Analysis of the user behavior and opinion classification based on the BBS

Huang, Weitong; Yu, Zhiwu; Lu, Yongqi

doi:10.1016/j.amc.2008.01.038

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…Thus, some advanced decision tree models (e.g., algorithm C4.5; Quinlan, 1993) are usually built by first growing a large tree and then pruning it back to a reasonable size. The main advantage of Naïve Bayes is the efficient computation due to the simplicity of assumption on word independency (Huang et al, 2008). However, this assumption is not always true in practice, which might lead to a biased classification result.…”

Section: Discussionmentioning

confidence: 99%

Screening for posttraumatic stress disorder using verbal features in self narratives: A text mining approach

Veldkamp

Vries

2012

Psychiatry Research

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Section: Discussionmentioning

confidence: 99%

Screening for posttraumatic stress disorder using verbal features in self narratives: A text mining approach

Veldkamp

Vries

2012

Psychiatry Research

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“…After mining and analyzing the regular pattern of users visiting BBS, also mining the posts by ARC-BC text classification algorithm on BBS, results showed that certain users and the collective performance have the obvious similarity or difference. It indicated that this algorithm presented a good performance on BBS text classification [ 11 ]. In some ways, topic tracking is very similar to a filtering task in information retrieval [ 11 – 13 ].…”

Section: Related Workmentioning

confidence: 99%

“…It indicated that this algorithm presented a good performance on BBS text classification [ 11 ]. In some ways, topic tracking is very similar to a filtering task in information retrieval [ 11 – 13 ]. The algorithm on Internet data discovery and dissemination is relatively mature, but there are less systematic topic discovery applications.…”

Section: Related Workmentioning

confidence: 99%

Information Spread of Emergency Events: Path Searching on Social Networks

Dai

et al. 2014

The Scientific World Journal

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Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.

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“…For this case, the Douban.com discussions are similar to BBS. The opinion leaders have limited influence on others, because they can only see the viewpoints of the first few people [46]. The disappearance and change of individual viewpoint will not destroy the whole information network, which greatly avoids the vulnerability of scale-free network [47]; and (c) Ising model is good at modeling shifting and extreme viewpoints of individuals.…”

Section: Introductionmentioning

confidence: 99%

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

Lü

Zhang

2021

Complex Intell. Syst.

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Under 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.

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Analysis of the user behavior and opinion classification based on the BBS

Cited by 11 publications

References 8 publications

Screening for posttraumatic stress disorder using verbal features in self narratives: A text mining approach

Screening for posttraumatic stress disorder using verbal features in self narratives: A text mining approach

Information Spread of Emergency Events: Path Searching on Social Networks

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

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