Domain-sensitive opinion leader mining from online review communities

Miao, Qingliang; Zhang, Shu; Meng, Yan; Yu, Hongfang

doi:10.1145/2487788.2487882

Cited by 13 publications

(3 citation statements)

References 3 publications

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“…To validate the effectiveness of HR, we evaluate HR using coverage, 38,39 which is commonly used in the field of key user identification, as an evaluation metric. Coverage measures the effectiveness of key user identification from the network topology formed by user interactions, by counting the number of affected users.…”

Section: Methodsmentioning

confidence: 99%

HackerRank: Identifying key hackers in underground forums

Huang

Guo

et al. 2021

International Journal of Distributed Sensor Networks

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With the rapid development of the Internet, cybersecurity situation is becoming more and more complex. At present, surface web and dark web contain numerous underground forums or markets, which play an important role in cybercrime ecosystem. Therefore, cybersecurity researchers usually focus on hacker-centered research on cybercrime, trying to find key hackers and extract credible cyber threat intelligence from them. The data scale of underground forums is tremendous and key hackers only represent a small fraction of underground forum users. It takes a lot of time as well as expertise to manually analyze key hackers. Therefore, it is necessary to propose a method or tool to automatically analyze underground forums and identify key hackers involved. In this work, we present HackerRank, an automatic method for identifying key hackers. HackerRank combines the advantages of content analysis and social network analysis. First, comprehensive evaluations and topic preferences are extracted separately using content analysis. Then, it uses an improved Topic-specific PageRank to combine the results of content analysis with social network analysis. Finally, HackerRank obtains users’ ranking, with higher-ranked users being considered as key hackers. To demonstrate the validity of proposed method, we applied HackerRank to five different underground forums separately. Compared to using social network analysis and content analysis alone, HackerRank increases the coverage rate of five underground forums by 3.14% and 16.19% on average. In addition, we performed a manual analysis of identified key hackers. The results prove that the method is effective in identifying key hackers in underground forums.

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Section: Methodsmentioning

confidence: 99%

HackerRank: Identifying key hackers in underground forums

Huang

Guo

et al. 2021

International Journal of Distributed Sensor Networks

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“…Accordingly, Miao et al (2013) proposed to identify opinion leaders in a specific domain. Observing the fact that customers post several reviews and that the reviews may belong to different domains; accordingly, in this approach, the number of reviews in the same domain was utilized to define the similarity of consumers.…”

Section: Online Opinion Data For Product Designmentioning

confidence: 99%

What makes consumers unsatisfied with your products: Review analysis at a fine-grained level

Jin

Kwong

2016

Engineering Applications of Artificial Intelligence

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“…Since opinion leaders have different expertise and they are domain sensitive [12], hence, with aim at identification of opinion leaders in Web-based stock message boards, we propose a method with two-stage processing, and it takes these deep factors into consideration. Clustering algorithm is firstly applied to the dataset which is generated by calculating the activities features from posts in message boards, and then sentiment analysis is employed to discovered the association between the opinion and the movement trend of stock price.…”

Section: Introductionmentioning

confidence: 99%

Identification of Opinion Leaders Based on User Clustering and Sentiment Analysis

Duan

Zeng

Luo

2014

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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Opinion leaders play an important role in influencing topics of discussion among a group of persons. Hence, identification of opinion leaders has receive recent attention. Specifically, discovering opinion leaders in a Web-based stock message board might be valuable for many investors. Current methods for finding opinion leaders mainly concentrate on a graph of user connections, and thus leads to large amount of computation. on the other hand, opinions in user message are usually ignored so that the effectiveness in finding opinion leaders is very limited. In the paper, a new method is proposed to recognize opinion leaders in Web-based stock message boards. We combine clustering algorithm and sentiment analysis to address the two problems in current methods. Features of user activities are calculated based on messages posted on the board, then clustering algorithm is applied to the user data and generate clusters which contain potential opinion leaders. Next, we employ sentiment analysis to candidates and associate the sentiment with the actual price movement trend. By this means, opinion leaders can be well discovered since good ability in analyzing stock market is considered as skills of Influential users. Comparative experiments on a data set which contains real discussions and stock messages are conducted and the effectiveness of the proposed method is evaluated.

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Domain-sensitive opinion leader mining from online review communities

Cited by 13 publications

References 3 publications

HackerRank: Identifying key hackers in underground forums

HackerRank: Identifying key hackers in underground forums

What makes consumers unsatisfied with your products: Review analysis at a fine-grained level

Identification of Opinion Leaders Based on User Clustering and Sentiment Analysis

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