In the last decade, user comments have become routinized, evolving into an engrained part of the digital news sphere around the globe. Along with the increasing popularity of user comments on news websites, the rise in popularity of social media has opened up other possibilities for posting comments on news content, such as commenting on news articles on Facebook or Twitter, or using the Facebook Comment Plugin to comment on newspapers’ websites. 

 

This project uses computational methods and tools for comparatively studying user comments in each platform and across platforms and news spheres. We are currently developing several custom tools for scraping comments from various news sites and from various social media platforms. The tools are designed to enable cross-platform comparison of commenting behavior, using analytic measures such as the comments' length, count, sentiment, pace and duration.