Finding and Assessing Journalistic Sources in Social Media

This line of research largely has to do with developing visual analytic interfaces and strategies to help media people such as journalists or public affairs analysts make sense of social media streams. For instance, in our CHI 2010 paper we looked at streams of Twitter messages in conjunction with sentiment analysis to assess aspects of the 2008 presidential debate such as strong or weak moments of either candidate, periodicity of sentiment response, and indications of controversial topics. In continuing work, we built Vox Civitas, a more comprehensive visual analytics environment for journalists interested in developing story ideas based on the individual and aggregate social media response to large scale broadcast events.

A further extension culminated in a prototype application called SRSR (standing for “Seriously Rapid Source Review” and pronounced “Sourcer”) which incorporates a number of advanced aggregations, computations, and cues that we thought would be helpful for journalists to find and assess sources in Twitter around breaking news events. The features we built into SRSR were informed by talking with many journalists and include facilities to filter and find eyewitnesses and archetypical user-types, as well as to characterize sources according to their implicit location, network, and past content. The SRSR interface allows the user to quickly scan through potential sources and get a feeling for whether they’re more or less credible and if they might make good sources for a story. We also evaluated it on two breaking news scenarios with seven super-star social media editors at leading local, national, and international news outlets. Read a blog post about the application here, or get all the details in the papers below:

  • N. Diakopoulos, M. De Choudhury, M. Naaman. Finding and Assessing Social Media Information Sources in the Context of Journalism. Conference on Human Factors in Computing Systems (CHI). May, 2012. [PDF]
  • M. De Choudhury, N. Diakopoulos, M. Naaman. Unfolding the Event Landscape on Twitter: Classification and Exploration of User Categories. Proc. Conference on Computer Supported Cooperative Work (CSCW). February, 2012. [PDF]
  • N. Diakopoulos, D. A. Shamma. Characterizing Debate Performance via Aggregated Twitter Sentiment. Conference on Human Factors in Computing Systems (CHI). April, 2010. [PDF]
  • N. Diakopoulos, M. Naaman, F. Kivran-Swaine. Diamonds in the Rough: Social Media Visual Analytics for Journalistic Inquiry. Conference on Visual Analytics Science and Technology (VAST). October, 2010. [PDF]
  • N. Diakopoulos, S. Goldenberg, I. Essa. Videolyzer: Quality Analysis of Online Informational Video for Bloggers and Journalists. Conference on Human Factors in Computing Systems (CHI). April, 2009. [PDF] [Video]