2024/06/17 by Evan Williams, Evan M. Williams, Williams, Evan M. +4 · 1 voice · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Authorship Attribution and Profiling #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Social and Information Networks (cs.SI) #Spam and Phishing Detection #Text and Document Classification Technologies #cs.AI #cs.CL #cs.CY #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.2406.11423
openalex publication_date 2024/06/17 · arxiv published 2024/06/17 · openalex created_date 2024/06/19 · arxiv updated 2025/06/17 · openalex updated_date 2026/08/01
Proactive content moderation requires platforms to rapidly and continuously evaluate the credibility of websites. Leveraging the direct and indirect paths users follow to unreliable websites, we develop a website credibility classification and discovery system that integrates both webgraph and large-scale social media contexts. We additionally introduce the concept of dredge words, terms or phrases for which unreliable domains rank highly on search engines, and provide the first exploration of their usage on social media. Our graph neural networks that combine webgraph and social media contexts generate to state-of-the-art results in website credibility classification and significantly improves the top-k identification of unreliable domains. Additionally, we release a novel dataset of dredge words, highlighting their strong connections to both social media and online commerce platforms.