Bond, Robert M., Christopher J. Fariss, Jason J. Jones, Adam DI Kramer, Cameron Marlow, Jaime E. Settle, and James H. Fowler. “A 61-million-person experiment in social influence and political mobilization.” Nature 489, no. 7415 (2012): 295.
Summary & Reflection:
Since traditional human behavior mainly spread by face-to-face social networks, which is difficult to measure social influence effects in it. But for online social networks, it might be a possible way to evaluate social interaction effects in it and the paper conducts a randomized controlled trial of political mobilization messages to identify social influence effects. Since the act of voting in national elections is a typical behavior that spreads through networks, the paper conducted a randomized controlled trial with all users in Facebook who access the website on the day of US congressional elections in 2010. All the users are randomly assigned to three groups — a ‘social message’ group, an ‘informational message’ group or a control group. The results imply that it’s not rigorous to say previous research suggested that online messages do not work, which possibly caused by small conventional sample sizes. The political mobilization messages can directly influence political self-expression, information seeking and real-world voting behavior of millions of people. In addition, the experiment measuring indirect effects that spread from person to person in the social network suggest strong ties are instrumental for people’s behavior in human social networks.
When measuring the direct effects of online mobilization by assigning users into three groups, they measure acts of political self-expression and information seeking. Personally, I don’t think the whole experiment design is rigorous and valid enough for several reasons. Firstly, there is a huge imbalance in the sample size of the social and informational message groups, i.e., 60,055,176 versus 611,044. I don’t think such a massive difference can be ignored, for instance, how do we make sure 611,044 users in group 2 are sufficient enough to represent the whole user community? Secondly, I’m not convinced that information seeking is a good indicator of people’s political positivity. If a person clicks “I voted” button, it would be very likely that he or she will not click the polling-place link because they’ve voted.
Kramer, Adam DI, Jamie E. Guillory, and Jeffrey T. Hancock. “Experimental evidence of massive-scale emotional contagion through social networks.” Proceedings of the National Academy of Sciences 111, no. 24 (2014): 8788-8790.
Summary & Reflection:
Emotional states can be contagious, which leads people to experience the same emotions without their awareness. The paper test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed on Facebook. It turns out that emotions expressed by other users on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks.
For the experiment design, they use Linguistic Inquiry and Word Count software word counting system to determine whether posts are positive or negative, which I don’t think is sufficient. This is like sentiment analysis by only counting positive and negative words. How about positive sentences expressed by the double negative? It would classify sentences with sarcasm into positive posts, but we all know that language expression is a pretty complex phenomenon. From my perspective, we may solve this problem by applying classic models of polarity analysis on posts. My another concern would be whether people update positive posts are actually happy in real life. There are many examples that people’s attitude showed in their social media does not necessarily represent their real mood or life status, sometimes people even pretend to be positive. This would raise another question about the definition of emotional contagion.
