Summary of A Semi-supervised Fake News Detection Using Sentiment Encoding and Lstm with Self-attention, by Pouya Shaeri and Ali Katanforoush
A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention
by Pouya Shaeri, Ali Katanforoush
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a semi-supervised self-learning method for detecting fake news on social media. The approach utilizes state-of-the-art pre-trained models for sentiment analysis and incorporates Long Short-Term Memory (LSTM) with self-attention layers. The model is trained in a semi-supervised fashion, leveraging unlabeled data from social media platforms to improve performance. Experimental results show that the proposed method outperforms competitive methods in precision, recall, and other measures on a dataset of 20,000 news content items. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us detect fake news on social media better by using some really smart computer models. The main idea is to teach these models to understand how people feel about different pieces of news without needing a huge amount of labeled data. We tested the model and found that it did really well in recognizing true or false news, even when compared to other methods. |
Keywords
» Artificial intelligence » Lstm » Precision » Recall » Self attention » Semi supervised
