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

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. 1 This distinction is subtle. In the context of this work we assume a 1:1 mapping between the (observable) network and the corresponding (sub)community it represents, and use these terms interchangeably.

A Survey on Event Tracking in Social Media Data Streams

“…This approach enables more accurate identification of hot events on social media and quick access to key features and trends of the events. Wang et al [56] proposed a unique multilayer residual and gating-based convolutional neural network architecture to capture more scale contextual information by increasing the perceptual field. This approach can effectively capture multi-level features in social media data and provide more refined and accurate analysis results for ED.…”

“…• Better captureing dependency contextual information for ED Wang et al [56] News article RG-ACNN framework Multimodality data…”

The comparative analysis of regulations in the Italian Republic and the Russian Federation against cryptolaundering techniques

“…Several studies have already used ClearNet forums and topic models to determine the effect of DNM busts (Porter, 2018), to discover anomalies signaling the advent of disturbing events (Shah et al, 2019), to determine critical players on specific DNM (Hazel Kwon and Shao, 2020). The usual topic model's approach examines documents over time with different topics, where a topic is a probability distribution over the words (Sohrabi et al, 2018).…”