malomodaniels/COVID-19-Fake-Tweets-Classification

Fake news is disseminated to intentionally persuade readers to accept biased or untrue beliefs by changing the way people interpret and respond to real news. Detecting fake news manually is relatively tedious especially with the rate at which information is been dispersed on Twitter, hence the need to leverage Machine Learning classifiers for this task. Search for a generally accepted COVID-19 dataset for fake news detection is still on, largely due to its novelty. A novel – more recent, and robust FND Dataset – was curated by scraping tweets from Twitter handles of some health organisations using Twitter API and socialscrapr. The dataset was preprocessed using Python libraries and Microsoft Excel after which it was split into train (80%), validation (10%) and test (10%) datasets and used on SVM, LR, DT baseline Machine Learning algorithms with SVM classifier obtaining the best result with 93.17% for both accuracy and F1 – score performance metrics.