Exploring public engagement with missing person appeals on Twitter

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 widely. 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 1008 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.

KEYWORDS:

• Missing persons
• social media
• public engagement
• policing
• investigation

Acknowledgements

This work was funded by the Manchester Statistical Society Campion Grant. All code was written in R (version 3.5.1) (R Core Team 2020), using the following packages: Wickham et al. (2020a), Wickham et al. (2020b), Slowikowski (2020), Xie (2020), Spinu et al. (2020), Allaire et al. (2020), Wickham and Seidel (2019), and Wickham and Henry (2020). Code for this paper can be found on www.github.com/maczokni/misperTweetsCode. The authors would like to thank Aiden Sidebottom, Freya O’Brien, Joe Apps, Jane Hunter, Emily Moir, and Juanjo Medina for valuable comments on earlier drafts of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The anonymised data for this study are available on the Open Science Framework project set up for this study here: https://osf.io/4w5eg/.

Supplementary online material

All code for data analysis and writing of this paper is available on github at: https://github.com/maczokni/misperTweetsCode.

Notes

1 Retweet counts that are more than outside 1.5 times the interquartile range above the upper quartile.

2 A constant term in the linear predictor which is not estimated, useful for measuring rate data (retweets per exposures (days)).

3 Overdispertion is indicated by Pearson Ch2 = 143,778.85, Dispersion = 247.47, and the observed variance (101,360.73) greatly exceeding the expected variance (58.45).

4 While it is common to interpret the incident rate ratios (presented in Figure 10) as estimates of effect size between the dependent variables (in this case tweet features) and independent variable (retweets), doing so can lead to mistaken interpretations of these estimates in line with the ‘Table 2 fallacy’ (Westreich and Greenland 2013).

5 Outside 1.5 times the interquartile range above the upper quartile.

Additional information

Funding

This project was funded by the Manchester Statistical Society Campion Grant.