Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/59665

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

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Title:Unsupervised Content-Based Characterization and Anomaly Detection of Online Community Dynamics
Authors:Shah, Danelle
Hurley, Michael
Liu, Jessamyn
Daggett, Matthew
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
Digital and Social Media
social networks, anomaly detection, event detection, network characterization
Date Issued:08 Jan 2019
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/59665
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.274
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


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