Uncovering Geo-Social Semantics from the Twitter Mention Network: An Integrated Approach Using Spatial Network Smoothing and Topic Modeling
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Advances in human dynamics research and availability of geo-referenced communication data provide an unprecedented opportunity for studying the semantics of communication and understanding the interplay between online social networks and geography. Among the most extensively studied topics in geographically-embedded communication networks, are the effect of geographic proximity on interpersonal communication; the influence of information diffusion and social networks on real-world geographic events such as group activities and demonstrations; and revealing structural and geographic characteristics of a communication network. However, little is known on how the content of interpersonal communication vary across geographic space. By integrating methods of spatial network smoothing and probabilistic topic modeling, this paper introduces an approach to extracting and visualizing geo-social semantics, i.e., how the semantics of information vary based on the geographic locations and communication ties among the users. Different from the previous work that examine the geographic variation in the content produced by individuals, the proposed approach focuses on an analysis of reciprocal conversations among individuals in a geographically-embedded communication network. To demonstrate the approach, geo-located mention tweets in the U.S. from Aug. 1, 2015 to Aug. 1, 2016 were analyzed. Topics extracted from the analysis reflect geo-social dynamics of the society, way of speaking in the context of friendship, linguistic variation and the use of social media acronyms. Although the tweets were collected during primary and presidential elections, political topics discovered from the reciprocal mentions focused more on civil rights rather than the candidates and primaries. While the topic of primary candidates and elections was prominent at locations of primary elections and core supporters of candidates; civil rights was a prominent topic across the whole country.
KeywordsGeo-social semantics Topic modeling Geographically-embedded social networks Reciprocal mention tweets
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