Reflection #8
- Bond, Robert M., et al. “A 61-million-person experiment in social influence and political mobilization.” Nature 489.7415 (2012): 295.
- 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 111.24 (2014): 8788-8790.
Summary:
Both the studies come from the Facebook Data Science team, where paper by Bond et al, shows that the social messages like user’s posts directly influence political self-expression, information seeking and real-world voting behaviour of millions of people and the paper by Kramer et al, tests whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the Newsfeed.
Reflection:
Paper 2: The objective behind Kramer et al. ‘s study on massive Facebook data was to show that emotional states can be transferred to others via emotional contagion which further leads people to experience the same emotions without their awareness. To evaluate this the author’s tested whether the posts with emotional content are more engaging and that expressions manipulated the extent to which people were exposed to emotional expression in their Newsfeed. They conducted an experiment on people who viewed Facebook in English. I wonder how will the results vary if people viewing Facebook in other language are also considered? The experiments for positive and negative emotions were conducted parallelly. Experiment 1: Exposure to friend’s positive emotional content in the user’s Newsfeed was reduced. Experiment 2: Exposure to friend’s negative emotional content was reduced. The authors have considered a status update to be positive or negative if it contains at least one positive or negative word. However, I am not convinced by this technique of determining positive or negative updates. Is it sufficient to classify posts like this without analyzing the sentiment behind the entire text? Moving forward, a control condition was introduced for each experiment where a similar proportion posts in user’s Newsfeed were omitted entirely at random. While these experiments were conducted for 1 week (Is a 1-week study sufficient to analyze the results?), participants (~155,000) were randomly selected per condition based on who posted at least 1 post during the experimental period. This makes me wonder if merely 1 status update is sufficient to identify the influence? In the end, the author’s analyzed 3 million posts, containing over 122 million words, 4 million of which were positive (3.6%) and 1.8 million negative (1.6%) and concluded that online messages influence user’s experience of emotions which may affect a variety of offline behaviors. To the best of my knowledge Facebook, these days is used more a social show-off platform where users share posts related to travel, food, success, new jobs, etc. How are such updates responsible for affecting offline behaviors and what kind of offline behaviors?
Paper 1: In this paper, Bond et al. have tried to analyze the spread of voting act in national elections through social networks. The authors have defined their hypothesis: How Political behavior can spread through an online social network and to test this hypothesis they have conducted randomized controlled trials wherein the users were assigned to a group: 1. Social message group (n = 60,055,176). The users in the social group were shown a statement at the top of their ‘News feed’, provided a link to find local polling places, showed a clickable button reading ‘I Voted’, showed a counter indicating how many other Facebook users had previously reported voting, and displayed up to six small randomly selected “profile pictures” of the user’s Facebook friends who had already clicked the ‘I voted’ button. 2 Information message group (n= 611,044)– Users were shown the message, poll information, counter and button, but they were not shown any faces of the friends.3 The control group (n=613,096) did not receive any message at the top of their Newsfeed. There is a huge imbalance between the #of users in social message group and the #of users in information message and control group. The authors claim that users who received the social message were 2.08% more likely to click on the I Voted button that those you received the informational message and the users who received the social message were also 0.26% more likely to click the polling-place information link than users who received the informational message. Hence, online political mobilization can have a direct effect on political self-expression, information seeking, and real-world voting behavior. In my opinion, this paper could have been more interesting if authors would have included the information about the models and the technique used by them to derive the results.
