PLAY PODCASTS
Machine Learning the Facebook URLs Dataset to Study News Credibility, with Dr. Tom Paskhalis
Episode 148

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

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.

Social Media and Politics

August 21, 202242m 42s

Audio is streamed directly from the publisher (dts.podtrac.com) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.

Show Notes

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. 

Topics

political science machine learningmedia politicsdata science politicsdata science researchpolitics mediasocial media journalismjournalism machine learningsocial media data sciencedata science journalismmachine learning facebooksocial science onemachine learning journalismstudies machine learningdata science social mediapolitics social mediamachine learning political sciencejournalism data sciencesocial media journalism data sciencesocial media researchmachine learning