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Rumor diffusion and convergence during the 3.11 earthquake: a twitter case study.

Takayasu M, Sato K, Sano Y, Yamada K, Miura W, Takayasu H - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan.

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.

No MeSH data available.


Related in: MedlinePlus

Cumulative distribution of number of tweets per user during one week after the quake x in a semi-log plot.Red dashed lines is the stretched exponential function introduced in Equation (15). (Inset) An enlarged part of the same figure in x ∈ [1, 200]. The most tweeted user is known as a “bot” who tweeted 15,635 times during the week. The bot is a kind of robot that automatically tweets information about the missing person’s safety with the hash tag “#pf_anpi.” The median value of tweets per user during the week is seven, that is, one tweet per day.
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pone.0121443.g008: Cumulative distribution of number of tweets per user during one week after the quake x in a semi-log plot.Red dashed lines is the stretched exponential function introduced in Equation (15). (Inset) An enlarged part of the same figure in x ∈ [1, 200]. The most tweeted user is known as a “bot” who tweeted 15,635 times during the week. The bot is a kind of robot that automatically tweets information about the missing person’s safety with the hash tag “#pf_anpi.” The median value of tweets per user during the week is seven, that is, one tweet per day.

Mentions: We analyzed all Japanese tweets from 09:00, March 11, 2011, to 09:00, March 17, 2011. In total, these included 179,286,297 tweets from 3,691,599 users (Fig. 7). In Fig. 8, cumulative distribution of tweets per users is plotted in semi-log scale. The median of the tweets during this week is seven tweets per user while the mean is 48. The most tweeted users are “bots” that automatically generate tweets about missing people. The bots repeated their announcement and tweeted more than 150 thousand times during the week. Here, we found that a stretched exponential function is well-fitted as follows.P(≥x)=exp-λ(x-1)γτ(15)where λ = 1.0, γ = 0.39, and τ = 2.9 that are estimated in the range x ∈ [1, 2800] by Levenberg-Marquardt algorithm [33].


Rumor diffusion and convergence during the 3.11 earthquake: a twitter case study.

Takayasu M, Sato K, Sano Y, Yamada K, Miura W, Takayasu H - PLoS ONE (2015)

Cumulative distribution of number of tweets per user during one week after the quake x in a semi-log plot.Red dashed lines is the stretched exponential function introduced in Equation (15). (Inset) An enlarged part of the same figure in x ∈ [1, 200]. The most tweeted user is known as a “bot” who tweeted 15,635 times during the week. The bot is a kind of robot that automatically tweets information about the missing person’s safety with the hash tag “#pf_anpi.” The median value of tweets per user during the week is seven, that is, one tweet per day.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4382198&req=5

pone.0121443.g008: Cumulative distribution of number of tweets per user during one week after the quake x in a semi-log plot.Red dashed lines is the stretched exponential function introduced in Equation (15). (Inset) An enlarged part of the same figure in x ∈ [1, 200]. The most tweeted user is known as a “bot” who tweeted 15,635 times during the week. The bot is a kind of robot that automatically tweets information about the missing person’s safety with the hash tag “#pf_anpi.” The median value of tweets per user during the week is seven, that is, one tweet per day.
Mentions: We analyzed all Japanese tweets from 09:00, March 11, 2011, to 09:00, March 17, 2011. In total, these included 179,286,297 tweets from 3,691,599 users (Fig. 7). In Fig. 8, cumulative distribution of tweets per users is plotted in semi-log scale. The median of the tweets during this week is seven tweets per user while the mean is 48. The most tweeted users are “bots” that automatically generate tweets about missing people. The bots repeated their announcement and tweeted more than 150 thousand times during the week. Here, we found that a stretched exponential function is well-fitted as follows.P(≥x)=exp-λ(x-1)γτ(15)where λ = 1.0, γ = 0.39, and τ = 2.9 that are estimated in the range x ∈ [1, 2800] by Levenberg-Marquardt algorithm [33].

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan.

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.

No MeSH data available.


Related in: MedlinePlus