#SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2570–2576, Lisbon, Portugal, 17-21 September 2015. c⃝2015 Association for Computational Linguistics. #SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns Dong Nguyen1 Tijs van den Broek2 Claudia Hauff3 Djoerd Hiemstra4 Michel Ehrenhard2 1Human Media Interaction, University of Twente, d.nguyen@utwente.nl 2NIKOS, University of Twente, {t.a.vandenbroek,m.l.ehrenhard}@utwente.nl 3Web Information Systems, Delft University of Technology, c.hauff@tudelft.nl 4Database Group, University of Twente, d.hiemstra@utwente.nl Abstract We consider the task of automatically identifying participants’ motivations in the public health campaign Movember and in- vestigate the impact of the different moti- vations on the amount of campaign dona- tions raised. Our classification scheme is based on the Social Identity Model of Col- lective 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 ac- tion research methods. 1 Introduction Social media is a valuable source for studying health-related behaviors (De Choudhury, 2014). For example, Twitter was used for disease surveil- lance (Lamb et al., 2013; Aramaki et al., 2011), and was studied for its role in disseminating med- ical information (Desai et al., 2012) and organiz- ing public health campaigns (Emery et al., 2014; Wehner et al., 2014). Social media data pro- vides many opportunities to study social phenom- ena such as health campaigns, but statistics based on aggregating across social media users only pro- vide a big picture of the phenomenon. A deeper analysis of such phenomena requires fine-grained information about the involved users. Since such information is often not readily available, numer- ous studies have appeared on automatically infer- ring user characteristics (Bamman et al., 2014; Eisenstein et al., 2010; Nguyen et al., 2013). In the context of health campaigns, social sci- entists have been interested in the motivations of the participants (Cugelman et al., 2011). Knowl- edge about individual motivations helps to explain the emergence and effectiveness of collective ac- tion, such as volunteering (Bekkers and Wiepking, 2011) or mobilizing other people (van den Broek et al., 2015). The Social Identity Model of Collec- tive Action (SIMCA) (van Zomeren et al., 2008) identifies three key motivations of participants: 1) social identification with the campaign organiza- tion and community, 2) a perception of injustice about the cause, and 3) collective efficacy, the col- lective belief that the campaign can make a differ- ence. Taken together, these three motivations pre- dict the chance that an individual will participate in collective action, such as participation in an on- line health campaign. Aggregating motivations to group-level may explain the effectiveness of on- line health campaigns. Social scientists, however, have not used computational methods to measure these motivations (Johnston et al., 2009), so that their analyses are often confined to small datasets. Our study is a first step towards scaling-up collective action research methods. To do so, we …

Content truncated to 500 words