We summarize the state of progress in artificial intelligence as used for classifying misinforma- tion, or ’fake news’. Making a case for AI in an assistive capacity for factchecking, we briefly examine the history of the field, divide current work into ’classical machine learning’ and ’deep learning’, and for both, examine the work that has led to certain algorithms becoming the de facto standards for this type of text classification task.
In a practical experiment, we benchmark five common text classification algorithms - Naive Bayes, Logistic Regression, Support Vector Machines, Random Forests, and eXtreme Gradient Boosting - on multiple misinformation datasets, accounting for both data-rich and data-poor environments.
Sometime in March 2018, the Sri Lankan government blocked access to Facebook, citing the spread of hate speech on the platform and tying it to the incidents of mob violence in Digana, Kandy.
A whitepaper distilling LIRNEasia's current thoughts on the possibilities and issues with the computation extraction of syntactic and semantic language from digital text.