Featuring: Marcel Salathé Salathé's bread and butter is infectious diseases, but his research group at Penn State has found that the disease contagion patterns can be applied to social behaviors as well. Which is good, as he thinks that in the near future, understanding social dynamics is going to be as important as understanding the spread of germs. In this session, he took us through the definition and dynamics of social contagion.
Let's say Al bought a segway, and his neighbor Phil, upon seeing Al chugging merrily along astride it (can you be astride a segway?), is impelled to go buy one for himself. Contagion. Butttt, let's say Al buys a segway, immediately goes on vacation, and while he's in Aruba, Phil buys a segway. Not contagion. In both cases, the two men are now connected, but in the former case, the connection (owning a segway) is the result of the men's similarity; in the the latter, it is cause of it.
The first case is an example of homophily, or the "birds of a feather flock together" phenomenon, and Salathe says that if you account for it, almost all contagion disappears. I'm not quite sure how this works--a cluster of points may have a smaller outside edge than the same number of scattered points, but, unless we're talking a completely isolated community, the edge is still there.
Salathé used his H1N1 Vaccine Twitter study to illustrate the dynamics of social contagion. He and his students collected all H1N1-related tweets, ran sentiment analysis on them, and used natural language processing to calculate the average sentiment core over time. They wanted to find out a) whether increased positive sentiment about the vaccine corresponded to increased vaccination rates, which it did. They also wanted to see whether Twitter users with similar sentiment are more likely to be connected (positive assortativity) than are users with different sentiments (negative assortativity), and they found that positive assortativity did increase the change of contagion, though negative assortativity did not. A few interesting Twitter assortivity findings:
- Your follower count has no affect on your tweeting positively, but does have an affect on your tweeting negatively
- If you have more negative friends, you're less likely to tweet negatively
- The more positive tweets you're exposed to, the more likely you are to tweet negatively. This last one goes against the popular "spread the cheer" viral messaging technique.