While we were enjoying Big Boulder 2016, a global event of far-reaching implications took place halfway across the world: Britain, against all polling predictions, voted to leave the European Union. Only five months later, the United States experienced our own upset with the presidential election, resulting in an outcome that even our best data analysts didn’t see coming. The aftermath of these events left a lot of people asking the same question: How were we all so wrong?
Deb Roy, Director of the Laboratory for Social Machines at the MIT Media Lab and Chief Media Scientist at Twitter, was already uniquely set up to study this very question. Using the collective power of the MIT Media Lab professionals, the team began collecting data in the months leading up to the election, attempting to determine the outcome. When those results were flagrantly different than anticipated, the team wanted to know why.
From August 2015 to the US Election Day in November, Roy’s team documented one billion tweets discussing the election specifically. They sought to map out what they called the “horse race of ideas,” filtering tweets through their deep learning network and separating them into topic classifications. Yes, the social media public was talking about the election online–but what do we talk about when we talk about elections? The deep learning network would “read” news sources across the political spectrum, from Huffington Post to Breitbart, and then “listen in” to the Twitter users as they chatted about those same conversation topics.
As the tweets were mined, the network narrowed the list of topics down to 19 particular conversations ranging from gun control to immigration to education and race. Using these tweets, the MIT Media Lab constructed debate briefs for presidential debate moderators leading up to the election, allowing moderators to comb through and select questions that would matter most to the American people.
The problem, they would eventually find, is that the topics that were the most important to American citizens were not necessarily the topics that were being discussed in common news coverage. Even more challenging was the discovery that users had a tendency to read only what they agreed with, following primarily their candidate of choice and, perhaps unwittingly, committing themselves to an insular “tribe,” as Roy called it.
With regard to media coverage, Roy made two important observations surrounding the role that journalists played in election influence. First, per a Pew study that year, more than 70% of Americans got their news from television. Whether the media loved or hated soon-to-be President Trump, he was the candidate who easily dominated television coverage across outlets, partisan or not. With 70% of potential voters taking in a 24-hour news cycle disproportionately covering one candidate, it’s not hard to understand how that candidate stayed top-of-mind for many.
Roy also noted that conversation is able to destroy brands and people, much like the conversation surrounding the very public snafus of brands like United Airlines or Pepsi…but the conversation is still centered around those brands or people. The old adage goes: there is no such thing as bad press.
The second major observation Roy discussed was the disparity in the type of topics covered by media outlets versus the topics that social media users seemed to care about. For instance, in the weeks leading up to the Vice Presidential debates, over 30% of media coverage revolved around the VP candidates; on Twitter, only 3% of conversations seemed to care about these candidates at all. Additionally, the data showed a major divergence in topics discussed by journalists online versus civilians: while campaign finance was heavily covered by the media, users online seemed disinterested in those talking points, preferring to focus on conversations about race.
So what does it all mean for the future of media coverage as it relates to the public?
First, we must start bridging the gap between journalists, pollsters, and the general constituency. Roy noted that 80% of journalists live in 1 of 3 major cities in America, not leaving much room for the topics that rural Americans care most about. “The data shows that people in rural Wisconsin don’t care about Russia,” he remarked. “They care about local issues that affect them.” Roy and the Media Lab are working on ways to show journalists and pollsters their own research bias through network data visualization. Additionally, they are beginning pilot programs to build networks of influencers in rural America whose voices are respected by citizens in those areas, and need to be heard.
Another point of interest is exposing social media users to their own “tribe,” and the information bias to which they leave themselves vulnerable. The Media Lab quietly released a Chrome extension in 2017 called FlipFeed, which allows Twitter users to see into the feed of a user completely unlike them. The user experience feels like ChatRoulette: a user can click “Flip My Feed” on their Twitter interface and the extension, using social listening tools to determine what tribe that user may belong to, “flips” to the feed of a Twitter user in a distinctly different tribe. The extension gives users a view into worlds they could otherwise ignore, which would only create wider expanses between people groups and political ideologies.
Finally, the question remains: how now should news be created and affected? Should journalists lead by following, listening to social media users and the topics that are important to them, and creating story topics from those conversations? Do we need to retire the old way of news creation in favor of listening to the data in front of our faces? Most importantly, what will this take and how will it affect publishers?
What we do know is that post-game analysis shows us the data we didn’t know existed, and we can’t afford to ignore it any longer.
- Social conversation online is evolving so rapidly that data collectors have a hard time maintaining the accuracy of which topics are relevant; the accuracy of that relevance has a tendency to drastically decrease within a two-month period.
- Polling data differed starkly from Twitter conversations as well: the Media Lab found that Twitter users were very interested in foreign policy, while polling data indicated the opposite.
- The MIT Media Lab team found that if Twitter users followed only one of the 19 early primary candidates, those users also tended to lean toward voting for that candidate.
- Surprisingly, most “Sanders tribes” also had two-way connections with “Trump tribes.”
- Through looking at network data visualization, it was discovered that most journalists tended to follow Twitter users connected to nearly every tribe but the “Trump tribes.”
- Citizens have begun to tire of “mainstream media agendas,” Roy notes. “There’s a real feeling that, my God, we’re being manipulated here.” When this happens, citizens simply create “new” media, giving credibility to previously discounted outlets and giving rise to “fake news” phenomena.
- There were more people who followed both Trump and Clinton than just one or the other. Determining language and intent played a big part in parsing out this demographic: for instance, did they use the word “illegals” or “undocumented?”