What are people saying about Colin Kaepernick?

As you have likely heard, at the 49ers game last Thursday, Colin Kaepernick decided to sit out the national anthem as a protest against police brutality and racial discrimination. As this is a hot button issue, this action predictably set off a social media firestorm, with some praising Kaepernick for his courage in standing up for a cause he believes in, and others calling him out for his lack of respect for the country, the military, and police officers. I wanted to take the opportunity to dig into what people are saying  and see if we can understand the varied reactions to this polarizing event.

To do this, I leveraged a new tool called Cognizer, an R package developed by Columbus Collaboratory that links directly with IBM Watson to provide easy services like text analysis in R. This service provides sentiment analysis, keyword matching, emotional scoring, personality insights, and tone analysis. Ideally, this can provide deeper insights into various aspects of what people are saying about Colin Kaepernick and how they’re saying it. However, since the API limits are fairly stringent for free users (1,000 calls a day), you have to pay to play if you want to examine lots of text, and so I limited my analysis to about 300 tweets in order to fit everything in. Even with that limitations, I was still pretty impressed with the services these packages provide and hope this work gives somewhat of a sample if you were trying to check it out! In this analysis, I focus on the sentiment and emotion functions available in Cognizer.

Starting with sentiment, overall what sentiment are the tweets about Colin Kaepernick? The plots below suggests that they are negative a little over twice as much as they are positive.

If you’ve seen tweets about him, this is not super surprising. Even when someone is supporting Kap, they can be doing so in a negative say. For example, @LongLiveKermy said “So many racist hate tweets toward Colin Kaepernick thus proving he was entirely right for not standing.” which scored as negative sentiment, but is actually a tweet in support of Kaepernick, so his support might be a bit understated here. (I will note that even though tweets like this popped up a few times, they were few and far between; IBM Watson does a good job of correctly identifying sentiment).

What about emotions? This service scores each tweet on 5 emotions: fear, anger, disgust, sadness, and joy (just like Inside Out! RIP Bing Bong). Below is the distribution for each emotion. The one that stands out the most is disgust. Anger and sadness are the next two common emotions, with fear and joy bringing up the rear.

But how do these distributions vary by tweet sentiment? The distributions aren’t quite as different as one would expect. The main difference that stands out is among joy, as there is a very right skew for negative tweets, but the distribution is more or less flat for positive tweets. When looking at median emotional scores by sentiment, joy has the largest difference between positive and negative tweets. What is interesting as well is that disgust, on average, scores the highest for both positive and negative tweets. I’m no psychologist, but I think this provides some pretty good insight into how the country reacts to these polarizing events, we’re likely to either be disgusted by the action being protested or by the protest itself.  In this sample, many people are showing support for Kaepernick by expressing disgust for the issues he is sitting down for, or they’re disgusted with him for doing so. Obviously I’d need more than 300 tweets to fully infer that, but I thought it was a potentially interesting directional read.

I had a lot of fun working with this package and on this topic. In the future, I hope to delve into the package a little deeper to check out tone and personality analysis, either on this topic or some others (likely related to the election).Overall, I thought the package was really easy to use and provided some good insight into an important topic.

Update: Code at my Github.