Political Messaging

‘Illuminating 2016’ Project Provides a Platform For Computational Journalism/Conversation Research

Journalists 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

the full election cycle (18 months), researchers collected myriad Facebook and Twitter messages and images posted by major party presidential candidates, as well as their Facebook comments and retweets and mentions on Twitter. Computer models were trained to categorize the messages. Nine categories of messaging were developed: attack, advocacy, image, issue, endorsement, call-to-action, conversational, informational or ceremonial in nature. Professor Stromer-Galley presents 'Illuminating' conclusions.

SIX FULL SERVERS It amounted to a vast repository. Researchers filled six servers with data from the social media messages of the 24 presidential candidates, according to CCDS Director Professor Jennifer Stromer-Galley. They posted the results of the data collection online throughout the election cycle, and achieved a 70 percent rate of prediction accuracy, she says.

Researchers also looked at how candidates’ messages changed over time, the way candidates drove public policy discussion through social media messaging, what types of campaign messages the public interacted with and spread the most. They also viewed how events and gaffes affected social media conversations, analyzed political fragmentation and assessed the extent to which the public

talked only with like-minded commenters. Extensive, charted data was updated as the election progressed, then made available online. During and after the campaign, CCDS team members discussed their findings and processes in the news media and in academic circles and in follow-up, produced several conference presentations and peer-reviewed journal articles.

NEXT: IDENTIFYING SHIFTS With the election done, the team took their assessments further, looking at the future of their social media data gathering platform and how to improve it. Stromer-Galley believes that the system of social listening, along with other measures of engagement, may eventually help identify shifts in public perception around political candidates—a highly valued political assessment tool.

Plans also call for fine-tuning the process during 2018’s mid-term elections and the 2020 presidential bid. Team members have been updating algorithms and some aspects of machine learning in the meantime. They want to do computational analysis of the imagery that candidates include in their social media posts, on top of the wording in their messages. In addition to its application for campaign analysis, Stromer-Galley sees the system as a means to help in crisis communications and in the event of natural disasters. The team also continues to work with journalists to encourage them to integrate the system into their research and reporting. The project is supported by theTow Center for Digital Journalism at Columbia University and the iSchool. n