scholarly journals Big data-drive agent-based modeling of online polarized opinions

2021 ◽  
Author(s):  
Peng Lu ◽  
Zhuo Zhang ◽  
Mengdi Li
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Evolutionary Dynamics ◽  
Life Cycles ◽  
Agent Based Modeling ◽  
Mobile Internet ◽  
Real Case ◽  
Agent Based ◽  
Individual Behaviors ◽  
Life Cycle Process

AbstractUnder the mobile internet and big data era, more and more people are discussing and interacting online with each other. The forming process and evolutionary dynamics of public opinions online have been heavily investigated. Using agent-based modeling, we expand the Ising model to explore how individuals behave and the evolutionary mechanism of the life cycles. The big data platform of Douban.com is selected as the data source, and the online case “NeiYuanWaiFang” is applied as the real target, for our modeling and simulations to match. We run 10,000 simulations to find possible optimal solutions, and we run 10,000 times again to check the robustness and adaptability. The optimal solution simulations can reflect the whole life cycle process. In terms of different levels and indicators, the fitting or matching degrees achieve the highest levels. At the micro-level, the distributions of individual behaviors under real case and simulations are similar to each other, and they all follow normal distributions; at the middle-level, both discrete and continuous distributions of supportive and oppositive online comments are matched between real case and simulations; at the macro-level, the life cycle process (outbreak, rising, peak, and vanish) and durations can be well matched. Therefore, our model has properly seized the core mechanism of individual behaviors, and precisely simulated the evolutionary dynamics of online cases in reality.

scholarly journals Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions

2021 ◽  
Author(s):  
Gang Zhang ◽  
Hao Li ◽  
Rong He ◽  
Peng Lu
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Evolutionary Dynamics ◽  
Life Cycles ◽  
Agent Based Modeling ◽  
Macro Level ◽  
Collective Actions ◽  
Agent Based ◽  
Individual Behaviors ◽  
Micro Level

AbstractThe outbreak of COVID-19 has greatly threatened global public health and produced social problems, which includes relative online collective actions. Based on the life cycle law, focusing on the life cycle process of COVID-19 online collective actions, we carried out both macro-level analysis (big data mining) and micro-level behaviors (Agent-Based Modeling) on pandemic-related online collective actions. We collected 138 related online events with macro-level big data characteristics, and used Agent-Based Modeling to capture micro-level individual behaviors of netizens. We set two kinds of movable agents, Hots (events) and Netizens (individuals), which behave smartly and autonomously. Based on multiple simulations and parametric traversal, we obtained the optimal parameter solution. Under the optimal solutions, we repeated simulations by ten times, and took the mean values as robust outcomes. Simulation outcomes well match the real big data of life cycle trends, and validity and robustness can be achieved. According to multiple criteria (spans, peaks, ratios, and distributions), the fitness between simulations and real big data has been substantially supported. Therefore, our Agent-Based Modeling well grasps the micro-level mechanisms of real-world individuals (netizens), based on which we can predict individual behaviors of netizens and big data trends of specific online events. Based on our model, it is feasible to model, calculate, and even predict evolutionary dynamics and life cycles trends of online collective actions. It facilitates public administrations and social governance.

scholarly journals Agent-Based Modeling of Temporal and Spatial Dynamics in Life Cycle Sustainability Assessment

2017 ◽  
Vol 21 (6) ◽  
pp. 1507-1521 ◽  
Author(s):  
Susie Ruqun Wu ◽  
Xiaomeng Li ◽  
Defne Apul ◽  
Victoria Breeze ◽  
Ying Tang ◽  
...  
Keyword(s):  
Life Cycle ◽  
Sustainability Assessment ◽  
Spatial Dynamics ◽  
Agent Based Modeling ◽  
Life Cycle Sustainability Assessment ◽  
Agent Based ◽  
Temporal And Spatial

Information Security Assessment in Big Data Environment using Fuzzy Logic

2021 ◽  
pp. 29-42
Author(s):  
Kanika Sharma ◽  
◽  
◽  
◽  
Achyut Shankar ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Big Data ◽  
Life Cycle ◽  
Data Security ◽  
Fuzzy Ahp ◽  
Security Assessment ◽  
Analytic Hierarchy ◽  
Data Environment ◽  
Hierarchy Process ◽  
Life Cycle Process

In recent years, it has been observed that disclosure of information leads to the risk. Without restrict the accessibility of information providing security is difficult. So, there is a demand of time to fill the gap between security and accessibility of information. In fact, security tools should be usable for improving the security as well as the accessibility of information. Though security and accessibility are not related directly, but some of their factors indirectly affect each other. Attributes play an important role in connecting the gap among security and accessibility. In this paper, finds the main attributes of security and accessibility that impact directly and indirectly each other such as confidentiality, integrity and availability and severity. The significance of every attribute in terms of their weight is important for their effect on the overall security during the big data security life cycle process. To calculate proposed work, researchers used the Fuzzy Analytic Hierarchy Process (Fuzzy AHP).

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