https://github.com/asubedi2001/fakenewsdetection
Misinformation classifier for social media content. We aim to use a Long Short Term Memory Model (LSTM) with attention to classify text as containing misinformation or not.
Science Score: 13.0%
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Low similarity (6.5%) to scientific vocabulary
Repository
Misinformation classifier for social media content. We aim to use a Long Short Term Memory Model (LSTM) with attention to classify text as containing misinformation or not.
Basic Info
- Host: GitHub
- Owner: asubedi2001
- Language: Jupyter Notebook
- Default Branch: main
- Size: 74.7 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
FakeNewsDetection
Binary and Multi-class misinformation classifiers based on LIAR dataset. We aim to LSTM, BiLSTM, GRU, and Transformer models to classify text which may contain misinformation or 'Fake News'
Dataset
LIAR: LIAR is a publicly available dataset for fake news detection. A decade-long of +12.K manually labeled short statements were collected in various contexts from POLITIFACT.COM, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. The LIAR dataset4 includes 12.8K human labeled short statements from POLITIFACT.COM’s API, and each statement is evaluated by a POLITIFACT.COM editor for its truthfulness. Source: “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection
Data pulled from https://paperswithcode.com/dataset/liar
Owner
- Name: Aakash
- Login: asubedi2001
- Kind: user
- Location: Maryland
- Repositories: 1
- Profile: https://github.com/asubedi2001
UMBC Computer Science '24
GitHub Events
Total
- Delete event: 4
- Member event: 2
- Push event: 31
- Public event: 1
- Pull request event: 8
- Create event: 4
Last Year
- Delete event: 4
- Member event: 2
- Push event: 31
- Public event: 1
- Pull request event: 8
- Create event: 4