Data on #ShoutYourAbortion Can Accurately Predict One’s Stance on Abortion Without Reading Content
Why should a movement ostensibly about video games be so vocal about abortion? Because it’s always been about harassing & controlling women.
— Lindy West (@thelindywest) October 12, 2015
We probably could’ve concluded the above without a study of data, but hard data always helps. Brian Clifton, Gilad Lotan, and Emma Pierson over at Quartz studied over 100,000 tweets to follow the progress of #ShoutYourAbortion and show how they can accurately figure out someone’s views on abortion via their social media connections – without actually reading their tweets or profile. Those who identify as members of #GamerGate are more likely to be anti-choice. Shocking, I know.
Above is a visualization the trio created to show the spread of #ShoutYourAbortion and #ShoutYourAdoption on Twitter. As they say:
Each point represents a user. Points are connected by lines where one user has chosen to follow the other. Color represents communities: groups of densely interconnected users.
In their study, they identified two distinct feminist communities, four distinct groups of conservatives, and of course, our “favorite” group of all – GamerGate. Apparently, 90% of users with strong views in the Gamergate and conservative communities are anti-choice.
However, the data shows that views on abortion aren’t nearly as cut and dried as we think. As they say in their report:
For example, there are clear differences between the two feminist clusters: the hashtag originates in Feminists 1, but users in Feminists 2 are more likely to be pro-life. Conservative clusters also differ: Conservatives 1 has many Catholics, while Conservatives 2 and 3 are more likely to describe themselves as Christian and conservative. We also observed gender skews in the anti-abortion groups: while the Gamergate community is 75% male, Conservatives 4 is 71% female. Prominent tweeters in the latter community are often young women (one profile read, “Political activist, but like, still a basic white girl. I can’t explain why I don’t need feminism, I’m too busy succeeding without it”). It’s tempting to reduce the abortion debate to two extremes, but as Twitter data shows, people support or oppose abortion for many reasons.
But perhaps the most interesting thing that this study shows is that you can predict someone’s position on this (or any) issue with over 85% accuracy, not by reading their actual tweets, but by discerning the communities of which they’re a part. Metadata is really important and powerful – so when we hear that the NSA is “only collecting metadata,” that’s enough. It’s an accurate way for anyone to identify your beliefs and communities, and they doesn’t even have to look at the substance of your content.
The folks at Quartz admit that the issue of abortion is particularly inflammatory:
It is worth noting that we are studying a group with unusually strong views; people tweeting about the abortion debate are not a representative population. They are arguably, however, an especially influential population—the ones who speak up online, who vote, who join special-interest groups and picket abortion clinics. It is a problem if the people who are most vocal about this debate are also so polarized.
It’s kind of disturbing. While it’s awesome that one person tweeting a tweet can have this kind of impact — starting movements, becoming an activist, working towards change — this is also indicative of a digital culture that is not only increasingly polarized, but that polarization is happening without processing any actual information. We retweet tweets because people we know and trust who believe things we believe do so, often without fully reading/processing the content. Polarity without nuance. Us vs. Them. Sharing positions without sharing information.
Check out all the info on the study over at Quartz, and then we’d love to hear from you below! How do you feel about this? What do you think can be done (if anything) to foster more nuanced conversation online? We want to know!
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