# The Veracity Problem: Detecting False Information and its Propagation on Online Social Media Networks

arXiv: https://arxiv.org/abs/2409.03948 · HTML: https://mlsanity.com/paper/2409.03948/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom](https://mlsanity.com/paper/2405.03371/) (2024)
- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [Towards Fine-Grained Reasoning for Fake News Detection](https://mlsanity.com/paper/2110.15064/) (2021)
- [Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository](https://mlsanity.com/paper/2002.00837/) (2020)
- [FakeFlow: Fake News Detection by Modeling the Flow of Affective Information](https://mlsanity.com/paper/2101.09810/) (2021)
- [Coordinated Behavior on Social Media in 2019 UK General Election](https://mlsanity.com/paper/2008.08370/) (2020)
- [VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media](https://mlsanity.com/paper/2110.15454/) (2021)
- [Heterogeneous Subgraph Transformer for Fake News Detection](https://mlsanity.com/paper/2404.13192/) (2024)

## Abstract (arXiv)

Detecting false information on social media is critical in mitigating its negative societal impacts. To reduce the propagation of false information, automated detection provide scalable, unbiased, and cost-effective methods. However, there are three potential research areas identified which once solved improve detection. First, current AI-based solutions often provide a uni-dimensional analysis on a complex, multi-dimensional issue, with solutions differing based on the features used. Furthermore, these methods do not account for the temporal and dynamic changes observed within the document's life cycle. Second, there has been little research on the detection of coordinated information campaigns and in understanding the intent of the actors and the campaign. Thirdly, there is a lack of consideration of cross-platform analysis, with existing datasets focusing on a single platform, such as X, and detection models designed for specific platform. This work aims to develop methods for effective detection of false information and its propagation. To this end, firstly we aim to propose the creation of an ensemble multi-faceted framework that leverages multiple aspects of false information. Secondly, we propose a method to identify actors and their intent when working in coordination to manipulate a narrative. Thirdly, we aim to analyse the impact of cross-platform interactions on the propagation of false information via the creation of a new dataset.