Words on the Web: Noninvasive Detection of Emotional Contagion in Online Social Networks

Does semantic expression spread online from person to person? And if so, what kinds of expression are most likely to spread? To address these questions, we developed a nonexperimental, noninvasive method to detect and quantify contagion of semantic expression in massive online social networks, which we review and discuss here. Using only observational data, the method avoids performing emotional experiments on users of online social networks, a research practice that recently became an object of criticism and concern. Our model combines geographic aggregation and instrumental variables regression to measure the effect of an exogenous variable on an individual's expression and the influence of this change on the expression of others to whom that individual is socially connected. In a previous work, we applied our method to the emotional content of posts generated by a large sample of users over a period of three years. Those results suggest that each post expressing a positive or negative emotion can cause friends to generate one to two additional posts expressing the same emotion, and it also inhibits their use of the opposite emotion. Here, we generalize our method so it can be applied to contexts different than emotional expression and to different forms of content generated by the users of online platforms. The method allows us to determine the usage of words in the same semantic category spread, and to estimate a signed relationship between different semantic categories, showing that an increase in the usage of one category alters the usage of another category in one's social contacts. Finally, it also allows us to estimate the total cumulative effect that a person has on all of her social contacts.