Limits...
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.

Show MeSH
Dynamic structure of a micro-blog network when the spreading threshold varies from 0.3 to 0.5.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4644827&req=5

fig3: Dynamic structure of a micro-blog network when the spreading threshold varies from 0.3 to 0.5.

Mentions: Figure 3 shows the dynamic structure of a micro-blog network when the spreading threshold varies from 0.3 to 0.5 [15]. It can be observed that the network structure is changing with time by different connection strengths; some connections may disappear but those strong connections still exist.


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)

Dynamic structure of a micro-blog network when the spreading threshold varies from 0.3 to 0.5.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: Dynamic structure of a micro-blog network when the spreading threshold varies from 0.3 to 0.5.
Mentions: Figure 3 shows the dynamic structure of a micro-blog network when the spreading threshold varies from 0.3 to 0.5 [15]. It can be observed that the network structure is changing with time by different connection strengths; some connections may disappear but those strong connections still exist.

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