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In part due to the proliferation of GPS-equipped mobile devices, massive volumes of geo-tagged streaming text messages are becoming available on social media. It is of great interest to discover most frequent nearby terms from such tremendous stream data. In this paper, we present novel indexing, updating, and query processing techniques that are capable of discovering topk most frequent nearby terms over a sliding window. Specifically, given a query location and a set of geo-tagged messagesdoi:10.1007/s11280-018-0606-x fatcat:bqthhmo3hfaf3onnfysko2e2ja