NDLI: Detecting multiple userids on Korean social media for mining TV audience response

Content Provider	IEEE Xplore Digital Library
Author	Kyounghun Kim Yunseok Noh Seong-Bae Park
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Kyungpook Nat. Univ., Daegu, South Korea (Kyounghun Kim; Yunseok Noh; Seong-Bae Park)
Abstract	Possession of multiple userids by a single user happens when more than two userids actually belong to the same user. In analysis of audience response of TV program, it is so important to detect these multi-id users because they often use the multiple ids to manipulate audience response or to take illegal profits. Detecting multiple userids of a single user has similiar nature with authorship attribution in terms of identifying authorships for given arbitrary texts. The conventional supervised techniques for authorship attribution, however, are difficult to be employed directly to the problem of multiple userids detection. This is because we do not know real authors and multiple userids may belong to the same author in the task of multiple userids detection. In addition, since we can not have all authors in advance, userids can not be treated as classes. This paper proposes a method of learning the element-wise differences between multiple userids. Each userid is represented as a feature vector from their postings on web social media. Then the similarity vector between two userid vectors can be obtained by performing their element-wise difference. With the similarity vectors, we train the similarity patterns for detecting if multiple userids belong to the same user or not. In order to solve the problem successfully, we present six features which are effective for Korean social media. We conducted comprehensive experiments on the Korean social media dataset. The experimental results show that the proposed similarity learning method with all presented features is successful for detecting multiple userids on Korean social media.
Starting Page	1
Ending Page	4
File Size	186017
Page Count	4
File Format	PDF
ISBN	9781479986392
ISSN	21593450
e-ISBN	9781479986415
DOI	10.1109/TENCON.2015.7373121
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Media Writing Training TV Feature extraction Learning systems Probabilistic logic
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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