2020/08/25 by Ishita Doshi, Sreekalyan Sajjalla, Doshi, Ishita +7
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Data Structures and Algorithms (cs.DS) #E.1 #FOS: Computer and information sciences #H.3.3 #I.5.3 #I.7.0 #Network Security and Intrusion Detection #Spam and Phishing Detection
paper · pdf · doi:10.48550/arxiv.2008.10828
openalex publication_date 2020/08/25 · openalex created_date 2020/09/01 · openalex updated_date 2026/07/28
We address the problem of large scale real-time classification of content posted on social networks, along with the need to rapidly identify novel spam types. Obtaining manual labels for user-generated content using editorial labeling and taxonomy development lags compared to the rate at which new content type needs to be classified. We propose a class of hierarchical clustering algorithms that can be used both for efficient and scalable real-time multiclass classification as well as in detecting new anomalies in user-generated content. Our methods have low query time, linear space usage, and come with theoretical guarantees with respect to a specific hierarchical clustering cost function (Dasgupta, 2016). We compare our solutions against a range of classification techniques and demonstrate excellent empirical performance.