Twitter is a widely-used social networking service which enables its users to post short text-based messages, so-called tweets. POI (Point of Interest) tags on tweets can show more human-readable high-level information about a place that is more meaningful and better interpretable than a pair of coordinates. We studied the prediction of POI tags based on a tweet’s textual content and time of posting. Potential applications include accurate positioning when GPS devices fail or disambiguating places located near each other. We consider this task as a ranking problem, i.e., we rank a set of candidate POIs according to a tweet by using statistical models of language use and temporal distribution of tweets. To tackle the sparsity of tweets tagged with POIs, we use web pages retrieved by search engines as an additional source of evidence. Our experiments show that tweets indeed have relationships with their places of origin in both textual and temporal dimensions.
This initial exploratory study will be presented as a poster at the 20th ACM International Conference on Information and Knowledge Management (CIKM) in Glasgow, UK.