# Using Deep Networks and Transfer Learning to Address Disinformation

arXiv: https://arxiv.org/abs/1905.10412 · HTML: https://mlsanity.com/paper/1905.10412/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 2 of 2)

- [BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets](https://mlsanity.com/paper/2101.09345/) (2021)
- [Bengali Fake Reviews: A Benchmark Dataset and Detection System](https://mlsanity.com/paper/2308.01987/) (2023)

## Abstract (arXiv)

We apply an ensemble pipeline composed of a character-level convolutional neural network (CNN) and a long short-term memory (LSTM) as a general tool for addressing a range of disinformation problems. We also demonstrate the ability to use this architecture to transfer knowledge from labeled data in one domain to related (supervised and unsupervised) tasks. Character-level neural networks and transfer learning are particularly valuable tools in the disinformation space because of the messy nature of social media, lack of labeled data, and the multi-channel tactics of influence campaigns. We demonstrate their effectiveness in several tasks relevant for detecting disinformation: spam emails, review bombing, political sentiment, and conversation clustering.