Abstract / Description of output
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.
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 language | English |
|---|---|
| Title of host publication | Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015 |
| Publisher | Association for Computational Linguistics |
| Pages | 2570-2576 |
| Number of pages | 7 |
| ISBN (Print) | 978-1-941643-32-7 |
| Publication status | Published - 2015 |