...where I can fix things like broken links. It was republished on my school's website, but unfortunately I don't have control over the HTML there. In the cases where stuff broke, it's because I linked to the author's personal manuscript and not the official journal page (because the former is freely available to everyone rather than behind a paywall).
I care a lot about Big Data research, especially involving social media, and think we too often ignore the conceptual leaps required to make inferences about the human experience from social media.
Here, the leap is: sentiment_analysis(what people say on social media) == how they really feel.
The point about LIWC (the sentiment analysis tool used here) is that (a) it's flawed, and perhaps not in a "random error" kind of way, (b) we don't really know how well it works because it has not been validated in social media or Facebook posts specifically, which should make most researchers nervous (but somehow doesn't), and (c) there's evidence from other data sources that suggests LIWC is biased in a way that would underrepresent the emotions of interest (namely low arousal emotions like sadness, depression, loneliness, feeling left out, etc.; these aren't picked up by LIWC as well as other negative emotions like anxiety).
See e.g.:
O’Carroll Bantum, E., & Owen, J. (2009). Evaluating the Validity of Computerized Content Analysis Programs for Identification of Emotional Expression in Cancer Narratives. Psychological Assessment, 21, 79-88.
The point isn't that using LIWC means the experiment is invalid, the point is that it should give us pause and caution us against stating the conclusions of the experiment too strongly. I think the authors do state their conclusions a bit strongly.
The other main critique is about biases inherent in social media as a datasource itself. The private, randomly-solicited emotion samples of experience sampling are more likely to capture Facebook's true emotional impact than the non-private, self-selected emotion samples of status updates. Let's just take arousal bias. If we know that people are more likely to post when they're emotionally aroused (excited, angry, fearful/anxious), but the emotional consequences we're concerned about involve low arousal emotions (sadness, depression), then there's a serious chance we'll miss exactly the emotions we're arguing don't exist. That's a big problem.
I think we're a bit too enamored with the idea that Big Data provides an unbiased window into the human experience. I think a tremendous amount of social science would argue otherwise.
Stepping back a bit, the Facebook experiment raised many interwoven issues for me, which is why they featured in the broader piece I wrote. About Facebook's culture, about ethics in corporate vs. academic research, about Facebook's emotional impact (foolish to believe there is none, I think), about how we use Big Data in research, about how we cope with Facebook's presence in our lives, for better or worse.
You may have a great experience with Facebook, and that's great. Others struggle with the medium. I mention social comparison not just because it's been the focus of research on social media, but also because the authors of the experiment bring up social comparison (as well as the "alone together" argument) in their work. Because they try strenuously to rebut the unfavorable findings about Facebook's emotional consequences, I thought it was important to point out that their study seems designed in a way that would systematically underrepresent exactly those negative emotions they're arguing against.
Certainly, some negative emotions were "contagious" through social media (anxious news reports, for example), as were some positive emotions. But is the emotion that gets retransmitted the full emotional picture? Probably not. Probably many emotions and feelings get withheld. The social science would suggest that when positive posts make us feel bad, we won't go back on Facebook and broadcast those feelings to all of our friends.
Thanks a ton for engaging with my critique and responding with your own. Writing that was a labor of love, and I learn a lot from the feedback, good and bad. Sorry it was so long.