Fake News Detection is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake.

The project aims to develop a machine-learning model capable of identifying and classifying any news article as fake or not.

Algorithms are trained to verify news content; detect amplification (excessive and/or targeted dissemination); spot fake accounts and detect campaigns.

12 июл. 2024 г. · How to Create a Fake News Detection System? · Step 1: Importing Libraries. · Step 2: Importing the Dataset · Step 3: Assigning Classes to the ...

29 окт. 2024 г. · Most fake news detection systems use ML techniques to help users distinguish whether the news they view on OSNs is fake. These systems compare ...

22 февр. 2024 г. · Visual semantic features aim to detect fake news by examining the coherence of visual content, textual content and event in the semantic level.

This research aims to analyze the machine learning algorithms and datasets used in training to identify fake news published in the literature.

Knowledge-based (KB) fake news detection detects the authenticity of news by verifying fake news and facts, so this is also called fact checking. Fact checking ...

The product model will test the unseen data, the results will be plotted, and accordingly, the product will be a model that detects and classifies fake articles ...

To ensure the readers have the credibility of the content, we propose a web-based extension enabling them to distinguish from the fake and real news content.