Improving Geocoding of a Twitter User Group using their Account Creation Times and Languages

This paper proposes a classifier for predicting the location of followers of a Twitter influencer using their account creation times. Time is a universal feature and thus it can be used to characterize influencers from different parts of the world. In addition, the language of the location is used to improve classification performance. In the first step of the proposed two-step approach influencer's followers' account creation times are used to create a time distribution to predict the time zone's Coordinated Universal Time (UTC) offset. In the second step, language features are used to constrain locations associated with the UTC offset. The approach is illustrated by categorizing 320K Twitter celebrity influencers by their country. High confidence influencer predictions are used as training data for an improved geocoder. This geocoder automatically learns popular ways that Twitter users refer to locations within the country and can handle foreign alphabets.