# Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale

arXiv: https://arxiv.org/abs/2308.02068 · HTML: https://mlsanity.com/paper/2308.02068/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 125 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 30 of 36)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus](https://mlsanity.com/paper/2112.09924/) (2021)
- [Fact-Checking Meets Fauxtography: Verifying Claims About Images](https://mlsanity.com/paper/1908.11722/) (2019)
- [Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations](https://mlsanity.com/paper/2202.04582/) (2022)
- [Trends in the Diffusion of Misinformation on Social Media](https://mlsanity.com/paper/1809.05901/) (2018)
- [Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ](https://mlsanity.com/paper/1703.00565/) (2017)
- [Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web](https://mlsanity.com/paper/1801.09288/) (2018)
- [Multilingual Clustering of Streaming News](https://mlsanity.com/paper/1809.00540/) (2018)
- [Causal bootstrapping](https://mlsanity.com/paper/1910.09648/) (2019)
- [Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit](https://mlsanity.com/paper/2205.14484/) (2022)
- [TROLLMAGNIFIER: Detecting State-Sponsored Troll Accounts on Reddit](https://mlsanity.com/paper/2112.00443/) (2021)
- [LAMBRETTA: Learning to Rank for Twitter Soft Moderation](https://mlsanity.com/paper/2212.05926/) (2022)
- [Adapting Security Warnings to Counter Online Disinformation](https://mlsanity.com/paper/2008.10772/) (2020)
- [A Golden Age: Conspiracy Theories' Relationship with Misinformation Outlets, News Media, and the Wider Internet](https://mlsanity.com/paper/2301.10880/) (2023)
- [Identifying Misinformation from Website Screenshots](https://mlsanity.com/paper/2102.07849/) (2021)
- [No Calm in The Storm: Investigating QAnon Website Relationships](https://mlsanity.com/paper/2106.15715/) (2021)
- [Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegram](https://mlsanity.com/paper/2301.10856/) (2023)

## Built on by (most cited first, 7 of 7)

- [Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings](https://mlsanity.com/paper/2506.00277/) (2025)
- [Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites](https://mlsanity.com/paper/2501.09102/) (2025)
- [Identifying Emerging Concepts in Large Corpora](https://mlsanity.com/paper/2502.21315/) (2025)
- [Modeling Information Narrative Detection and Evolution on Telegram during the Russia-Ukraine War](https://mlsanity.com/paper/2409.07684/) (2024)
- [Cross-National Information Attacks: A Two-Decade Analysis of Troll Behavior in Korea](https://mlsanity.com/paper/2606.22785/) (2026)
- [From Repetition to Recognition: Inductive Discovery of Disinformation Narratives](https://mlsanity.com/paper/2609.11128/) (2026)
- [Unifying Models of Intergroup Hostility in Online Discourse](https://mlsanity.com/paper/2609.20808/) (2026)

## Abstract (arXiv)

Misinformation, propaganda, and outright lies proliferate on the web, with some narratives having dangerous real-world consequences on public health, elections, and individual safety. However, despite the impact of misinformation, the research community largely lacks automated and programmatic approaches for tracking news narratives across online platforms. In this work, utilizing daily scrapes of 1,334 unreliable news websites, the large-language model MPNet, and DP-Means clustering, we introduce a system to automatically identify and track the narratives spread within online ecosystems. Identifying 52,036 narratives on these 1,334 websites, we describe the most prevalent narratives spread in 2022 and identify the most influential websites that originate and amplify narratives. Finally, we show how our system can be utilized to detect new narratives originating from unreliable news websites and to aid fact-checkers in more quickly addressing misinformation. We release code and data at https://github.com/hanshanley/specious-sites.