Fake-news-classification-model  

   

Table of Contents

• Table of Contents
  • Introduction
  • News Cateogries
  • Architecture
  • System interface design
  • Poster
  • Demo-video
  • Tech stack
  • Project Deliverables
  • Installation
  • Prerequisites
    • How to run the app?
    • How to stop the Deep learning model servers?
  • Google colab notebook
  • Acknowledgement
  • Support me
  • Roadmap
  • Contributing
  • License
  • Contact
  • Contributors

Introduction

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Fake news is playing an increasingly dominant role in spreading misinformation by influencing people’s perceptions or knowledge to distort their awareness and decision-making.

The growth of social media and online forums has spurred the spread of fake news causing it to easily blend with truthful information.

This study provides a novel text analytics–driven approach to fake news detection for reducing the risks posed by fake news consumption.

News Cateogries

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No.	Content	Discription
1	Fake news	Fake News - Sources that entirely fabricate information, disseminate deceptive content, or grossly distort actual news reports,aim of damaging the reputation of a person or entity, or making money through advertising revenue
2	Satire	Satire - Sources that use humor, irony, exaggeration, ridicule, and false information to comment on current events, providing fake insights about an on-going real news event.
3	Bias	Extreme Bias - Sources that come from a particular point of view and may rely on propaganda, decontextualized information, and opinions distorted as facts.
4	Conspiracy Theories	Conspiracy Theory - is an explanation or interpretation of events that is based on questionable or nonexistent evidence, which are almost always completely fabricated, even if individual elements of the theories contain nuggets of fact -- can be presented as fake news when they are packaged as factual news stories.
5	Rumor	Rumor - Sources that traffic in rumors, gossip, innuendo, and unverified claims, statement consisting of unverified pieces of information at the time of posting
6	State	State News - Sources in repressive states operating under government sanction.
7	Junk science	Junk Science - Sources that promote pseudoscience, metaphysics, naturalistic fallacies, and other scientifically dubious claims.
8	Hate	Hate News - Sources that actively promote racism (based on something such as religion, ethnicity, nationality, sexual orientation), misogyny, homophobia, and other forms of discrimination.
9	Clickbait	Clickbait - Sources that provide generally credible content, but use exaggerated, misleading, or questionable headlines, social media descriptions, and/or images.
10	Unreliable	Unreliable - Proceed With Caution, Sources that may be reliable but whose contents require further verification.
11	Political	Political - Sources that provide generally verifiable information in support of certain points of view or political orientations.
12	Reliable	Credible - Sources that circulate news and information in a manner consistent with traditional and ethical practices in journalism (Remember: even credible sources sometimes rely on clickbait-style headlines or occasionally make mistakes. No news organization is perfect, which is why a healthy news diet consists of multiple sources of information).
13	Celebrity	Celebrity - Celebrity/Gossip magazines (sometimes referred to as tabloid magazines) are magazines that feature scandalous stories about the personal lives of celebrities and other well-known individuals.
14	Hoax	Hoax - is a news containing facts that are either inaccurate or false but which are presented as genuine. A hoax news conveys a half-truth used deliberately to mislead the public.
15	Unknown	Unknown - is a sources that have not yet been analyzed (many of these were suggested by readers/users or are found on other lists and resources). Help us expand our resource by providing us information!
16	Propaganda	Propaganda - is the spreading of rumors, information which is often inaccurate and especially of a biased or misleading nature, used to promote a political cause or point of view.
18	Misinformation	Misinformation - is false, inaccurate, or misleading information that is communicated regardless of an intention to deceive. Examples of misinformation are false rumors, insults, and pranks.
19	Poor Sourcing	Poor Sourcing - is the out-of-context information that does not on its own constitute fake news. This kind of information is not wholly fabricated, and it can exist within a news report that is based on actual events that occurred but does not have the proper evidence for it.
20	Lack of Transparency	Lack of Transparency - is the out-of-context information that does not on its own constitute fake news. This kind of information is not wholly fabricated, and it can exist within a news report that is based on actual events that occurred but does not have the proper evidence for it.
21	False Information	False Information - Sources that entirely fabricate information, disseminate deceptive content, or grossly distort actual news reports,aim of damaging the reputation of a person or entity, or making money through advertising revenue
22	Sensationalism	Sensationalism - In journalism (and more specifically, the mass media), sensationalism is a type of editorial tactic. Events and topics in news stories are selected and worded to excite the greatest number of readers and viewers.
23	Plagarism	Plagiarism is the representation of another author's language, thoughts, ideas, or expressions as one's own original work. Plagiarism is considered a violation of integrity and a breach of journalistic ethics.

Architecture

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System interface design

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Poster

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Poster-01	Poster-02
PDF	PDF

Demo-video

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Tech stack

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1. Programming Language : Python
2. Frontend : Streamlit
3. API : VirusTotal API
4. Platform : Colab notebook, Jupyter notebook, Visual studio code
5. Testing tool : Apache Jmeter, Google PageSpeed insights, Selenium
6. Machine learning Library : Tensorflow, PyTorch, Keras, Huggingface transformers, simpletransformers
7. Web framework : Flask
8. WSGI server : Gunicorn
9. Web server : Nginx
10. Cloud infrastructure support: AWS EC2 instance, AWS secret manager
11. Containerization/deployment: Docker

Project Deliverables

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The Project Deliverables includes detailed documentation of the project, including Software Requirement Specifications (SRS), Software Project Management Plan (SPMP), Software Design Description (SDD), Software Testing Document (STD), Implementation details, and Result and conclusion.

Installation

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Clone git repository

    $ git clone "https://github.com/hritik5102/Fake-news-classification-model"

Prerequisites

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Run the setup file on git bash or any linux terminal:

$ bash setup.sh

How to run the app?

Run the following commands:

1. bash start-server.sh on git bash or any linux terminal This will start the deep learning model servers.

2. cd src

3. streamlit run app.py

How to stop the Deep learning model servers?

run bash stop-server.sh on git bash or any linux terminal

Google colab notebook

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Filename	Notebook
GPT2 Model
BiLSTM + GloVe Model
BERT Model
Roberta Model

Acknowledgement

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Some resources have been used to build this project, so I'd like to acknowledge the resources in the reference section. Take a look.

If you think that I've referred any piece of code or material but have not acknowledged it. Please create an issue mentioning the site info & a link pointing to that material.

Support me

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Roadmap

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See the open issues for a list of proposed features (and known issues).

Contributing

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Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

Make sure your changes don't break existing functionality without good reason.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Discuss the features, ideas you want to add to the project from here 📌

License

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Distributed under the MIT License. See LICENSE for more information.

Contact

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No.	Name	Twitter handle 🐦	Email 📩
1.	Heet sakaria	@HeetSakaria	heet.sakaria@somaiya.edu
2.	Hritik Jaiswal	@imhritik_dj	hritik.jaiswal@somaiya.edu

Project Link - hritik5102/Fake-news-classification-model (github.com)

Contributors

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Thanks goes to these wonderful people 👏👏

Heet Sakaria 💻 🤔📖🖋

vedangparasnis 💻📖

Viraj Thakkar 📖

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