Rumor Diffusion and Convergence during the 3.11 Earthquake: A Twitter Case Study

Abstract

We focus on Internet rumors and present an empirical analysis and simulation results of their diffusion and convergence during emergencies. In particular, we study one rumor that appeared in the immediate aftermath of the Great East Japan Earthquake on March 11, 2011, which later turned out to be misinformation. By investigating whole Japanese tweets that were sent one week after the quake, we show that one correction tweet, which originated from a city hall account, diffused enormously. We also demonstrate a stochastic agent-based model, which is inspired by contagion model of epidemics SIR, can reproduce observed rumor dynamics. Our model can estimate the rumor infection rate as well as the number of people who still believe in the rumor that cannot be observed directly. For applications, rumor diffusion sizes can be estimated in various scenarios by combining our model with the real data.

Exploiting Context for Rumour Detection in Social Media

“…Despite increasing interest in rumours in social media [23,26,39,28,31,40], there has been very little work in automatic rumour detection [36]. Much of what work that has been done on rumour detection [24,10,11] has been limited to finding rumours known a priori.…”

Managing social media uncertainty to support the decision making process during Emergencies

“…Misinformation can spread really fast in social media platforms (Lukasik, Cohn, & Bontcheva, 2015;Takayasu et al, 2015). This challenge impacts on the allocation of resources by first responders and citizens' decisions before, during and after emergencies.…”

“…Bring your umbrella and rain coat with you to protect your skin from the dangerous rain!! However, this was a rumour without scientific basis (Chen et al, 2015;Takayasu et al, 2015). Other examples can be found after Hurricane Sandy in October 2012, where rumours circulated on social networks about paid volunteer opportunities and reimbursements for survivors (Jacobs & Tuohy, 2012).…”

Polarized Ukraine 2014: opinion and territorial split demonstrated with the bounded confidence XY model, parametrized by Twitter data

“…Though the integration of the two approaches has been increasingly discussed [5,6], there are only few studies so far that have actually attempted to combine these [7–11]. None of them, however, studies polarization and integrates the unstructured data analysis comprehensively with the computational model.…”