File Download
There are no files associated with this item.
Supplementary
-
Citations:
- Appears in Collections:
Conference Paper: Predicting success of online petitions from the perspective of agenda setting
Title | Predicting success of online petitions from the perspective of agenda setting |
---|---|
Authors | |
Keywords | Online petitions collective actions text mining agenda setting |
Issue Date | 2019 |
Publisher | Association for Information Systems (AIS). |
Citation | Proceedings of the Fortieth International Conference on Information Systems (ICIS) 2019, Munich, Germany, 15-18 December 2019, p. Paper ID 2205 How to Cite? |
Abstract | 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. |
Description | Session Digital Government and Smart Cities - Paper ID 2205 (Short Paper) |
Persistent Identifier | http://hdl.handle.net/10722/289947 |
ISBN |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, TYP | - |
dc.contributor.author | Lu, AY | - |
dc.contributor.author | E, F | - |
dc.contributor.author | Chau, MCL | - |
dc.date.accessioned | 2020-10-22T08:19:45Z | - |
dc.date.available | 2020-10-22T08:19:45Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of the Fortieth International Conference on Information Systems (ICIS) 2019, Munich, Germany, 15-18 December 2019, p. Paper ID 2205 | - |
dc.identifier.isbn | 978-0-9966831-9-7 | - |
dc.identifier.uri | http://hdl.handle.net/10722/289947 | - |
dc.description | Session Digital Government and Smart Cities - Paper ID 2205 (Short Paper) | - |
dc.description.abstract | 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. | - |
dc.language | eng | - |
dc.publisher | Association for Information Systems (AIS). | - |
dc.relation.ispartof | Proceedings of the International Conference on Information Systems (ICIS 2019) | - |
dc.subject | Online petitions | - |
dc.subject | collective actions | - |
dc.subject | text mining | - |
dc.subject | agenda setting | - |
dc.title | Predicting success of online petitions from the perspective of agenda setting | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Chau, MCL: mchau@business.hku.hk | - |
dc.identifier.authority | Chau, MCL=rp01051 | - |
dc.identifier.hkuros | 317197 | - |
dc.identifier.spage | Paper ID 2205 | - |
dc.identifier.epage | Paper ID 2205 | - |
dc.publisher.place | Munich, Germany | - |