Towards detection of influential sentences affecting reputation in Wikipedia

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Abstract

Wikipedia has become the most frequently viewed online encyclopaedia website. Some sentences in Wikipedia articles have direct and obvious impact on people's opinions towards the mentioned named entities. This paper defines and tackles the problem of reputation-influential sentence detection in Wikipedia articles from various domains. We leverage multiple lexicons, to generate domain independent features. We generate topical features and word embedding features from unlabelled dataset, to boost the classification performance. We conduct several experiments, to prove the effectiveness of these features. We further adapt a two-step binary classification method, to perform multi-classification. Our evaluation results show that this method outperforms the state-of-the-art one-vs-one multi-classification method for this problem.

Item Type: Conference Item (Paper)
Subjects: Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Electronic information resource searching, Data mining
Journal or Publication Title: WebSci '16 Proceedings of the 8th ACM Conference on Web Science
Publisher: ACM
ISBN: 9781450342087
Official Date: 2016
Dates:
Date
Event
2016
Published
23 March 2016
Accepted
Page Range: pp. 244-248
DOI: 10.1145/2908131.2908177
Status: Peer Reviewed
Publication Status: Published
Date of first compliant deposit: 6 October 2016
Date of first compliant Open Access: 6 October 2016
Conference Paper Type: Paper
Title of Event: ACM Web Science Conference 2016
Type of Event: Conference
Location of Event: Hannover, Germany
Date(s) of Event: 22-25 May 2016
Related URLs:
URI: https://wrap.warwick.ac.uk/78605/

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