The Natural Language Processing Approach to Mitigating the Fake News Crisis
Combat Misleading Content
Natural Language Processing (NLP) researchers are working to tackle the spread of fake news online. This involves developing ways to automatically identify and classify content as fake, by analyzing the language patterns and sources. They also study how false claims spread through social media, to understand this process. NLP can assess the reliability and biases in the language used, to judge the credibility of information. Researchers are also building systems to automatically fact-check claims against reliable sources.
[1] Methods to Identify Fake News in Social Media Using Artificial Intelligence Technologies
[2] Fake News Identification Based on Sentiment and Frequency Analysis
[3] A systematic mapping on automatic classification of fake news in social media
[4] A Novel Approach to Detect, Characterize, and Analyze the Fake News on Social Media
[5] A Detailed Survey Study on Classification and Various Attributes of Fake News on Social Media
[6] A Technique to Detect Fake News Using Machine Learning
[7] Fake News Identification on Social Media Using Machine Learning Techniques
[8] Fake news detection in social media based on sentiment analysis using classifier techniques
[9] A Fake News Classification and Identification Model Based on Machine Learning Approach
[10] ConFake: fake news identification using content based features
