Work

Bridging Social Media and Search Engines: Dredge Words and the Detection of Unreliable Domains

Year: 2025

Type: conference-paper

Abstract: 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.

Source: Proceedings of the International AAAI Conference on Web and Social Media

DOI: 10.1609/icwsm.v19i1.35917

Authors: Evan M. Williams, Peter Carragher, Kathleen M. Carley

Institution: Carnegie Mellon University

Language: English

Topic: Spam and Phishing Detection

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: John S. and James L. Knight Foundation, Carnegie Mellon University, Office of Naval Research

Award: G1755953439