Fake News Detection using ML

Fake News Detection Using ML (Machine Learning) is a field that utilizes machine learning techniques to identify and prevent the spread of fake news. With the popularization of social media and the internet, fake news has become a serious problem, causing negative impacts on society and individuals. The application of ML in this field aims to automatically identify and filter fake news through algorithms and models, in order to curb its spread. The common methods of ML in fake news detection include content-based analysis, network analysis, social media analysis, etc. Content based analysis mainly uses natural language processing techniques to identify language patterns and semantic features in texts, and determine their authenticity. Network analysis studies the network structure of news release and dissemination, identifying the propagation paths of fake news. Social media analysis utilizes social media data to analyze user behavior and interaction patterns to identify fake news.This is a mind map about Fake News Detection using ML. The process consists of 11 steps, namely: Literature Survey of ML Models, ML Model shortlisting and Implementation, Comparative Analysis of ML models, Deployment on Telegram Fake News Detection Bot, Study of DL models and Survey, LSTM implementation and evaluation, Improvements in Feature extraction: Sentiment Analysis, Improvement trial in LSTM: Bi Dir LSTM, Evaluation and Feasibility Analysis, Web Deployment, Testing and Documentation. Each step has sub branches for further detailed description. Suitable for people interested in machine language.

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• Fake News Detection using ML

  Fake News Detection Using ML (Machine Learning) is a field that utilizes machine learning techniques to identify and prevent the spread of fake news. With the popularization of social media and the internet, fake news has become a serious problem, causing negative impacts on society and individuals. The application of ML in this field aims to automatically identify and filter fake news through algorithms and models, in order to curb its spread. The common methods of ML in fake news detection include content-based analysis, network analysis, social media analysis, etc. Content based analysis mainly uses natural language processing techniques to identify language patterns and semantic features in texts, and determine their authenticity. Network analysis studies the network structure of news release and dissemination, identifying the propagation paths of fake news. Social media analysis utilizes social media data to analyze user behavior and interaction patterns to identify fake news.This is a mind map about Fake News Detection using ML. The process consists of 11 steps, namely: Literature Survey of ML Models, ML Model shortlisting and Implementation, Comparative Analysis of ML models, Deployment on Telegram Fake News Detection Bot, Study of DL models and Survey, LSTM implementation and evaluation, Improvements in Feature extraction: Sentiment Analysis, Improvement trial in LSTM: Bi Dir LSTM, Evaluation and Feasibility Analysis, Web Deployment, Testing and Documentation. Each step has sub branches for further detailed description. Suitable for people interested in machine language.