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

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

FromSocial Media and Politics


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

FromSocial Media and Politics

ratings:
Length:
43 minutes
Released:
Aug 21, 2022
Format:
Podcast episode

Description

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. 
Released:
Aug 21, 2022
Format:
Podcast episode

Titles in the series (100)

Social Media and Politics is a podcast bringing you innovative, first-hand insights into how social media is changing the political game. Subscribe for interviews and analysis with politicians, academics, and leading digital strategists to get their take on how social media influences the ways we engage with politics and democracy. Social Media and Politics is hosted by Michael Bossetta, political scientist at Lund University. Check out the podcast's official website: https://socialmediaandpolitics.org.