Algorithms and criteria for diversification of news article comments

Giannopoulos, G, Koniaris, M, Weber, I, Jaimes, A and Sellis, T 2014, 'Algorithms and criteria for diversification of news article comments', Journal of Intelligent Information Systems, vol. 44, no. 1, pp. 1-47.


Document type: Journal Article
Collection: Journal Articles

Title Algorithms and criteria for diversification of news article comments
Author(s) Giannopoulos, G
Koniaris, M
Weber, I
Jaimes, A
Sellis, T
Year 2014
Journal name Journal of Intelligent Information Systems
Volume number 44
Issue number 1
Start page 1
End page 47
Total pages 47
Publisher Springer
Abstract In this paper, we introduce an approach for diversifying user comments on news articles. We claim that, although content diversity suffices for the keyword search setting, as proven by existing work on search result diversification, it is not enough when it comes to diversifying comments of news articles. Thus, in our proposed framework, we define comment-specific diversification criteria in order to extract the respective diversification dimensions in the form of feature vectors. These criteria involve content similarity, sentiment expressed within comments, named entities, quality of comments and combinations of them. Then, we apply diversification on comments, utilizing the extracted features vectors. The outcome of this process is a subset of the initial set that contains heterogeneous comments, representing different aspects of the news article, different sentiments expressed, different writing quality, etc. We perform an experimental analysis showing that the diversity criteria we introduce result in distinctively diverse subsets of comments, as opposed to the baseline of diversifying comments only w.r.t. to their content. We also present a prototype system that implements our diversification framework on news articles comments.
Subject Database Management
Web Technologies (excl. Web Search)
Keyword(s) similarity
Experimental analysis
Feature vectors
Features vector
DOI - identifier 10.1007/s10844-014-0328-1
Copyright notice © 2014, Springer Science+Business Media New York
ISSN 0925-9902
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