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Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

Hu H, Mao H, Hu X, Hu F, Sun X, Jing Z, Duan Y - Comput Intell Neurosci (2015)

Bottom Line: Due to the extensive social influence, public health emergency has attracted great attention in today's society.The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy.ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation.

View Article: PubMed Central - PubMed

Affiliation: School of Management, Fudan University, Shanghai 200433, China.

ABSTRACT
Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

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Relationship diagram of interconnections and strengths among websites.
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fig6: Relationship diagram of interconnections and strengths among websites.

Mentions: (2) Data: Data Model. Data model should be built from the historical data under specific situational context in the real society. We selected mainstream media websites covering the total number ranked top 15 that were released by Chinese Internet data platform. These sites represent the vast majority of Internet users' access channel to news in China. We use these 15 web sites as nodes to generate web site correlation model by analyzing the link number of each site that pointed to other sites by weighted graph. With this model, we may track information about Internet users' personal browsing behavior affected from the strength of interconnection network sites, which will further affect netizens' emotions and cognition. Considering the information dissemination of public health emergency, we can obtain the relationship diagram as shown in Figure 6.


Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

Hu H, Mao H, Hu X, Hu F, Sun X, Jing Z, Duan Y - Comput Intell Neurosci (2015)

Relationship diagram of interconnections and strengths among websites.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4644827&req=5

fig6: Relationship diagram of interconnections and strengths among websites.
Mentions: (2) Data: Data Model. Data model should be built from the historical data under specific situational context in the real society. We selected mainstream media websites covering the total number ranked top 15 that were released by Chinese Internet data platform. These sites represent the vast majority of Internet users' access channel to news in China. We use these 15 web sites as nodes to generate web site correlation model by analyzing the link number of each site that pointed to other sites by weighted graph. With this model, we may track information about Internet users' personal browsing behavior affected from the strength of interconnection network sites, which will further affect netizens' emotions and cognition. Considering the information dissemination of public health emergency, we can obtain the relationship diagram as shown in Figure 6.

Bottom Line: Due to the extensive social influence, public health emergency has attracted great attention in today's society.The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy.ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation.

View Article: PubMed Central - PubMed

Affiliation: School of Management, Fudan University, Shanghai 200433, China.

ABSTRACT
Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

Show MeSH