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Abstract
Social media are becoming increasingly important for communication between government organisations and citizens. Although research on this issue is expanding, the structure of these new communication patterns is still poorly understood. This study contributes to our understanding of these new communication patterns by developing an explanatory model of message diffusion on social media. Messages from 964 Dutch police force Twitter accounts are analysed using trace data drawn from the Twitter™ API to explain why certain police tweets are forwarded and others are not. Based on an iterative human calibration procedure, message topics were automatically coded based on customised lexicons. A principal component analysis of message characteristics generated four distinct patterns of use in (in)personal communication and new/versus reproduced content. Message characteristics were combined with user characteristics in a multilevel logistic general linear model. Our main results show that URLs or use of informal communication increases chances of message forwarding. In addition, contextual factors such as user characteristics impact diffusion probability. Recommendations are discussed for further research into authorship styles and their implications for social media message diffusion. For the police and other government practitioners, a list of recommendation about how to reach a larger number of citizens through social media communications is presented.
Keywords:
Notes
1. ‘User’ is used synonymously with ‘account’, as accounts managed by more than one person are still perceived under one name, as one ‘source’. This conforms to the literature on the organisational use of Twitter accounts, which takes the organisation as the user. Note that most accounts in this study are tended to by no more than one person.
2. ‘aangehouden’, ‘inbraak’, ‘onderzoek’.
3. ‘overleg’, ‘project’, ‘spreekuur’.
4. ‘campagne’, ‘acties’, ‘voorlichting’.
5. ‘verkeer’, ‘snelheid’, ‘thv’ (ter hoogte van).
6. ‘tips’, ‘voorkomen’, ‘waarschuwing’
7. ‘0900-8844’, ‘getuige?’, ‘iets gezien?’.
8. ‘vermist’, ‘vermiste’, ‘laatst gezien’.
9. ‘;-)’, ‘leuk’, ‘mooi’. For the complete wordlists used for each topic, consult Appendix 1.
10. This approach was deemed most feasible considering the amount of data (130, 000+ tweets) and vertical nature of semantic entities (all drawn from police-authored communication). The latter provides less ambiguity and thus increased reliability for simple coding.
11. Due to the rudimentary approach of this method, between 27.5% and 40.5% of the messages are not attributed to the right topic and thus default to ‘generic’ tweets.
12. These are the accounts followed by a user, contrary to following the user.
13. As an example: mentions often denote conversation (CitationHoneycutt and Herring 2009), users with a high mention average can therefore be characterised as more interaction oriented relative to those who do not use mentions. By examining such signals across multiple variables, the reliability of stylistic distinctions is increased.
14. Out of 1000 accounts listed, 36 accounts were deleted because they appeared to be deleted or shielded in the mean time, making the effective sample size 964.
15. Although not a hard requirement (CitationHarrell 2001), non-normal distributions may inflate coefficients.
16. Although our software noted false convergence, the multicolinearity check produced no problems. In addition, neither coefficients nor standard errors are visibly inflated (CitationHosmer and Lemeshow 2000), nor do iteration reports show deviance between iterations. Finally, the R packages applied is known to be stringent with estimation procedures, which often yields false positives on convergence checks (CitationBates 2009). In such cases, deviance is overestimated, making the results more conservative than optimal, but equally reliable.
17. The other category of functions includes ‘animal cops’ and ‘loverboy team cops’.
18. Both follower and friend counts were normalised to compensate for their skewed distribution (see Section 3).
19. Morning is 06:00–11:59:59, afternoon is 12:00–17:59:59, evening is 18:00–23:59:59 and night is between 0:00 and 05:59:59.
20. Other effects are department (), small talk (
), prevention (
), crime or incident (
) and traffic (
, ρ<0.05).