Incorporate credibility into context for the best social media answers

Qi Su; Helen Kai Yun Chen; Chu-ren Huang

Incorporate credibility into context for the best social media answers

Qi Su, Helen Kai Yun Chen, Chu-ren Huang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

6 Citations (Scopus)

Abstract

In this paper, we focus on the task of identifying the best answer for a usergenerated question in Collaborative Question Answering (CQA) services. Given that most existing research on CQA has focused on non-textual features such as click-through counts which are relatively difficult to access, we examine the effectiveness of diverse content-based features for the task. Specially, we propose to explore how the information of evidentiality can contribute to the task. By the comparison of diverse textual features and their combinations, the current study provides useful insight into the issues of detecting the best answer to a given question in CQA without user features or system specific link structures.

Original language	English
Title of host publication	PACLIC 24 - Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation
Pages	535-541
Number of pages	7
Publication status	Published - 1 Dec 2010
Event	24th Pacific Asia Conference on Language, Information and Computation, PACLIC 24 - Sendai, Japan Duration: 4 Nov 2010 → 7 Nov 2010

Conference

Conference	24th Pacific Asia Conference on Language, Information and Computation, PACLIC 24
Country/Territory	Japan
City	Sendai
Period	4/11/10 → 7/11/10

Keywords

Answer assessment
Collaborative question answering
Credibility
Quality

ASJC Scopus subject areas

Language and Linguistics
Computer Science (miscellaneous)

Cite this

@inproceedings{0ed2d664e8a6486cacedd6065d52325b,

title = "Incorporate credibility into context for the best social media answers",

abstract = "In this paper, we focus on the task of identifying the best answer for a usergenerated question in Collaborative Question Answering (CQA) services. Given that most existing research on CQA has focused on non-textual features such as click-through counts which are relatively difficult to access, we examine the effectiveness of diverse content-based features for the task. Specially, we propose to explore how the information of evidentiality can contribute to the task. By the comparison of diverse textual features and their combinations, the current study provides useful insight into the issues of detecting the best answer to a given question in CQA without user features or system specific link structures.",

keywords = "Answer assessment, Collaborative question answering, Credibility, Quality",

author = "Qi Su and Chen, {Helen Kai Yun} and Chu-ren Huang",

year = "2010",

month = dec,

day = "1",

language = "English",

isbn = "9784905166009",

pages = "535--541",

booktitle = "PACLIC 24 - Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation",

note = "24th Pacific Asia Conference on Language, Information and Computation, PACLIC 24 ; Conference date: 04-11-2010 Through 07-11-2010",

}

Incorporate credibility into context for the best social media answers. / Su, Qi; Chen, Helen Kai Yun; Huang, Chu-ren.
PACLIC 24 - Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation. 2010. p. 535-541.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Incorporate credibility into context for the best social media answers

AU - Su, Qi

AU - Chen, Helen Kai Yun

AU - Huang, Chu-ren

PY - 2010/12/1

Y1 - 2010/12/1

N2 - In this paper, we focus on the task of identifying the best answer for a usergenerated question in Collaborative Question Answering (CQA) services. Given that most existing research on CQA has focused on non-textual features such as click-through counts which are relatively difficult to access, we examine the effectiveness of diverse content-based features for the task. Specially, we propose to explore how the information of evidentiality can contribute to the task. By the comparison of diverse textual features and their combinations, the current study provides useful insight into the issues of detecting the best answer to a given question in CQA without user features or system specific link structures.

AB - In this paper, we focus on the task of identifying the best answer for a usergenerated question in Collaborative Question Answering (CQA) services. Given that most existing research on CQA has focused on non-textual features such as click-through counts which are relatively difficult to access, we examine the effectiveness of diverse content-based features for the task. Specially, we propose to explore how the information of evidentiality can contribute to the task. By the comparison of diverse textual features and their combinations, the current study provides useful insight into the issues of detecting the best answer to a given question in CQA without user features or system specific link structures.

KW - Answer assessment

KW - Collaborative question answering

KW - Credibility

KW - Quality

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M3 - Conference article published in proceeding or book

SN - 9784905166009

SP - 535

EP - 541

BT - PACLIC 24 - Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

T2 - 24th Pacific Asia Conference on Language, Information and Computation, PACLIC 24

Y2 - 4 November 2010 through 7 November 2010

ER -

Incorporate credibility into context for the best social media answers

Abstract

Conference

Keywords

ASJC Scopus subject areas

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