Details
| Original language | English |
|---|---|
| Pages (from-to) | 367-393 |
| Number of pages | 27 |
| Journal | Journal of Benefit-Cost Analysis |
| Volume | 12 |
| Issue number | 2 |
| Early online date | 10 Feb 2021 |
| Publication status | Published - Jun 2021 |
Abstract
Bystander programs contribute to crime prevention by motivating people to intervene in violent situations. Social media allow addressing very specific target groups, and provide valuable information for program evaluation. This paper provides a conceptual framework for conducting benefit-cost analysis of bystander programs and puts a particular focus on the use of social media for program dissemination and data collection. The benefit-cost model treats publicly funded programs as investment projects and calculates the benefit-cost ratio. Program benefit arises from the damages avoided by preventing violent crime. We provide systematic instructions for estimating this benefit. The explained estimation techniques draw on social media data, machine-learning technology, randomized controlled trials and discrete choice experiments. In addition, we introduce a complementary approach with benefits calculated from the public attention generated by the program. To estimate the value of public attention, the approach uses the bid landscaping method, which originates from display advertising. The presented approaches offer the tools to implement a benefit-costs analysis in practice. The growing importance of social media for the dissemination of policy programs requires new evaluation methods. By providing two such methods, this paper contributes to evidence-based decisionmaking in a growing policy area.
Keywords
- Benefit-cost analysis, Bystander programs, Conceptual framework, Discrete choice experiments, Machine learning, Social media
ASJC Scopus subject areas
- Social Sciences(all)
- Sociology and Political Science
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
- Social Sciences(all)
- Public Administration
Sustainable Development Goals
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In: Journal of Benefit-Cost Analysis, Vol. 12, No. 2, 06.2021, p. 367-393.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Benefit-Cost analysis of social media facilitated bystander programs
AU - Ebers, Axel
AU - Thomsen, Stephan L.
N1 - Funding Information: 1 We like to thank the editor, Amanda Ross, and two anonymous referees for valuable comments and suggestions. This research was conducted as part of the joint research project Security Communication via Online Social Networks – An Innovative Approach to Crime Prevention, which was funded by the German Federal Ministry of Education and Research.
PY - 2021/6
Y1 - 2021/6
N2 - Bystander programs contribute to crime prevention by motivating people to intervene in violent situations. Social media allow addressing very specific target groups, and provide valuable information for program evaluation. This paper provides a conceptual framework for conducting benefit-cost analysis of bystander programs and puts a particular focus on the use of social media for program dissemination and data collection. The benefit-cost model treats publicly funded programs as investment projects and calculates the benefit-cost ratio. Program benefit arises from the damages avoided by preventing violent crime. We provide systematic instructions for estimating this benefit. The explained estimation techniques draw on social media data, machine-learning technology, randomized controlled trials and discrete choice experiments. In addition, we introduce a complementary approach with benefits calculated from the public attention generated by the program. To estimate the value of public attention, the approach uses the bid landscaping method, which originates from display advertising. The presented approaches offer the tools to implement a benefit-costs analysis in practice. The growing importance of social media for the dissemination of policy programs requires new evaluation methods. By providing two such methods, this paper contributes to evidence-based decisionmaking in a growing policy area.
AB - Bystander programs contribute to crime prevention by motivating people to intervene in violent situations. Social media allow addressing very specific target groups, and provide valuable information for program evaluation. This paper provides a conceptual framework for conducting benefit-cost analysis of bystander programs and puts a particular focus on the use of social media for program dissemination and data collection. The benefit-cost model treats publicly funded programs as investment projects and calculates the benefit-cost ratio. Program benefit arises from the damages avoided by preventing violent crime. We provide systematic instructions for estimating this benefit. The explained estimation techniques draw on social media data, machine-learning technology, randomized controlled trials and discrete choice experiments. In addition, we introduce a complementary approach with benefits calculated from the public attention generated by the program. To estimate the value of public attention, the approach uses the bid landscaping method, which originates from display advertising. The presented approaches offer the tools to implement a benefit-costs analysis in practice. The growing importance of social media for the dissemination of policy programs requires new evaluation methods. By providing two such methods, this paper contributes to evidence-based decisionmaking in a growing policy area.
KW - Benefit-cost analysis
KW - Bystander programs
KW - Conceptual framework
KW - Discrete choice experiments
KW - Machine learning
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85111426802&partnerID=8YFLogxK
U2 - 10.1017/bca.2020.34
DO - 10.1017/bca.2020.34
M3 - Article
AN - SCOPUS:85111426802
VL - 12
SP - 367
EP - 393
JO - Journal of Benefit-Cost Analysis
JF - Journal of Benefit-Cost Analysis
SN - 2194-5888
IS - 2
ER -