Twitter Outrage, Charted: The Partial Anatomy of the #FuckPhyllis Trend, or Why I Don’t Trust BuzzFeed

by michael_simeone

On Sunday, January 26, Chancellor Phyllis Wise of the University of Illinois at Urbana-Champaign sent out notice that despite below-freezing temperatures, classes at the University would resume according to schedule on Monday morning.  The prospect of attending class in these conditions upset many students at Illinois, prompting an uproar on Twitter reported by BuzzFeed and the creation of the phony @ChanPhyllisWise twitter handle as well as the #FuckPhyllis hashtag.  As evidenced by a dozen or so embedded tweets, the #FuckPhyllis trend contained despicably racist and sexist insults directed at the Chancellor. Buzzfeed also reported that students turned to change.com to produce a petition calling for classes to be cancelled, which garnered over 7,000 signatures over night.   According to social media analytics service Topsy, the #FuckPhyllis hashtag only included some 2,000 tweets at the height of its activity.  And while there is no disputing how reprehensible the conduct of these students is, we should look closer at this trend: who is tweeting, when, about what, and in agreement or to echo whom? BuzzFeed calls as much attention to the hate speech as possible (seemingly implicating every petition signer in the social media hate-fest), but to whom did this hashtag, now effectively dead, ultimately belong?

When considering the scale of Twitter, 2,000 tweets is a very small number of communications.  But the reason #FuckPhyllis is so interesting (and likely the reason Buzzfeed paid attention) is how rapidly it trended:

Screen Shot 2014-01-28 at 3.43.22 PM

Today the hashtag sees barely any activity at all.  But in the lifespan of this particular social media spike there may be some interesting patterns surrounding the network topology of different communities of tweeters. It’s important to suspect that many of the offensive tweets have since been removed by their authors, but many do remain. Still, this is a hole in our data. Assuming many of the most vile things are now gone, all we can do is examine hostile vs. corrective communication networks rather than a deep look at racist tweets of unknown quantity and verbiage.

What follows is a quick look into  the #FuckPhyllis hashtag, the twitter activity surrounding the topic on January 26, 2014, starting at around 10pm CST all the way up to 5pm CST the following day.  I used NodeXL to pull tweets that used the #FuckPhyllis hashtag, and so the limitations of the Twitter API are present: not every tweet is included in this analysis.  Also, embedded tweets appear to refer to UTC rather than CST (making tweets from the 26th appear to be dated on the 27th of January) Still, there are some general trends in the hashtag that are illuminating.

First, lets take a look at a graph of the tweets and retweets from the two hours of the life of the hashtag:

Image1240

In this graph, lines or edges indicate a retweet, and loops indicate tweets that are likely responses to tweets by the same user.  Thus, the appearance of many loops lumped together shows people holding conversations by replying to their own tweets.  Each node is a tweet.  Node colors are determined by a modularity algorithm (Girvan-Newman) that groups nodes by shared connections.  What is shocking about this graph is how many tweets are actually reacting against the original negativity of the #FuckPhyllis trend.  The node highlighted in red, with by far the most retweets, is @suey_park’s retweet about white privilege from U of I student Briana Walker (who is synthesizing comments made by Park earlier on):

In fact, nearly every tweet that has a truly graphical property–retweeted by overlapping communities of agents who in turn are retweeted–rather than a simple hierarchal layout (see the red subgroup in the lower left of the above graph) contains or retweets a message of anti-racism.  This sentiment ranges from simple eye rolling to more sophisticated thoughts on white privilege at U of I.  Within a few hours of the first hateful tweet,  social media conversation was dominated (centered on) by reprimands and anit-racist commentary.  The graph demonstrates that it is the anti #FuckPhyllis tweets that have a high Eigenvector centrality (used in network analysis as one way to study influence), or in other words  a high number of retweets and mentions that also happen to be retweeted and mentioned.  The influence of anti-racist tweets appears to outstrip that of anti-Wise tweets.

One of the most popular remaining anti-Wise tweets (although this contains zero racist overtones) is represented by the aforementioned red subgroup demonstrates several retweets of the following:

Even though the above tweet was retweeted a total of 64 times in its total lifespan, the network reveals that it has little influence over the general social media conversation as it evolves.   Now in the upper left of the next graph,  this particular tweet remains marginal in the following 17 hours worth of tweets:

hashtagfuckphyllis

The dark blue component in the middle of this second graph continues to feature @suey_park (although this time for a different but similarly anti-racist tweet).  @suey_park’s tweet again has the most mentions and retweets by those who are also retweeted and mentioned.  As before, the graph is predominantly an anti-racist backlash, and the most retweeted of the anti-Wise messages is not overtly racist or sexist.

ChanPhyllisWise

By comparison, here is a graph of tweets that include “ChanPhyllisWise”, the title of the now deleted bogus twitter account used to mock the Illinois Chancellor:   Similarly, this graph has been clustered by modularity.  The majority of tweets shown on this graph demonstrate the kind of vitriol reported by Buzzfeed, and here are the two central dark blue and light blue tweets that rest in a pair at the middle of the graph:

Both of these tweets demonstrate a relatively high total degree centrality (retweeted more than peer tweets) but a comparatively low Eigenvector rating (retweeted, but not by those who were themselves retweeted). Furthermore, we can see the topology of this graph to be markedly different from the first two.  Where as the first two showed resonance around a small set of tweets, the third shows significantly more isolated conversations or tweets that were not retweeted at all.  The conversation that was significantly more sexist, racist, and hostile in tone is also one that features more fractured conversations, less information exchange, and lower connectivity among all nodes.

Conclusions

I’m tempted to hypothesize that conversations that feature this kind of hostility in social media have a performative quality to them, and users appear to want to one up one another.  The network topology of the third graph, for what little information we have, suggests self interest and not very much consensus beyond using the same hashtag and adopting an insulting tone.  The first two show preferential attachment to @suey_park and some consensus about who is “right.”  It would be interesting to listen in on similar (and sadly inevitable) Twitter trends as they emerge and again compare the topology of hostile and corrective social media networks and see how they stack up.  From their networks of communication, we can see a difference between principle and anger as motivating principles for social media use.   As Christopher Simeone put it when presented with this case, “principled actors who see themselves as part of a bigger cause or purpose behave differently than those whose only uniting principle is rage and self interest.”  Or perhaps we don’t have enough information to tell yet.

What’s interesting, however, is that the BuzzFeed article, while calling attention to the racist, sexist, and hateful things U of I students tweeted, greatly over-represents the salacious portions of this social media trend.  We’ll never know exactly how many tweets were deleted out of shame, but analysis of this tend shows a swift and harmonic response that obliterated anti-Wise sentiment and replaced it with a new conversation about white privilege.  Yes, U of I students filled out a petition that got 7,000 signatures, but that does not equal 7,000 racists.  Clicking a bubble that tries to get one out of class is very different from taking to social media to spread hate.  The real A missing piece of the story is the response and unity of response to #FuckPhyllis.

Source files from NodeXL:

fuckphyllis hashtagfuckphyllis ChanPhyllisWise

Update 1/28/2014 9:50 MST: I want to encourage everyone to see Kevin Hamilton’s comments below, as they raise some important concerns, and I feel the need to clarify.  I do not believe that the University of Illinois is a place free of racism and white privilege or a place where anti-racism somehow excuses acts of racism.  I do, however, see the data discussed above as  showing a contrast in approaches to communication that may map on to angry vs. principled tweeting, and, crucially, how divided the University can be when it comes to issues of race. While there is persistent and unjust quarter given to racial intolerance on campus (highlighted below by Kevin’s comments), this should not obfuscate that there are principled actors at U of I, and that the story of the place is deep and storied division on racial issues, not thorough moral decay.

Oh, and 1/28/2014 11:20 MST: I’m a U of I alum (2011)

Update 1/29/2014 4:32 MST: The joint statement published today by U of I President and Board Chairman

Update 2/3/2014 11:12pm MST: Part 2, pertaining to social media stories and where they go wrong

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