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POI-level geo-information of social posts is critical to many location-basedapplications and services. However, the multi-modality, complexity and diversenature of social media data and their platforms limit the performance ofinferring such fine-grained locations and their subsequent applications. Toaddress this issue, we present a transformer-based general framework, whichbuilds upon pre-trained language models and considers non-textual data, forsocial post geolocation at the POI level. To this end, inputs are categorizedto handle different social data, and an optimal combination strategy isprovided for feature representations. Moreover, a uniform representation ofhierarchy is proposed to learn temporal information, and a concatenated versionof encodings is employed to capture feature-wise positions better. Experimentalresults on various social datasets demonstrate that three variants of ourproposed framework outperform multiple state-of-art baselines by a large marginin terms of accuracy and distance error metrics.

A Transformer-based Framework for POI-level Social Post Geolocation