Mining online social data for detecting social network mental disorders

Hong Han Shuai, Chih Ya Shen, De Nian Yang, Yi Feng Lan, Wang Chien Lee, Philip S. Yu, Ming Syan Chen

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

14 Citations (Scopus)

Abstract

An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental factors considered in standard diagnostic criteria (questionnaire) cannot be observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMDbased Tensor Model (STM) to improve the performance. Our framework is evaluated via a user study with 3126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results show that SNMDD is promising for identifying online social network users with potential SNMDs.

Original languageEnglish (US)
Title of host publication25th International World Wide Web Conference, WWW 2016
PublisherInternational World Wide Web Conferences Steering Committee
Pages275-285
Number of pages11
ISBN (Electronic)9781450341431
DOIs
StatePublished - Jan 1 2016
Event25th International World Wide Web Conference, WWW 2016 - Montreal, Canada
Duration: Apr 11 2016Apr 15 2016

Publication series

Name25th International World Wide Web Conference, WWW 2016

Other

Other25th International World Wide Web Conference, WWW 2016
CountryCanada
CityMontreal
Period4/11/164/15/16

Fingerprint

Tensors
Learning systems

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Cite this

Shuai, H. H., Shen, C. Y., Yang, D. N., Lan, Y. F., Lee, W. C., Yu, P. S., & Chen, M. S. (2016). Mining online social data for detecting social network mental disorders. In 25th International World Wide Web Conference, WWW 2016 (pp. 275-285). (25th International World Wide Web Conference, WWW 2016). International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/2872427.2882996
Shuai, Hong Han ; Shen, Chih Ya ; Yang, De Nian ; Lan, Yi Feng ; Lee, Wang Chien ; Yu, Philip S. ; Chen, Ming Syan. / Mining online social data for detecting social network mental disorders. 25th International World Wide Web Conference, WWW 2016. International World Wide Web Conferences Steering Committee, 2016. pp. 275-285 (25th International World Wide Web Conference, WWW 2016).
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title = "Mining online social data for detecting social network mental disorders",
abstract = "An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental factors considered in standard diagnostic criteria (questionnaire) cannot be observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMDbased Tensor Model (STM) to improve the performance. Our framework is evaluated via a user study with 3126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results show that SNMDD is promising for identifying online social network users with potential SNMDs.",
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Shuai, HH, Shen, CY, Yang, DN, Lan, YF, Lee, WC, Yu, PS & Chen, MS 2016, Mining online social data for detecting social network mental disorders. in 25th International World Wide Web Conference, WWW 2016. 25th International World Wide Web Conference, WWW 2016, International World Wide Web Conferences Steering Committee, pp. 275-285, 25th International World Wide Web Conference, WWW 2016, Montreal, Canada, 4/11/16. https://doi.org/10.1145/2872427.2882996

Mining online social data for detecting social network mental disorders. / Shuai, Hong Han; Shen, Chih Ya; Yang, De Nian; Lan, Yi Feng; Lee, Wang Chien; Yu, Philip S.; Chen, Ming Syan.

25th International World Wide Web Conference, WWW 2016. International World Wide Web Conferences Steering Committee, 2016. p. 275-285 (25th International World Wide Web Conference, WWW 2016).

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

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Shuai HH, Shen CY, Yang DN, Lan YF, Lee WC, Yu PS et al. Mining online social data for detecting social network mental disorders. In 25th International World Wide Web Conference, WWW 2016. International World Wide Web Conferences Steering Committee. 2016. p. 275-285. (25th International World Wide Web Conference, WWW 2016). https://doi.org/10.1145/2872427.2882996