‘Illuminating 2016’ Project Provides a Platform For Computational Journalism/Conversation Research
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ournalists have always needed valid sources of information to report the news, and more so, to interpret the impact of news developments and events to their audiences. In today’s world, with important conversations conducted across a myriad of social media, digital and online channels, there is much more information available for journalists to use in their reporting—but they face greater challenges capturing, compiling and assessing it. The iSchool’s Center for Computational Data Science (CCDS) and Behavior, Information Technology and Society (BITS) Lab innovative project provides an impactful new resource for political journalists and the public at large. Illuminating 2016 was designed to assess what indicators across social media can be used to determine support for presidential candidates. The availability of that information permits the public a greater understanding of precisely what candidates are saying through their social media accounts.
“I think one of the most important things that we learned during this election cycle is that popular public perceptions of what the candidates are doing on social media don’t square with how they’re actually behaving on these platforms. For instance, our data showed us that Hillary Clinton used attack language more often on social media, but the public perception is that Trump was the one who was attacking more.” — JENNIFER STROMER-GALLEY, PROFESSOR, CCDS DIRECTOR
TRACKING STRATEGIES During the last presidential campaign, other projects had tracked social media postings of candidates and the structured data surrounding them—such as changes in the numbers of followers and follower rates. Illuminating 2016 uniquely tracked what the candidates were actually saying. The project analyzed the unstructured data of candidates’ Tweets and Facebook posts and through state-of-the-art computational analysis, was able to characterize, analyze and count data. Initially, researchers and students worked with journalists to determine what types of information were most helpful. They then built a tool to tag the topics contained in candidate messages, and in parallel, for the public’s conversations about the candidates. Then, for
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