# VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text

arXiv: https://arxiv.org/abs/2102.00816 · HTML: https://mlsanity.com/paper/2102.00816/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Hierarchical Representations for Efficient Architecture Search](https://mlsanity.com/paper/1711.00436/) (2017)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours](https://mlsanity.com/paper/1704.05972/) (2017)
- [RumourEval 2019: Determining Rumour Veracity and Support for Rumours](https://mlsanity.com/paper/1809.06683/) (2018)
- [A Survey on Natural Language Processing for Fake News Detection](https://mlsanity.com/paper/1811.00770/) (2018)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Trends in the Diffusion of Misinformation on Social Media](https://mlsanity.com/paper/1809.05901/) (2018)
- [Automatic Rumor Detection on Microblogs: A Survey](https://mlsanity.com/paper/1807.03505/) (2018)
- [Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations](https://mlsanity.com/paper/1609.09028/) (2016)
- [Rumor Detection and Classification for Twitter Data](https://mlsanity.com/paper/1912.08926/) (2019)
- [Deep Two-path Semi-supervised Learning for Fake News Detection](https://mlsanity.com/paper/1906.05659/) (2019)

## Built on by (most cited first, 9 of 9)

- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence](https://mlsanity.com/paper/2207.13970/) (2022)
- [RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking](https://mlsanity.com/paper/2507.09174/) (2025)
- [Traceable and Authenticable Image Tagging for Fake News Detection](https://mlsanity.com/paper/2211.10923/) (2022)
- [Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor Detection](https://mlsanity.com/paper/2204.11515/) (2022)
- [An Emotion-Aware Multi-Task Approach to Fake News and Rumour Detection using Transfer Learning](https://mlsanity.com/paper/2211.12374/) (2022)
- [FaKnow: A Unified Library for Fake News Detection](https://mlsanity.com/paper/2401.16441/) (2024)
- [Adaptive Learning of Consistency and Inconsistency Information for Fake News Detection](https://mlsanity.com/paper/2408.08013/) (2024)

## Abstract (arXiv)

Social media became popular and percolated almost all aspects of our daily lives. While online posting proves very convenient for individual users, it also fosters fast-spreading of various rumors. The rapid and wide percolation of rumors can cause persistent adverse or detrimental impacts. Therefore, researchers invest great efforts on reducing the negative impacts of rumors. Towards this end, the rumor classification system aims to detect, track, and verify rumors in social media. Such systems typically include four components: (i) a rumor detector, (ii) a rumor tracker, (iii) a stance classifier, and (iv) a veracity classifier. In order to improve the state-of-the-art in rumor detection, tracking, and verification, we propose VRoC, a tweet-level variational autoencoder-based rumor classification system. VRoC consists of a co-train engine that trains variational autoencoders (VAEs) and rumor classification components. The co-train engine helps the VAEs to tune their latent representations to be classifier-friendly. We also show that VRoC is able to classify unseen rumors with high levels of accuracy. For the PHEME dataset, VRoC consistently outperforms several state-of-the-art techniques, on both observed and unobserved rumors, by up to 26.9%, in terms of macro-F1 scores.