Abstract

Trust plays a crucial role in helping users collect reliable information in an online world, and has attracted more and more attention in research communities lately. As a conceptual counterpart of trust, distrust can be as important as trust. However, distrust is rarely studied in social media because distrust information is usually unavailable. The value of distrust has been widely recognized in social sciences and recent work shows that distrust can benefit various online applications in social media. In this work, we investigate whether we can obtain distrust information via learning when it is not directly available, and propose to study a novel problem - predicting distrust using pervasively available interaction data in an online world. In particular, we analyze interaction data, provide a principled way to mathematically incorporate interaction data in a novel framework dTrust to predict distrust information. Experimental results using real-world data show that distrust information is predictable with interaction data by the proposed framework dTrust. Further experiments are conducted to gain a deep understand on which factors contribute to the effectiveness of the proposed framework.

Original languageEnglish (US)
Title of host publicationCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Pages181-190
Number of pages10
ISBN (Print)9781450325981
DOIs
StatePublished - Nov 3 2014
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: Nov 3 2014Nov 7 2014

Other

Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
CountryChina
CityShanghai
Period11/3/1411/7/14

Fingerprint

Social sciences
Experiments
Interaction
Predictability
Distrust

Keywords

  • Balance theory
  • Distrust in social media
  • Interaction data
  • Predictability of distrust

ASJC Scopus subject areas

  • Information Systems and Management
  • Computer Science Applications
  • Information Systems

Cite this

Tang, J., Hu, X., Chang, Y., & Liu, H. (2014). Predictability of distrust with interaction data. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management (pp. 181-190). Association for Computing Machinery, Inc. https://doi.org/10.1145/2661829.2661988

Predictability of distrust with interaction data. / Tang, Jiliang; Hu, Xia; Chang, Yi; Liu, Huan.

CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, 2014. p. 181-190.

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

Tang, J, Hu, X, Chang, Y & Liu, H 2014, Predictability of distrust with interaction data. in CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, pp. 181-190, 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, 11/3/14. https://doi.org/10.1145/2661829.2661988
Tang J, Hu X, Chang Y, Liu H. Predictability of distrust with interaction data. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc. 2014. p. 181-190 https://doi.org/10.1145/2661829.2661988
Tang, Jiliang ; Hu, Xia ; Chang, Yi ; Liu, Huan. / Predictability of distrust with interaction data. CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, 2014. pp. 181-190
@inproceedings{b071af9127644cc38288769f408a3e59,
title = "Predictability of distrust with interaction data",
abstract = "Trust plays a crucial role in helping users collect reliable information in an online world, and has attracted more and more attention in research communities lately. As a conceptual counterpart of trust, distrust can be as important as trust. However, distrust is rarely studied in social media because distrust information is usually unavailable. The value of distrust has been widely recognized in social sciences and recent work shows that distrust can benefit various online applications in social media. In this work, we investigate whether we can obtain distrust information via learning when it is not directly available, and propose to study a novel problem - predicting distrust using pervasively available interaction data in an online world. In particular, we analyze interaction data, provide a principled way to mathematically incorporate interaction data in a novel framework dTrust to predict distrust information. Experimental results using real-world data show that distrust information is predictable with interaction data by the proposed framework dTrust. Further experiments are conducted to gain a deep understand on which factors contribute to the effectiveness of the proposed framework.",
keywords = "Balance theory, Distrust in social media, Interaction data, Predictability of distrust",
author = "Jiliang Tang and Xia Hu and Yi Chang and Huan Liu",
year = "2014",
month = "11",
day = "3",
doi = "10.1145/2661829.2661988",
language = "English (US)",
isbn = "9781450325981",
pages = "181--190",
booktitle = "CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management",
publisher = "Association for Computing Machinery, Inc",

}

TY - GEN

T1 - Predictability of distrust with interaction data

AU - Tang, Jiliang

AU - Hu, Xia

AU - Chang, Yi

AU - Liu, Huan

PY - 2014/11/3

Y1 - 2014/11/3

N2 - Trust plays a crucial role in helping users collect reliable information in an online world, and has attracted more and more attention in research communities lately. As a conceptual counterpart of trust, distrust can be as important as trust. However, distrust is rarely studied in social media because distrust information is usually unavailable. The value of distrust has been widely recognized in social sciences and recent work shows that distrust can benefit various online applications in social media. In this work, we investigate whether we can obtain distrust information via learning when it is not directly available, and propose to study a novel problem - predicting distrust using pervasively available interaction data in an online world. In particular, we analyze interaction data, provide a principled way to mathematically incorporate interaction data in a novel framework dTrust to predict distrust information. Experimental results using real-world data show that distrust information is predictable with interaction data by the proposed framework dTrust. Further experiments are conducted to gain a deep understand on which factors contribute to the effectiveness of the proposed framework.

AB - Trust plays a crucial role in helping users collect reliable information in an online world, and has attracted more and more attention in research communities lately. As a conceptual counterpart of trust, distrust can be as important as trust. However, distrust is rarely studied in social media because distrust information is usually unavailable. The value of distrust has been widely recognized in social sciences and recent work shows that distrust can benefit various online applications in social media. In this work, we investigate whether we can obtain distrust information via learning when it is not directly available, and propose to study a novel problem - predicting distrust using pervasively available interaction data in an online world. In particular, we analyze interaction data, provide a principled way to mathematically incorporate interaction data in a novel framework dTrust to predict distrust information. Experimental results using real-world data show that distrust information is predictable with interaction data by the proposed framework dTrust. Further experiments are conducted to gain a deep understand on which factors contribute to the effectiveness of the proposed framework.

KW - Balance theory

KW - Distrust in social media

KW - Interaction data

KW - Predictability of distrust

UR - http://www.scopus.com/inward/record.url?scp=84937553495&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84937553495&partnerID=8YFLogxK

U2 - 10.1145/2661829.2661988

DO - 10.1145/2661829.2661988

M3 - Conference contribution

AN - SCOPUS:84937553495

SN - 9781450325981

SP - 181

EP - 190

BT - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

PB - Association for Computing Machinery, Inc

ER -