RThread: a thread-centric analysis of security forums

• Main Authors: Tachaiya, Jakapun, Gharibshah, Joobin, Papalexakis, Evangelos E., Faloutsos, Michalis
• Format: Conference Proceeding
• Language: English
• Subjects: Computing methodologies; Computing methodologies > Machine learning; Data visualization; Information systems; Information systems > Information retrieval; Information systems > Information retrieval > Retrieval tasks and goals; Information systems > Information retrieval > Retrieval tasks and goals > Clustering and classification; Information systems > Information systems applications; Information systems > Information systems applications > Data mining; Information systems > Information systems applications > Data mining > Clustering; Log-normal distribution; Online communities mining; Search engines; Security; Social networking (online); Systematics
• ISSN: 2473-991X
• DOI: 10.1109/ASONAM49781.2020.9381312

Online forums have been shown to contain a wealth of useful information. With a few notable exceptions, such forums have not received much attention from the research community, unlike other online social media. Our goal here is to conduct an in-depth thread-centric analysis of online forums, focusing on security forums. We propose, RThread, a comprehensive unsupervised clustering approach with a powerful visualization component, which we provide as a publicly-accessible web-based tool. Our approach leverages 92 thread features that span three groups: (a) temporal, (b) behavioral, and (c) content related. We analyze data from 8 security forums with 400k posts over a span of 8 years. First, we find that many thread-centric properties follow a log-normal distribution, which is persistent across several forums and over time. Second, we show how our approach can identify clusters of threads with similar behavior, while our visualization component provides an easy way to spot the differences between these clusters. Finally, we show how our approach can spot surprising behaviors, including a cluster, whose threads are used for Search Engine Optimization. We see our approach and our publicly available platform as a building block towards understanding forum activity and extracting interesting information in an unsupervised way.