An Examination of Missing Person Social Media Engagement Through Data Mining and Experimentation: An Application of the Crisis and Emergency Risk Communication Model

Author

Cailin M. Kuchenbecker, Chapman UniversityFollow

Date of Award

Spring 5-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Health and Strategic Communication

First Advisor

Hannah Ball, Ph.D.

Second Advisor

Jennifer L. Bevan, Ph.D.

Third Advisor

Megan A. Vendemia, Ph.D.

Fourth Advisor

Timothy L. Sellnow, Ph.D.

Abstract

According to the Federal Bureau of Investigation (FBI), approximately 600,000 individuals are reported missing each year in the United States (2022). When missing person cases do not meet alert (e.g., AMBER) criteria, law enforcement often utilize social media to crowdsource information to ultimately return the missing home. Therefore, guided by the crisis and emergency risk communication model (CERC; Reynolds & Seeger, 2005) and its recently clarified propositions (Miller et al., 2021), the purpose of this dissertation was to (a) identify strategies law enforcement use to crowdsource missing person information and (b) experimentally test message characteristics that facilitate prosocial sharing of missing person posts on social media. In study one, a quantitative content analysis of 600 extracted missing person X (Twitter) posts identified that all CERC model message characteristics (i.e., timeliness, accuracy, source credibility, empathy, action-orientation, respect) were present in current law enforcement crowdsourcing posts. Additionally, a linear regression analysis indicated that timeliness, empathy, and respect predict message engagement (i.e., retweets, likes, replies) and were used to inform experimental messages in study two. In study two, participants (N = 377) who were 18 years or older and use X (Twitter) were randomly assigned one pilot tested experimental missing person message (i.e., timeliness, empathy, respect, or control). Parallel multiple mediation analyses indicated that timeliness is positively related to self-efficacy and uncertainty; empathy is positively related to self-efficacy, knowledge of risks and resources, and emotional turmoil; and respect is positively related to self-efficacy and uncertainty as well as negatively related to emotional turmoil. Additionally, self-efficacy, uncertainty, and emotional turmoil are positively related to behavioral intention whereas only self-efficacy and emotional turmoil can predict actual behavior. Finally, indirect relationships exist between timeliness and behavioral intention through self-efficacy and uncertainty; empathy and behavioral intention through self-efficacy and emotional turmoil; as well as respect and behavioral intention through self-efficacy, uncertainty, and emotional turmoil. This inquiry offers theoretical implications by being one of the first to experimentally investigate the recently clarified propositions of the CERC model. Practically, this work provides law enforcement with clear recommendations on crafting missing person messages on social media.

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Recommended Citation

Kuchenbecker, C. M. (2024). An examination of missing person social media engagement through data mining and experimentation: An application of the crisis and emergency risk communication model [Doctoral dissertation, Chapman University]. Chapman University Digital Commons. https://doi.org/10.36837/chapman.000550