Journalism

#148: Machine Learning the Facebook URLs Dataset to Study News Credibility, with Dr. Tom Paskhalis

August 21, 2022 Academics

 

Dr. Tom Paskhalis, Assistant Professor in Political and Data Science at Trinity College Dublin, shares his research on applying machine learning to the Facebook URLs Dataset from Social Science One. The project develops a model to label whether a news domain is credible or not based on Facebook interactions data. We discuss the Facebook URLs dataset, what types of machine learning techniques were applied to it, and how the model performed across the US and EU countries. 

#141: Rating News Credibility with Algorithms, with Arjun Moorthy

April 17, 2022 Civil Society

Arjun Moorthy, co-founder and CEO of The Factual, discusses how machine learning and natural language processing can rank news for credibility. Arjun breaks down the criteria underpinning The Factual’s rating system as well as how it tries to minimize bias. We talk about some of the pitfalls of optimizing news for engagement, as well as how anonymity in a different incentive structure affects discourse around discussing news. Towards the end of the episode, we discuss the current state of AI in the newsroom, and how automation might affect news consumption in the future.


Check out the Unbiased Podcast!

And test out The Factual’s engine at IsThisCredible.com