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

arXiv: https://arxiv.org/abs/2406.11423 · HTML: https://mlsanity.com/paper/2406.11423/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data](https://mlsanity.com/paper/2102.06314/) (2021)
- [Visualizing and Understanding Curriculum Learning for Long Short-Term Memory Networks](https://mlsanity.com/paper/1611.06204/) (2016)
- [A Topic-Agnostic Approach for Identifying Fake News Pages](https://mlsanity.com/paper/1905.00957/) (2019)
- [Social Media-based User Embedding: A Literature Review](https://mlsanity.com/paper/1907.00725/) (2019)
- [Detection and Discovery of Misinformation Sources using Attributed Webgraphs](https://mlsanity.com/paper/2401.02379/) (2024)
- [Mutual Hyperlinking Among Misinformation Peddlers](https://mlsanity.com/paper/2104.11694/) (2021)
- [Conspiracy Brokers: Understanding the Monetization of YouTube Conspiracy Theories](https://mlsanity.com/paper/2205.15943/) (2022)
- [Evaluating Trustworthiness of Online News Publishers via Article Classification](https://mlsanity.com/paper/2401.01781/) (2024)
- [Algorithmically Curated Lies: How Search Engines Handle Misinformation about US Biolabs in Ukraine](https://mlsanity.com/paper/2401.13832/) (2024)
- [Misinformation Resilient Search Rankings with Webgraph-based Interventions](https://mlsanity.com/paper/2404.08869/) (2024)

## Built on by (most cited first, 2 of 2)

- [CrediBench: Building Web-Scale Network Datasets for Information Integrity](https://mlsanity.com/paper/2509.23340/) (2025)
- [From Articles to Publishers: Aggregating Language Model Predictions for News Source Reliability Inference](https://mlsanity.com/paper/2609.24219/) (2026)

## Abstract (arXiv)

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.