of coordinates. In this paper, we attempt to predict the POI tag of a tweet based on its textual content and time of posting. Potential applications include accurate positioning when GPS devices fail and disambiguating places located near each other. We consider this task as a ranking problem, i.e., we try to rank a set of candidate POIs according to a tweet by using language and time models. To tackle the sparsity of tweets tagged with POIs, we use web pages retrieved by search engines as an additional source of evidence. From our experiments, we find that users indeed leak some information about their accurate locations in their tweets.