TY - GEN
T1 - Understanding interactions between municipal police departments and the public on twitter
AU - Huang, Yun
AU - Wu, Qunfang
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Law enforcement agencies have started using social media for building community policing, i.e., establishing collaborations between the people in a community and local police departments. Both researchers and practitioners need to understand how the two parties interact on social media on a daily basis, such that effective strategies or tools can be developed for the agencies to better leverage the platforms to fulfill their missions. In this paper, we collected 9,837 tweets from 16 municipal police department official Twitter accounts within 6 months in 2015 and annotated them into different strategies and topics. We further examined the association between tweet features (e.g., hashtags, mentions, content) and user interactions (favorites and retweets) by using regression models. The models reveal surprising findings, e.g., that the number of mentions has a negative correlation with favorites. Our findings provide insights into how to improve interactions between the two parties.
AB - Law enforcement agencies have started using social media for building community policing, i.e., establishing collaborations between the people in a community and local police departments. Both researchers and practitioners need to understand how the two parties interact on social media on a daily basis, such that effective strategies or tools can be developed for the agencies to better leverage the platforms to fulfill their missions. In this paper, we collected 9,837 tweets from 16 municipal police department official Twitter accounts within 6 months in 2015 and annotated them into different strategies and topics. We further examined the association between tweet features (e.g., hashtags, mentions, content) and user interactions (favorites and retweets) by using regression models. The models reveal surprising findings, e.g., that the number of mentions has a negative correlation with favorites. Our findings provide insights into how to improve interactions between the two parties.
UR - http://www.scopus.com/inward/record.url?scp=85044401948&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-78105-1_5
DO - 10.1007/978-3-319-78105-1_5
M3 - Conference contribution
AN - SCOPUS:85044401948
SN - 9783319781044
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 37
EP - 46
BT - Transforming Digital Worlds - 13th International Conference, iConference 2018, Proceedings
A2 - Chowdhury, Gobinda
A2 - McLeod, Julie
A2 - Gillet, Val
A2 - Willett, Peter
PB - Springer Verlag
T2 - 13th International Conference on Transforming Digital Worlds, iConference 2018
Y2 - 25 March 2018 through 28 March 2018
ER -