# FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Features

arXiv: https://arxiv.org/abs/2109.04835 · HTML: https://mlsanity.com/paper/2109.04835/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [Predicting Factuality of Reporting and Bias of News Media Sources](https://mlsanity.com/paper/1810.01765/) (2018)
- [The Role of User Profile for Fake News Detection](https://mlsanity.com/paper/1904.13355/) (2019)
- [Fake News Early Detection: An Interdisciplinary Study](https://mlsanity.com/paper/1904.11679/) (2019)
- [A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/1812.00315/) (2018)
- [Learning Hierarchical Discourse-level Structure for Fake News Detection](https://mlsanity.com/paper/1903.07389/) (2019)
- [TI-CNN: Convolutional Neural Networks for Fake News Detection](https://mlsanity.com/paper/1806.00749/) (2018)
- [Credibility-based Fake News Detection](https://mlsanity.com/paper/1911.00643/) (2019)
- [Detecting Fake News with Weak Social Supervision](https://mlsanity.com/paper/1910.11430/) (2019)

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

- [A Multi-Level Strategy for Deepfake Content Moderation under EU Regulation](https://mlsanity.com/paper/2507.08879/) (2025)

## Abstract (arXiv)

In recent years, with the expansion of the Internet and attractive social media infrastructures, people prefer to follow the news through these media. Despite the many advantages of these media in the news field, the lack of any control and verification mechanism has led to the spread of fake news, as one of the most important threats to democracy, economy, journalism and freedom of expression. Designing and using automatic methods to detect fake news on social media has become a significant challenge. In this paper, we examine the publishers' role in detecting fake news on social media. We also suggest a high accurate multi-modal framework, namely FR-Detect, using user-related and content-related features with early detection capability. For this purpose, two new user-related features, namely Activity Credibility and Influence, have been introduced for publishers. Furthermore, a sentence-level convolutional neural network is provided to combine these features with latent textual content features properly. Experimental results have shown that the publishers' features can improve the performance of content-based models by up to 13% and 29% in accuracy and F1-score, respectively.