# Rumor Detection and Classification for Twitter Data

arXiv: https://arxiv.org/abs/1912.08926 · HTML: https://mlsanity.com/paper/1912.08926/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Simulating Rumor Spreading in Social Networks using LLM Agents](https://mlsanity.com/paper/2502.01450/) (2025)
- [VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text](https://mlsanity.com/paper/2102.00816/) (2021)
- [How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution](https://mlsanity.com/paper/2103.05944/) (2021)
- [Tweets2Stance: Users stance detection exploiting Zero-Shot Learning Algorithms on Tweets](https://mlsanity.com/paper/2204.10710/) (2022)

## Abstract (arXiv)

With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First we detect rumors as a type of misinformation propagation and next we go beyond detection to perform the task of rumor classification. WE explore the problem using a standard data set. We devise novel features and study their impact on the task. We experiment with various levels of preprocessing as a precursor of the classification as well as grouping of features. We achieve and f-measure of over 0.82 in RDC task in mixed rumors data set and 84 percent in a single rumor data set using a two-step classification approach.