Unsupervised Content-Based Characterization and Anomaly Detection of Online Community Dynamics

dc.contributor.author Shah, Danelle
dc.contributor.author Hurley, Michael
dc.contributor.author Liu, Jessamyn
dc.contributor.author Daggett, Matthew
dc.date.accessioned 2019-01-03T00:02:20Z
dc.date.available 2019-01-03T00:02:20Z
dc.date.issued 2019-01-08
dc.description.abstract The structure and behavior of human networks have been investigated and quantitatively modeled by modern social scientists for decades, however the scope of these efforts is often constrained by the labor-intensive curation processes that are required to collect, organize, and analyze network data. The surge in online social media in recent years provides a new source of dynamic, semi-structured data of digital human networks, many of which embody attributes of real-world networks. In this paper we leverage the Reddit social media platform to study social communities whose dynamics indicate they may have experienced a disturbance event. We describe an unsupervised approach to analyzing natural language content for quantifying community similarity, monitoring temporal changes, and detecting anomalies indicative of disturbance events. We demonstrate how this method is able to detect anomalies in a spectrum of Reddit communities and discuss its applicability to unsupervised event detection for a broader class of social media use cases.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2019.274
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59665
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining and Machine Learning for Social Media
dc.subject Digital and Social Media
dc.subject social networks, anomaly detection, event detection, network characterization
dc.title Unsupervised Content-Based Characterization and Anomaly Detection of Online Community Dynamics
dc.type Conference Paper
dc.type.dcmi Text
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