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Extracting and Summarizing the Commonly Faced Security Issues from Community Question Answering Site

Extracting and Summarizing the Commonly Faced Security Issues from Community Question Answering Site

Abhishek Kumar Singh, Naresh Kumar Nagwani, Sudhakar Pandey
Copyright: © 2019 |Volume: 13 |Issue: 3 |Pages: 12
ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781522564621|DOI: 10.4018/IJISP.201907010103
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MLA

Singh, Abhishek Kumar, et al. "Extracting and Summarizing the Commonly Faced Security Issues from Community Question Answering Site." IJISP vol.13, no.3 2019: pp.48-59. http://doi.org/10.4018/IJISP.201907010103

APA

Singh, A. K., Nagwani, N. K., & Pandey, S. (2019). Extracting and Summarizing the Commonly Faced Security Issues from Community Question Answering Site. International Journal of Information Security and Privacy (IJISP), 13(3), 48-59. http://doi.org/10.4018/IJISP.201907010103

Chicago

Singh, Abhishek Kumar, Naresh Kumar Nagwani, and Sudhakar Pandey. "Extracting and Summarizing the Commonly Faced Security Issues from Community Question Answering Site," International Journal of Information Security and Privacy (IJISP) 13, no.3: 48-59. http://doi.org/10.4018/IJISP.201907010103

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Abstract

Community question-answering (CQA) sites are popular as information-seeking platforms where users communicate to their peers. Security-related posts are gaining popularity with the rapid development of information technology in these sites and. CQA sites contains wide range of posts from classic cryptography to recently popular mobile security. Investigating such posts can be useful for researchers, teachers and developers. In this article, spectral clustering and frequent term-based summarization techniques are proposed for security related posts. The proposed method is developed in three stages. In the first stage, security related folksonomies are created and security post profile matrix is built with the help of tag frequency-inverse security post frequency. In the second stage, security related posts are grouped with help of spectral clustering algorithms. Finally, in the third stage, frequent terms are extracted from each cluster for security related post summarization with the help of frequent words and semantic similarity.

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