Location Inference from Tweets using Grid-based Classification
This addresses location inference for social media analysis, but it is incremental as it builds on existing methods with a novel grid-based framework.
The paper tackles the problem of inferring user locations from tweets by proposing a grid-based classification approach using Multinomial Naive Bayes and geographic metadata, achieving over 57% accuracy at city-level granularity.
The impact of social media and its growing association with the sharing of ideas and propagation of messages remains vital in everyday communication. Twitter is one effective platform for the dissemination of news and stories about recent events happening around the world. It has a continually growing database currently adopted by over 300 million users. In this paper we propose a novel grid-based approach employing supervised Multinomial Naive Bayes while extracting geographic entities from relevant user descriptions metadata which gives a spatial indication of the user location. To the best of our knowledge our approach is the first to make location inference from tweets using geo-enriched grid-based classification. Our approach performs better than existing baselines achieving more than 57% accuracy at city-level granularity. In addition we present a novel framework for content-based estimation of user locations by specifying levels of granularity required in pre-defined location grids.