“What if the current context mostly promotes rather than resolves anxiety? A broader sensitivity to a rapidly changing information environment is still needed.”
I talked about a version of this paper/project at SSSP in Montreal and I’ve got a related draft paper under review – some links, including references:
The presentation slides: Rumor Studies, Post-Truth, and Fake Everything
The draft paper:
Far from Idle, but Far from Clear: Understanding the state of Rumor Studies in a hyper-subjective time (draft journal article version)
Excerpt: Some basic data on lack of overlap between stochastic rumor literature and social science rumor literature:
Rumor Contagion Models [SEC]: A Parallel Universe of Scholarship
Daley and Kendall’s “Stochastic Rumours” article (1965), appearing in Journal of the Institute of Mathematics and its Applications proposed a simulation model of rumor spreading, that used a stochastic model (DK) to mimic rumor spread. Treating the spread of rumor as similar to that of disease is partially useful to SSRS [Social Science Rumor Studies], setting some formal baselines that we have tended only to observe in retrospect. In the DK model nodes are designated as ignorant/naïve (haven’t heard the rumor), spreading (actively disseminating), and stifling (heard it but did not pass it on). The MT, or Maki-Thompson model (1973) introduces phases and can manipulate assumptions about how naïve and spreading nodes respond if the simulation is run in steps. They model variables like time lag, memory decay, intensity of belief, distribution of skepticism, and so forth. Hayes (1993) provides the simplest description of both models: spreaders lose interest in dissemination when they encounter other spreaders as it is “no fun” to continue dissemination in that direction; stiflers fail to disseminate regardless of belief status.
SSRS developed, it appears, with near complete autonomy from SEC and vice versa. I examined the 279 citations of Daley and Kendall’s original 1965 article recorded by Google Scholar as of January 9, 2017. SEC is a small scholarly world, too, much like SSRS. Only 16 citations came from social science journals, and of these 16, 7 came from either the Journal of Mathematical Sociology or Mathematical Social Science. 5 came from economics journals. I found one each in Social Science and Medicine, Social Networks, Socio-Economic Planning Sciences, and Perspectives in Urban Geography … and none of these last four, nor the 5 in economics, were about rumors, though they were about information, sentiment, or narratives. It was the 7 quantitative journals that displayed interest in DK as a part of studying simulated rumor processes.
 Now called IMA Journal of Applied Mathematics
 MT introduced the non-proximal random spreader (Hayes, 1993) mimicking the multi-nodal origin of most rumors.