HisRect: Features from Historical Visits and Recent Tweet for Co-Location Judgement

Social media users are increasingly going mobile, fostering location based services on social media platforms (e.g., Twitter). Services like friends notification and community detection benefit from co-location judgement that decides if two users are co-located in some point-of-interest (POI). This problem is challenging due to the limited information and the lack of explicit geo-tags in tweets that can be used as labeled data. Our approach is based on a novel concept HisRect features extracted from users' historical visits and recent tweets: The former has impacts on where a user visits in general, whereas the latter gives more hints about where a user is currently. As labeled data is scarce in practice, we design a semi-supervised learning (SSL) framework that leverages unlabeled data to extract HisRect features. Moreover, we employ an embedding neural network layer to process HisRect features of two users, which decides co-location based on the embedding difference between the two features. Our model is evaluated on large sets of real Twitter data from over one million users. The experimental results demonstrate that our HisRect features and SSL framework are highly effective at deciding co-locations. In terms of multiple metrics, our approach clearly outperforms alternative approaches using state-of-the-art techniques.