Chrome Extension for Misinformation Detection

  • Tanya Lillian Borges
Keywords: NLP, Tokenization, Misinformation, Chrome Extension, Flask, TF-IDF Vectorizer


Misinformation can be stories, hoaxes, or news deliberately created to spread false news and deceive readers. Fake news has always been a part of our lives. However, it has become a topic of interest only recently. Majorly due to the rise of SOCIAL MEDIA. As stated in news articles the Supreme Court condemns these actions and advice a regulatory mechanism. As the use of social media has increased so, has the number of unreliable sources. During covid, there was a variety of misinformation floating around like, applying cow dung can cure covid. In reality, cow dung can't cure covid but can cause black fungal infection. Some other examples include inciting religious sentiments, causing chaos, and harming someone's reputation. Therefore, detecting fake news and stopping it from spreading is necessary. In this model, I have applied TF-IDF vectorizer and Passive Aggressive Classifiers to train my model. After the training and testing have been completed, a local server using Flask has been set up to assist in the development of the second phase of the project, which is Chrome Extension. The Chrome Extension is called Gossip Checker and sends selected data to the model and returns a predicted score of 0 and 1, where 0 means the data is reliable and 1 is not.


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How to Cite
Borges, T. L. (2022). Chrome Extension for Misinformation Detection. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 8(3), 6-11.