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

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Manage episode 338454801 series 1401914
Content provided by Michael Bossetta. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Michael Bossetta or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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.

  continue reading

176 episodes

Artwork
iconShare
 
Manage episode 338454801 series 1401914
Content provided by Michael Bossetta. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Michael Bossetta or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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.

  continue reading

176 episodes

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