文件列表:

dataset (0, 2022-10-31)
dataset\gossipcop_fake.csv (12538704, 2022-10-31)
dataset\gossipcop_real.csv (19978126, 2022-10-31)
dataset\politifact_fake.csv (3286418, 2022-10-31)
dataset\politifact_real.csv (8278658, 2022-10-31)
fake_news_classification.joblib (4563545, 2022-10-31)
get_results.py (915, 2022-10-31)
main.ipynb (24743, 2022-10-31)
requirements.txt (1552, 2022-10-31)
templates (0, 2022-10-31)
templates\home.html (2001, 2022-10-31)

# Fake News Detection This project aims to detect Fake News based on a user query and display the results on a webpage using Flask.
## Dataset overview FakeNewsNet contains 2 datasets collected using ground truths from Politifact and Gossipcop. The minimalistic version of this dataset provided by FakeNewsNet includes the following files: - `politifact_fake.csv` - Samples related to fake news collected from PolitiFact - `politifact_real.csv` - Samples related to real news collected from PolitiFact - `gossipcop_fake.csv` - Samples related to fake news collected from GossipCop - `gossipcop_real.csv` - Samples related to real news collected from GossipCop Each of the above CSV files is comma separated file and has the following columns: - `id` - Unique identifider for each news - `url` - Url of the article from web that published that news - `title` - Title of the news article - `tweet_ids` - Tweet ids of tweets sharing the news. This field is list of tweet ids separated by tab ## Installation ### Requirements: Data download scripts are writtern in python and requires `python 3.8+` to run. Install all the libraries in `requirements.txt` using the following command: ```` pip install -r requirements.txt ```` ## Running Code ### Virtual Environment setup: Create the virtual environment: ```` > py -3 -m venv venv ```` Activate the corresponding environment: ```` > venv\Scripts\activate ```` ### Flask setup: Configure flask environment: ```` > $env:FLASK_ENV = "development" > $env:FLASK_APP = "get_results.py" ```` After the setup, run the following command to launch the flask app on your localhost: ```` > flask run ```` ## References If you use this dataset, please cite the following papers: ~~~~ @article{shu2018fakenewsnet, title={FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media}, author={Shu, Kai and Mahudeswaran, Deepak and Wang, Suhang and Lee, Dongwon and Liu, Huan}, journal={arXiv preprint arXiv:1809.01286}, year={2018} } ~~~~ ~~~~ @article{shu2017fake, title={Fake News Detection on Social Media: A Data Mining Perspective}, author={Shu, Kai and Sliva, Amy and Wang, Suhang and Tang, Jiliang and Liu, Huan}, journal={ACM SIGKDD Explorations Newsletter}, volume={19}, number={1}, pages={22--36}, year={2017}, publisher={ACM} } ~~~~ ~~~~ @article{shu2017exploiting, title={Exploiting Tri-Relationship for Fake News Detection}, author={Shu, Kai and Wang, Suhang and Liu, Huan}, journal={arXiv preprint arXiv:1712.07709}, year={2017} } ~~~~ [Fake News Detection on Social Media: A Data Mining Perspective](https://arxiv.org/abs/1708.01967)
[Exploiting Tri-Relationship for Fake News Detection](http://arxiv.org/abs/1712.07709)
[FakeNewsTracker](http://blogtrackers.fulton.asu.edu:3000)
[FakeNewsNet](https://arxiv.org/abs/1809.01286) (C) 2019 Arizona Board of Regents on Behalf of ASU

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