mlsanityData to 14 Oct 2026

News Source Credibility Assessment: A Reddit Case Study

arXiv 2402.10938 ↗First posted 7 Feb 2024cs.CL cs.SIcited by 1

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Abstract (arXiv)

In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation. We present the CREDiBERT (CREDibility assessment using Bi-directional Encoder Representations from Transformers), a source credibility assessment model fine-tuned for Reddit submissions focusing on political discourse as the main contribution. We adopt a semi-supervised training approach for CREDiBERT, leveraging Reddit's community-based structure. By encoding submission content using CREDiBERT and integrating it into a Siamese neural network, we significantly improve the binary classification of submission credibility, achieving a 9% increase in F1 score compared to existing methods. Additionally, we introduce a new version of the post-to-post network in Reddit that efficiently encodes user interactions to enhance the binary classification task by nearly 8% in F1 score. Finally, we employ CREDiBERT to evaluate the susceptibility of subreddits with respect to different topics.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 7 Feb 2024.

Builds on

9 of 23 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  6. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  7. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 336
  8. 2022FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Biascited by 10
  9. 2018Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sourcescited by 4

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