# Leveraging Social Interactions to Detect Misinformation on Social Media

arXiv: https://arxiv.org/abs/2304.02983 · HTML: https://mlsanity.com/paper/2304.02983/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 6 Apr 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation](https://mlsanity.com/paper/1903.09196/) (2019)
- [Fake News Early Detection: An Interdisciplinary Study](https://mlsanity.com/paper/1904.11679/) (2019)
- [A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone](https://mlsanity.com/paper/2002.00850/) (2020)
- [The COVID-19 Infodemic: Twitter versus Facebook](https://mlsanity.com/paper/2012.09353/) (2020)
- [Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement](https://mlsanity.com/paper/2202.12413/) (2022)
- [ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds](https://mlsanity.com/paper/2210.14763/) (2022)

## Built on by (most cited first, 1 of 1)

- [FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs](https://mlsanity.com/paper/2402.05904/) (2024)

## Abstract (arXiv)

Detecting misinformation threads is crucial to guarantee a healthy environment on social media. We address the problem using the data set created during the COVID-19 pandemic. It contains cascades of tweets discussing information weakly labeled as reliable or unreliable, based on a previous evaluation of the information source. The models identifying unreliable threads usually rely on textual features. But reliability is not just what is said, but by whom and to whom. We additionally leverage on network information. Following the homophily principle, we hypothesize that users who interact are generally interested in similar topics and spreading similar kind of news, which in turn is generally reliable or not. We test several methods to learn representations of the social interactions within the cascades, combining them with deep neural language models in a Multi-Input (MI) framework. Keeping track of the sequence of the interactions during the time, we improve over previous state-of-the-art models.