Title |
Rumor Diffusion and Convergence during the 3.11 Earthquake: A Twitter Case Study
|
---|---|
Published in |
PLOS ONE, April 2015
|
DOI | 10.1371/journal.pone.0121443 |
Pubmed ID | |
Authors |
Misako Takayasu, Kazuya Sato, Yukie Sano, Kenta Yamada, Wataru Miura, Hideki Takayasu |
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. |
X Demographics
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Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 6 | 25% |
United States | 5 | 21% |
Hong Kong | 1 | 4% |
India | 1 | 4% |
Canada | 1 | 4% |
Unknown | 10 | 42% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 13 | 54% |
Scientists | 8 | 33% |
Practitioners (doctors, other healthcare professionals) | 2 | 8% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 1% |
Indonesia | 1 | <1% |
Unknown | 136 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 32 | 23% |
Student > Master | 25 | 18% |
Researcher | 11 | 8% |
Student > Bachelor | 8 | 6% |
Professor | 6 | 4% |
Other | 24 | 17% |
Unknown | 33 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Social Sciences | 22 | 16% |
Computer Science | 21 | 15% |
Engineering | 11 | 8% |
Medicine and Dentistry | 9 | 6% |
Psychology | 8 | 6% |
Other | 32 | 23% |
Unknown | 36 | 26% |