The growing availability of multi-faceted social media data gives rise to unprecedented opportunities for unveiling complex real-world online behaviors. This also supports the proliferation of complex network models where the expressive power of the graph-based relational structure is enhanced through exposing several types of features that are peculiar of the social media platforms. This workshop aims to explore innovative methods that are designed to improve our understanding of behaviors and relations underlying feature-rich networks built upon social media, here called social-media-driven complex networks. Exemplary network models of such kind include heterogeneous, multilayer/multiplex/multirelational networks, temporal, location-aware, and probabilistic networks, and any other type of data-driven network that can be inferred from social media data contexts.
The aim of the Soc2Net workshop, that will be held the 7 December 2020 as a virtual event in conjunction with The 2020 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM 2020), is to bring together researchers and practitioners from around the world interested in 1) exploring different perspectives and approaches to mine social-media-driven complex networks, 2) analyzing user behavior and evolution in social-media-driven complex networks, and 3) building models and frameworks for evaluating the respective approaches.
Authors are encouraged to evaluate their models, methods, metrics and algorithms on real-world social networks built upon publicly available datasets. We solicit interdisciplinary submissions focusing on topics of interest to different research communities, including social science, economics and digital humanities.