#SSSP17 – New Work on Rumor Theory

“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.[1] 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.[2] 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.

[1] Now called IMA Journal of Applied Mathematics

[2] MT introduced the non-proximal random spreader (Hayes, 1993) mimicking the multi-nodal origin of most rumors.

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