Predicting potential responders in social Q&A based on non-QA features

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Given the recent advancement of online social networking technologies, social question and answering has become an important venue for individuals to seek and share information. While studies have suggested the possibilities of routing questions to potential answerers for their help and the information provided, there is little analysis proposed to identify the characteristics that differentiate the possible responders from the nonresponders. In order to address such gap, in this work we present a model to predict potential responders in social Q&A using only non-QA-based attributes. We build the classifier using features from two different aspects, including: features extracted from one's social profile and style of posting. To evaluate our model, we collect over 20, 000 questions posted on Wenwo, a social Q&A application based on Weibo, along with all their responders. Our experimental results over the collected dataset demonstrate the effectiveness of responder prediction based on non-QA features and proposed potential implications for system design.

Original languageEnglish (US)
Title of host publicationCHI EA 2014
Subtitle of host publicationOne of a ChiNd - Extended Abstracts, 32nd Annual ACM Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Pages2131-2136
Number of pages6
ISBN (Print)9781450324748
DOIs
StatePublished - Jan 1 2014
Event32nd Annual ACM Conference on Human Factors in Computing Systems, CHI EA 2014 - Toronto, ON, Canada
Duration: Apr 26 2014May 1 2014

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Other

Other32nd Annual ACM Conference on Human Factors in Computing Systems, CHI EA 2014
CountryCanada
CityToronto, ON
Period4/26/145/1/14

Fingerprint

Classifiers
Systems analysis

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

Cite this

Liu, Z., & Jansen, B. J. (2014). Predicting potential responders in social Q&A based on non-QA features. In CHI EA 2014: One of a ChiNd - Extended Abstracts, 32nd Annual ACM Conference on Human Factors in Computing Systems (pp. 2131-2136). (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/2559206.2581366
Liu, Zhe ; Jansen, Bernard J. / Predicting potential responders in social Q&A based on non-QA features. CHI EA 2014: One of a ChiNd - Extended Abstracts, 32nd Annual ACM Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2014. pp. 2131-2136 (Conference on Human Factors in Computing Systems - Proceedings).
@inproceedings{e6b9f843881a4613ba4bc38119937960,
title = "Predicting potential responders in social Q&A based on non-QA features",
abstract = "Given the recent advancement of online social networking technologies, social question and answering has become an important venue for individuals to seek and share information. While studies have suggested the possibilities of routing questions to potential answerers for their help and the information provided, there is little analysis proposed to identify the characteristics that differentiate the possible responders from the nonresponders. In order to address such gap, in this work we present a model to predict potential responders in social Q&A using only non-QA-based attributes. We build the classifier using features from two different aspects, including: features extracted from one's social profile and style of posting. To evaluate our model, we collect over 20, 000 questions posted on Wenwo, a social Q&A application based on Weibo, along with all their responders. Our experimental results over the collected dataset demonstrate the effectiveness of responder prediction based on non-QA features and proposed potential implications for system design.",
author = "Zhe Liu and Jansen, {Bernard J.}",
year = "2014",
month = "1",
day = "1",
doi = "10.1145/2559206.2581366",
language = "English (US)",
isbn = "9781450324748",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
pages = "2131--2136",
booktitle = "CHI EA 2014",

}

Liu, Z & Jansen, BJ 2014, Predicting potential responders in social Q&A based on non-QA features. in CHI EA 2014: One of a ChiNd - Extended Abstracts, 32nd Annual ACM Conference on Human Factors in Computing Systems. Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, pp. 2131-2136, 32nd Annual ACM Conference on Human Factors in Computing Systems, CHI EA 2014, Toronto, ON, Canada, 4/26/14. https://doi.org/10.1145/2559206.2581366

Predicting potential responders in social Q&A based on non-QA features. / Liu, Zhe; Jansen, Bernard J.

CHI EA 2014: One of a ChiNd - Extended Abstracts, 32nd Annual ACM Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2014. p. 2131-2136 (Conference on Human Factors in Computing Systems - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Predicting potential responders in social Q&A based on non-QA features

AU - Liu, Zhe

AU - Jansen, Bernard J.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Given the recent advancement of online social networking technologies, social question and answering has become an important venue for individuals to seek and share information. While studies have suggested the possibilities of routing questions to potential answerers for their help and the information provided, there is little analysis proposed to identify the characteristics that differentiate the possible responders from the nonresponders. In order to address such gap, in this work we present a model to predict potential responders in social Q&A using only non-QA-based attributes. We build the classifier using features from two different aspects, including: features extracted from one's social profile and style of posting. To evaluate our model, we collect over 20, 000 questions posted on Wenwo, a social Q&A application based on Weibo, along with all their responders. Our experimental results over the collected dataset demonstrate the effectiveness of responder prediction based on non-QA features and proposed potential implications for system design.

AB - Given the recent advancement of online social networking technologies, social question and answering has become an important venue for individuals to seek and share information. While studies have suggested the possibilities of routing questions to potential answerers for their help and the information provided, there is little analysis proposed to identify the characteristics that differentiate the possible responders from the nonresponders. In order to address such gap, in this work we present a model to predict potential responders in social Q&A using only non-QA-based attributes. We build the classifier using features from two different aspects, including: features extracted from one's social profile and style of posting. To evaluate our model, we collect over 20, 000 questions posted on Wenwo, a social Q&A application based on Weibo, along with all their responders. Our experimental results over the collected dataset demonstrate the effectiveness of responder prediction based on non-QA features and proposed potential implications for system design.

UR - http://www.scopus.com/inward/record.url?scp=84900531833&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84900531833&partnerID=8YFLogxK

U2 - 10.1145/2559206.2581366

DO - 10.1145/2559206.2581366

M3 - Conference contribution

SN - 9781450324748

T3 - Conference on Human Factors in Computing Systems - Proceedings

SP - 2131

EP - 2136

BT - CHI EA 2014

PB - Association for Computing Machinery

ER -

Liu Z, Jansen BJ. Predicting potential responders in social Q&A based on non-QA features. In CHI EA 2014: One of a ChiNd - Extended Abstracts, 32nd Annual ACM Conference on Human Factors in Computing Systems. Association for Computing Machinery. 2014. p. 2131-2136. (Conference on Human Factors in Computing Systems - Proceedings). https://doi.org/10.1145/2559206.2581366