This pair of papers explores how the behavior of users can propagate to other users through social media. In the Bond et al. study, the authors measured the influence of voting information and social sharing across a Facebook friend network. Users were assigned to one of three groups: a control group with no additional voting information, an informational message group with links to polling places and a global voting tally, and a social message group who received the same information as the informational message group plus profile pictures of friends who said they had voted. The researchers found that both the informational message group and the social message group outperformed the control group in voting influence, when measured only by clicks of the “I voted” button. When examining validated voting records, the difference between social message and control group persisted, while the difference between informational message and control disappeared. Further, the social message group greatly outperformed the informational message group under both of the same measures. In total, this experiment generated thousands of additional votes. In the Kramer et al. study, the authors manipulated the news feeds of Facebook users to either change the amount of positive-leaning or negative-learning posts that a user sees, and measuring whether that user is likely to be influenced by the bias mood of their news feed. The researchers found that emotionally-biased news feeds are contagious, and that text-only communication of emotional state is possible; non-verbal cues are not necessary to influence mood.
I was glad to see that the Bond study validated their findings with public voting records, as it’s certainly reasonable to assume that a Facebook user might see many of their friends voting and click the “I voted” button as well for the social credibility. It was certainly interesting to see the change in results, from a 2% boost between the social message group and control group when measuring button clicks vs. the 0.39% boost through voter record validation. I also didn’t expect that the informational message would have no influence in the voting-validated data; I would expect at least some increase in voting rate, but that’s not what the researchers found.
I took some issue with the positive/negative measurement of posts in the Kramer study. The authors noted that a post was determined to be positive or negative if they contained at least one positive or negative LIWC word. However, this doesn’t seem to take into account things like sarcasm. For example, “I’m so glad that my friends care about me” contains two words that I expect to be positive (“glad” and “care”), but the post itself could certainly be negative overall if the intent was sarcastic. I would expect this to affect some posts; obviously not enough of them to change the statistical significance of their results, but the amount of sarcasm and cynicism that I see from friends on Facebook can often be overwhelming. Could the authors have gotten even stronger results with a better model to gauge whether a post is positive or negative?
I had never heard of Poisson regression before reading the Kramer paper, so I decided to look into it a bit further. I presume that they authors chose this regression model because they hypothesized (or knew) that Facebook users’ post rates follow a Poisson distribution. My understanding of the Poisson distribution is that it assumes the events being recorded are independent and occur at a constant rate; however, I feel that my own Facebook postings violate both of those assumptions. My posts are often independent, but occasionally I’ll post several things on the same theme (like calling for gun control following a school shooting) rapidly. Further, I’ll occasionally go a week or more without posting because of how busy my schedule is, whereas other times I’ll make multiple posts in a day. My post distribution seems to be more bimodal than Poisson. Can anyone fill in the gap in my understanding why the authors chose Poisson regression?
