Truth Seeker of the Largest Social Media Content using Machine Learning Algorithms
Maysa Khalil, Mohammad Azzeh · 2023
Social media is the source of news, information, and opinions for many people. So, the pursuit of truth in the realm of social media is a noble and essential endeavor because it underpins informed decision-making, counters misinformation, nurtures critical thinking and builds trust. We used machine learning(ML) algorithms and Natural Language Processing(NLP) to understand and process human language used in social media posts of the Largest Social Media Ground-Truth Dataset for Real/Fake Content to create a model that could be used as a reference to detect fake news from Twitter posts, which is a binary classification problem. We created two different datasets from the original large dataset using different NLP vectorization techniques (word2vec and TF -IDF), generating different features per dataset. Then we applied seven ML algorithms on each dataset and compared the results by extracting the confusion matrix and ROC per model. Random forest and AdBoost algorithms achieved the best F1 scores.