#SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)
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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 (ACL)
Number of pages7
ISBN (Print)978-1-941643-32-7
Publication statusPublished - Sept 2015
EventConference on Empirical Methods in Natural Language Processing 2015 - Lisbon, Portugal
Duration: 17 Sept 201521 Sept 2015

Publication series

PublisherAssociation for Computational Linguistics


ConferenceConference on Empirical Methods in Natural Language Processing 2015
Abbreviated titleEMNLP 2015
Internet address


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

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