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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.

SUBMITTER: Takayasu M

PROVIDER: S-EPMC4382198 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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

Takayasu Misako M Sato Kazuya K Sano Yukie Y Yamada Kenta K Miura Wataru W Takayasu Hideki H

PloS one 20150401 4

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 ...[more]

PMID: 25831122

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Project description:BackgroundIn order to the significance of lessons learned from the natural disasters for health care systems particularly in developing and under-developed countries, the main purpose of this study was to identify challenges and limitations in light of the earthquake experience in Kermanshah Province.MethodsThe present study was conducted in 2019 as a qualitative research using content analysis method. In this regard, 19 key informants were selected using snowball sampling. To enhance the accuracy of the study, the four validation criteria for qualitative studies in data coding developed by Guba and Lincoln including credibility, transferability, dependability and confirmability were used. Data was analyzed applying Graneheim and Lundman (2004) approach for analyzing the qualitative content of an interview text.ResultsAnalysis of the data led to the identification of 2 main themes, 5 sub- themes and 17 main categories. The first main theme was health system oriented challenges containing challenges of medication supply and preparation, structural challenges, challenges in crisis-scene management and challenges of service delivery and the second main theme was non-health system oriented challenges including social and psychosocial challenges.ConclusionAccording to the results, along with health system oriented challenges with the inter-sectoral or intra-sectoral nature, the non-health system oriented challenges the same as social, cultural and psychological factors can be considered as the major challenges of Iran's healthcare system in the face of crises. This complicated context can shed the light to policy makers that not only attention to the medicine and medical equipment supply chain, manpower preparation and service delivery system can be considered as an emergency, but also careful attention to the structural challenges and crisis-scene management should be planned and considered as a priority. Besides, the policy makers and the local managers should try to plan and act in a contingent situation according to the social and cultural characteristics of the region and the psychological condition and the mental needs of the people.

Project description:BackgroundTwitter is increasingly used to estimate disease prevalence, but such measurements can be biased, due to both biased sampling and inherent ambiguity of natural language.ObjectiveWe characterized the extent of these biases and how they vary with disease.MethodsWe correlated self-reported prevalence rates for 22 diseases from Experian's Simmons National Consumer Study (n=12,305) with the number of times these diseases were mentioned on Twitter during the same period (2012). We also identified and corrected for two types of bias present in Twitter data: (1) demographic variance between US Twitter users and the general US population; and (2) natural language ambiguity, which creates the possibility that mention of a disease name may not actually refer to the disease (eg, "heart attack" on Twitter often does not refer to myocardial infarction). We measured the correlation between disease prevalence and Twitter disease mentions both with and without bias correction. This allowed us to quantify each disease's overrepresentation or underrepresentation on Twitter, relative to its prevalence.ResultsOur sample included 80,680,449 tweets. Adjusting disease prevalence to correct for Twitter demographics more than doubles the correlation between Twitter disease mentions and disease prevalence in the general population (from .113 to .258, P <.001). In addition, diseases varied widely in how often mentions of their names on Twitter actually referred to the diseases, from 14.89% (3827/25,704) of instances (for stroke) to 99.92% (5044/5048) of instances (for arthritis). Applying ambiguity correction to our Twitter corpus achieves a correlation between disease mentions and prevalence of .208 ( P <.001). Simultaneously applying correction for both demographics and ambiguity more than triples the baseline correlation to .366 ( P <.001). Compared with prevalence rates, cancer appeared most overrepresented in Twitter, whereas high cholesterol appeared most underrepresented.ConclusionsTwitter is a potentially useful tool to measure public interest in and concerns about different diseases, but when comparing diseases, improvements can be made by adjusting for population demographics and word ambiguity.

Dataset Information

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

Publications

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

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