Source-linked AI summary
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
Kai Nakamura, Sharon Levy, William Yang Wang
TL;DR
Fake-news research lacks comprehensive datasets spanning multimodal content, metadata, comments, scale, and fine-grained categories. Fakeddit constructs and quality-assures over 1 million multimodal samples with 2-way, 3-way, and 6-way labels, then evaluates text, image, and hybrid models; the experiments report that multimodal features perform best and that the BERT–ResNet50 maximum-fusion model is most optimal.
Problem
Fake-news datasets lack comprehensive multimodal data, metadata, comments, large scale, and fine-grained categorization needed for detection research.
Method
Fakeddit collects over 1 million Reddit submissions, applies multiple quality-assurance steps, assigns 2-way, 3-way, and 6-way labels, and evaluates text-only, image-only, and multimodal models.
Results
Multimodal features performed best, with BERT text features and ResNet50 image features combined through maximum fusion performing most optimally.
Takeaways & Limitations
Fakeddit provides a large-scale multimodal benchmark for fine-grained fake-news detection and supports applications including implicit fact-checking.
Abstract
from arXiv · showhide
Fake news has altered society in negative ways in politics and culture. It has adversely affected both online social network systems as well as offline communities and conversations. Using automatic machine learning classification models is an efficient way to combat the widespread dissemination of fake news. However, a lack of effective, comprehensive datasets has been a problem for fake news research and detection model development. Prior fake news datasets do not provide multimodal text and image data, metadata, comment data, and fine-grained fake news categorization at the scale and breadth of our dataset. We present Fakeddit, a novel multimodal dataset consisting of over 1 million samples from multiple categories of fake news. After being processed through several stages of review, the samples are labeled according to 2-way, 3-way, and 6-way classification categories through distant supervision. We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.
1. Introduction
Fakeddit addresses limitations in fake-news datasets by providing large-scale multimodal data with quality assurance and labels for both broad and fine-grained classification.
- Dataset gap: Prior datasets often lacked multimodal data, sufficient size and variation, and fine-grained fake-news categories.Many conventional datasets also used only binary true-versus-false labels.
- Dataset contribution: The dataset undergoes multiple quality-assurance steps before use.These steps include filtering and manual review procedures.
- Evaluation: Experiments evaluate image-only, text-only, and multimodal models across several classification settings and baseline architectures.The evaluation is designed to assess the significance of combining image and text data.
2. Related Work
Existing fake-news datasets vary in size, modality, and labeling, while Fakeddit extends multimodal and fine-grained fake-news research with broader data coverage.
- Text datasets: Most existing datasets are small or contain only a limited number of classes, such as fake versus true.Only four listed datasets exceed half a million samples.
- Image datasets: Most existing fake-news datasets collect only text data, despite the relevance of images to fake-news detection.Existing fake-image datasets are also described as limited in size and diversity.
- Image datasets: Fakeddit expands multimodal dataset scope by combining text and image data with broader coverage than conventional image-verification datasets.Its data includes material from multiple sources and extends the size and scope of prior subreddit-based image data.
- Fact-checking: Fakeddit can support implicit fact-checking because its data combines text and images for verifying truth and proof.The paper positions this as an application of the dataset’s multimodality.
3. Fakeddit
Fakeddit is a large, diverse Reddit dataset combining multimodal content and hierarchical labels for coarse- and fine-grained fake-news detection. Compared with FEVER, it contains longer and more lexically diverse text.
- Data Collection: Fakeddit contains over 1 million Reddit submissions from 22 subreddits, spanning nearly a decade and more than 300,000 users.The dataset includes varied content from political news to everyday posts.
- Data Collection: Approximately 64% of Fakeddit samples contain both text and images, supporting multimodal experiments and error analysis.Comments associated with images also contribute submission data.
- Data Collection: The dataset applies multiple quality-control stages, including subreddit moderation, score filtering, and manual review.Posts with scores below 1 were filtered under the assumption that invalid or irrelevant posts would be down-voted or removed.
- Labeling: Fakeddit provides 2-way, 3-way, and 6-way labels, ranging from fake-versus-true classification to categories of fake news.The hierarchy supports both high-level and fine-grained detection.
- Dataset Analysis: Fakeddit has broader text-length and lexical diversity than FEVER, including samples approaching 100 words and more unique n-grams at equal sample sizes.FEVER captions peak near 10 words and end below 70 words, whereas Fakeddit contains more varied and longer text.
4. Experiments
The experiments compare text-only, image-only, and multimodal models across 2-, 3-, and 6-way classification. Multimodal models perform best, with BERT and ResNet50 combined using maximum feature merging achieving the strongest overall configuration.
- Fake News Detection: Text features are extracted with InferSent and BERT, while image features use VGG16, EfficientNet, and ResNet50.InferSent produces 4096-dimensional title features, and BERT embeddings contain 768 elements.
- Experiment Settings: The study evaluates image-only, text-only, and multimodal inputs across 2-way, 3-way, and 6-way fake-news classification.Submissions missing either text or image data were excluded.
- Results: Multimodal features perform best, followed by text-only and image-only features, indicating the value of combining text and image inputs.The maximum method yields the highest accuracy among feature-merging methods.
- Results: The strongest overall model combines BERT text features and ResNet50 image features using maximum merging.For 6-way classification, the best configuration uses 224 hidden units, a 1e-4 learning rate, and 20 training epochs.
5. Error Analysis
The 6-way model struggled most with imposter content and satire, while performing well on manipulated content. Class imbalance may also bias predictions toward the true label.
- Error patterns: Imposter content was the hardest category because machine-generated text can resemble human-written news.The model also had difficulty with satire, whose realistic presentation may require contextual knowledge to identify.
- Error patterns: Satire was the second-poorest category, reflecting the challenge of distinguishing realistic-seeming satirical content.The authors identify insufficient contextual knowledge as a possible difficulty for this category.
- Implications: The baseline results leave significant room for improvement in classifying imposter content and satire.These two categories are described as complex challenges in the 6-way task.
- Class distribution: Class-imbalance differences between 2-way and 6-way classification may incline the model toward predicting the true label.The 6-way setup contains more true samples relative to the fake classes.
6. Conclusion
The conclusion presents Fakeddit as a large multimodal dataset with multiple labels for fine-grained fake news classification. It reports broad research utility while identifying unused metadata and comments as opportunities for future work.
- Conclusion: Fakeddit provides large-scale multimodal samples with multiple labels for different levels of fine-grained fake news classification.The dataset is positioned as distinct from previous datasets through its combination of scale, multimodality, and label granularity.
- Conclusion: Experiments and error analysis highlight multimodality’s importance while showing substantial room for improvement in fine-grained fake news detection.The conclusion summarizes multiple baseline experiments and the remaining challenges in fine-grained detection.
- Future applications: Unused submission metadata and user comments may support future research, including tracking user credibility.The paper also suggests incorporating video data and using image-caption verification for implicit fact-checking.
Appendix
The appendix lists the subreddits used to construct Fakeddit.
- Appendix: Table 7 contains the list of subreddits used in the dataset.