Bayesian network model of crowd emotion and negative behavior

Ramli, Nurulhuda; Ghani, Noraida Abdul; Hatta, Zulkarnain A.; Hashim, Intan Hashimah Mohd; Sulong, Jasni; Mahudin, Nor Diana Mohd; Rahman, Shukran Abdul; Saad, Zarina Mat

doi:10.1063/1.4903685

Cited by 2 publications

(4 citation statements)

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“…The existing research substantiates the signi cant role of negative emotions in shaping public risk-coping behaviors (Ramli et al, 2014;Lei and Yang, 2016;Hu et al, 2020). Moreover, it establishes that negative emotions possess predictive capabilities regarding the public's risk-coping behaviors (Ford and Feinberg, 2020).…”

Section: Introductionmentioning

confidence: 59%

“…The present research ed the impact of negative emotions on public risk-coping behaviors (Ramli et al, 2014;Lei and Yang, 2016;Hu et al, 2020;Ford and Feinberg, 2020), but fewer studies focused on the prediction of negative emotions on public risk-coping behaviors. This study veri ed that negative emotions can effectively predict public information searching behaviors and violent crime behaviors using the case of relocation policy of Beijing Municipal Government.…”

Section: Of Major Findings and Contributionsmentioning

confidence: 93%

“…When focusing on negative emotions, studies suggest their impact on risk-coping behaviors. For instance, Ramli et al (2014) found that controlling negative emotions during group gatherings reduces aggressive and dangerous behaviors. Lei and Yang (2016) proposed that stronger negative emotions correlate with higher likelihoods of choosing high-risk coping behaviors.…”

Section: Negative Emotions and Risk-coping Behaviorsmentioning

confidence: 99%

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Predicting Risk-Coping Behaviors In the Urban Relocation Process by Online Negative Emotions：A Case from Beijing

Xie,

Wu,

Zhang

2024

Preprint

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This article explores the use of online negative emotions to predict public risk-coping behaviors during urban relocation. Through a literature review, the paper proposes hypotheses that anticipate advanced prediction of public risk-coping behaviors based on online negative emotions. The study's empirical focus is on the relocation of the Beijing municipal government, utilizing Granger causality analysis on time series data. Data on online negative emotions is sourced from Sina Weibo, while risk-coping behaviors are measured through public information search behaviors and the incidence of violent crimes. The results indicated that: 1) Online negative emotions regarding the relocation policy predict public risk-coping behaviors in advance. 2) Negative comments are more effective predictors than negative feelings; 3) Negative emotions about relocation policy formulation predict risk-coping behaviors better than those related to policy effectiveness and implementation; 4) Negative emotions from individuals better predict public risk-coping behaviors than those from institutions; 5) Negative emotions from key stakeholders better predict public risk-coping behaviors than those from non-key or marginal stakeholders. This study is expected to offer valuable insights and recommendations for institutions involved in policy formulation, implementation, and evaluation.

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Section: Introductionmentioning

confidence: 59%

Section: Of Major Findings and Contributionsmentioning

confidence: 93%

Section: Negative Emotions and Risk-coping Behaviorsmentioning

confidence: 99%

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Predicting Risk-Coping Behaviors In the Urban Relocation Process by Online Negative Emotions：A Case from Beijing

Xie,

Wu,

Zhang

2024

Preprint

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“…On the other hand, it analyzed the nature of human emotions and the negative behavior resulting from overcrowding during mass events. Utilizing the Bayesian network, his model shows the dependence structure between different emotions and negative behaviors of pilgrims in the crowd [11].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Posterior Predictive Probability Happiness

Rodríguez-Hernández¹,

Domínguez-Zacarías²,

Lugo³

2016

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We propose to determine the underlying causal structure of the elements of happiness from a set of empirically obtained data based on Bayesian. We consider the proposal to study happiness as a multidimensional construct which converges four dimensions with two different Bayesian techniques, in the first we use the Bonferroni correction to estimate the mean multiple comparisons, on this basis it is that we use the function t and a z-test, in both cases the results do not vary, so it is decided to present only those shown by the t test. In the Bayesian Multiple Linear Regression, we prove that happiness can be explained through three dimensions. The technical numerical used is MCMC, of four samples. The results show that the sample has not atypical behavior too and that suitable modifications can be described through a test. Another interesting result obtained is that the predictive probability for the case of sense positive of life and personal fulfillment dimensions exhibit a non-uniform variation.

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Bayesian network model of crowd emotion and negative behavior

Cited by 2 publications

References 0 publications

Predicting Risk-Coping Behaviors In the Urban Relocation Process by Online Negative Emotions：A Case from Beijing

Predicting Risk-Coping Behaviors In the Urban Relocation Process by Online Negative Emotions：A Case from Beijing

Bayesian Posterior Predictive Probability Happiness

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