#SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns

Dong-Phuong Nguyen, Tijs Adriaan van den Broek, C. Hauff, Djoerd Hiemstra, Michel Léon Ehrenhard

Abstract

We consider the task of automatically identifying participants’ motivations in the public health campaign Movember and investigate the impact of the different motivations on the amount of campaign donations raised. Our classification scheme is based on the Social Identity Model of Collective Action (van Zomeren et al., 2008). We find that automatic classification based on Movember profiles is fairly accurate, while automatic classification based on tweets is challenging. Using our classifier, we find a strong relation between types of motivations and donations. Our study is a first step towards scaling-up collective action research methods.
Original languageUndefined
Title of host publicationProceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015
Place of PublicationNew York, USA
PublisherAssociation for Computational Linguistics
Pages2570-2576
Number of pages7
ISBN (Print)978-1-941643-32-7
StatePublished - Sep 2015
EventConference on Empirical Methods in Natural Language Processing 2015 - Lisbon, Portugal

Publication series

Name
PublisherAssociation for Computational Linguistics

Conference

ConferenceConference on Empirical Methods in Natural Language Processing 2015
Abbreviated titleEMNLP 2015
CountryPortugal
CityLisbon
Period17/09/1521/09/15
Internet address

Fingerprint

Collective action
Donation
Scaling
Public health
Social identity
Research methods
Classifier

Keywords

  • EWI-26676
  • health campaignmotivationMovemberTwittercomputational linguisticscomputational social science
  • health campaign
  • Twitter
  • METIS-315151
  • Computational Linguistics
  • Motivation
  • Movember
  • IR-99122
  • computational social science

Cite this

Nguyen, D-P., van den Broek, T. A., Hauff, C., Hiemstra, D., & Ehrenhard, M. L. (2015). #SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 (pp. 2570-2576). New York, USA: Association for Computational Linguistics.

Nguyen, Dong-Phuong; van den Broek, Tijs Adriaan; Hauff, C.; Hiemstra, Djoerd; Ehrenhard, Michel Léon / #SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns.

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015. New York, USA : Association for Computational Linguistics, 2015. p. 2570-2576.

Research output: Scientific - peer-reviewConference contribution

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abstract = "We consider the task of automatically identifying participants’ motivations in the public health campaign Movember and investigate the impact of the different motivations on the amount of campaign donations raised. Our classification scheme is based on the Social Identity Model of Collective Action (van Zomeren et al., 2008). We find that automatic classification based on Movember profiles is fairly accurate, while automatic classification based on tweets is challenging. Using our classifier, we find a strong relation between types of motivations and donations. Our study is a first step towards scaling-up collective action research methods.",
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Nguyen, D-P, van den Broek, TA, Hauff, C, Hiemstra, D & Ehrenhard, ML 2015, #SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns. in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015. Association for Computational Linguistics, New York, USA, pp. 2570-2576, Conference on Empirical Methods in Natural Language Processing 2015, Lisbon, Portugal, 17-21 September.

#SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns. / Nguyen, Dong-Phuong; van den Broek, Tijs Adriaan; Hauff, C.; Hiemstra, Djoerd; Ehrenhard, Michel Léon.

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015. New York, USA : Association for Computational Linguistics, 2015. p. 2570-2576.

Research output: Scientific - peer-reviewConference contribution

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Nguyen D-P, van den Broek TA, Hauff C, Hiemstra D, Ehrenhard ML. #SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015. New York, USA: Association for Computational Linguistics. 2015. p. 2570-2576.