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Exploring public engagement with missing person appeals on Twitter

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  • Solymosi, Reka
  • Petcu, Oana
  • Wilkinson, Jack

Abstract

Police agencies globally are seeing an increase in reports of people going missing. These people are often vulnerable, and their safe and early return is a key factor in preventing them from coming to serious harm. One approach to quickly find missing people is to disseminate appeals for information using social media. Yet despite the popularity of twitter-based missing person appeals, presently little is known about how to best construct these messages to ensure they are shared far and wide. This paper aims to build an evidence-base for understanding how police accounts tweet appeals for information about missing persons, and how the public engage with these tweets by sharing them. We analyse 1,008 Tweets made by Greater Manchester Police between the period of 2011 and 2018 in order to investigate what features of the tweet, the twitter account, and the missing person are associated with levels of retweeting. We find that tweets with different choice of image, wording, sentiment, and hashtags vary in how much they are retweeted. Tweets that use custody images have lower retweets than Tweets with regular photos, while tweets asking the question “have you seen...?” and asking explicitly to be retweeted have more engagement in the form of retweets. These results highlight the need for conscientious, evidence-based crafting of missing appeals, and pave the way for further research into the causal mechanisms behind what affects engagement, to develop guidance for police forces worldwide.

Suggested Citation

  • Solymosi, Reka & Petcu, Oana & Wilkinson, Jack, 2020. "Exploring public engagement with missing person appeals on Twitter," SocArXiv wugxs, Center for Open Science.
    • Handle: RePEc:osf:socarx:wugxs
    DOI: 10.31219/osf.io/wugxs
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    1. Hilbe,Joseph M., 2014. "Modeling Count Data," Cambridge Books, Cambridge University Press, number 9781107611252, September.
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    Cited by:

    1. Dong, Xuefan & Lian, Ying, 2021. "A review of social media-based public opinion analyses: Challenges and recommendations," Technology in Society, Elsevier, vol. 67(C).

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