LAUSR

ABSTRACT Interpersonal communication on online social networks has a significant impact on the society by not only diffusing information, but also forming social ties, norms, and behaviors. Knowing how the conversational discourse semantically and geographically vary over time can help uncover the changing dynamics of interpersonal ties and the digital traces of social events. This article introduces a framework for modeling and visualizing the semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. The framework consists of (1) a topic modeling workflow for modeling topics and extracting the evolution of conversational discourse; (2) a geo-social network modeling and smoothing approach to projecting connection characteristics and semantics of communication onto geographic space and time; (3) a web-based geovisual analytics environment for exploring semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. To demonstrate, geo-located and reciprocal user mention and reply tweets over the course of the 2016 primary and presidential elections in the United States from 1 August 2015 to 15 November 2016 were analyzed. The large portion of the topics extracted from mention tweets were related to daily life routines, human activities, and interests such as school, work, sports, dating, wearing, birthday celebration, music, food, and live-tweeting. Specific focus on the analysis of political conversations revealed that the content of conversational discourse was split between civil rights and election-related discussions of the political campaigns and candidates. These political topics exhibited major shifts in terms of content and the popularity in reaction to primaries, debates, and events throughout the study period. While civil rights discussions were more dominant and in higher intensity across the nation and throughout the whole time period, election-specific conversations resulted in temporally varying local hotspots that correlated with locations of primaries and events.