Paper ID
2205
Paper Type
short
Description
Existing predictive models of online petition popularity largely overlooked the literature of agenda-setting. This study adheres to Cobb and Elder’s (1972) issue expansion model and symbolism (Birkland, 2017) in the agenda-setting literature. Examining the literature, we identified features of popular petitions and will examine the effects of these features on online petition success. Commonly used models will be used to evaluate our proposed features and to compare their performance with benchmark cases. The predictive model, i.e. the product of our study, is the combination of our proposed features and the best performing model. The contributions of the study are two-fold. This study demonstrates how we can translate the textual characteristics described by the literature of agenda-setting into technical features that are comprehensible to machines. On practical implications, a better predictive model helps activists to better utilize online platforms to secure support for their proposed policy changes.
Recommended Citation
Lee, Philip Tin Yun; LU, Alvin Ying; E, Feiyu; and Chau, Michael, "Predicting success of online petitions from the perspective of agenda setting" (2019). ICIS 2019 Proceedings. 5.
https://aisel.aisnet.org/icis2019/digital_government/digital_government/5
Predicting success of online petitions from the perspective of agenda setting
Existing predictive models of online petition popularity largely overlooked the literature of agenda-setting. This study adheres to Cobb and Elder’s (1972) issue expansion model and symbolism (Birkland, 2017) in the agenda-setting literature. Examining the literature, we identified features of popular petitions and will examine the effects of these features on online petition success. Commonly used models will be used to evaluate our proposed features and to compare their performance with benchmark cases. The predictive model, i.e. the product of our study, is the combination of our proposed features and the best performing model. The contributions of the study are two-fold. This study demonstrates how we can translate the textual characteristics described by the literature of agenda-setting into technical features that are comprehensible to machines. On practical implications, a better predictive model helps activists to better utilize online platforms to secure support for their proposed policy changes.