I don’t understand you but I trust you: using computer-aided text analysis to examine medical terminology use and engagement of vaccine online articles

ABSTRACT

Background

Public health departments recommend that medical terms should be replaced by plain language in order to make health information understandable to the general public. However, as a heuristic cue to assess information credibility, terminology makes an article appear more convincing and objective to its audience. Terminology can also make the information more engaging and persuasive. Using online articles about vaccine as an example, the present study examined whether practitioners should reduce medical terminology to lower the readability level or increase medical terminology to engage more audience.

Method

Computer-aided text analysis was used to analyze 541 pro-vaccine articles and 382 anti-vaccine articles.

Results

Anti-vaccine online articles contained significantly more medical terminology than pro-vaccine articles. Medical terminology was not significantly associated with readability. The use of medical terminology positively predicted reader engagement in both pro- and anti-vaccine articles, while the reading level of the articles negatively predicted the pro-vaccine articles’ reader engagement. Finally, pro-vaccine articles gradually reduced the use of medical terms and lowered readability from 2007 to 2017. Readability and the amount of medical terminology included in anti-vaccine articles did not significantly change over time.

Conclusions

Health practitioners are recommended not to simply reduce the use of medical terminology to lower readability levels when creating health education materials. Other recommendations for designing effective health messages and combating misinformation are provided.

KEYWORDS:

• Vaccination
• social media
• engagement
• terminology
• readability

Disclosure statement

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

Notes

1 Buzzsumo is a paid online program that collects a link’s total number of the shares, reactions, and comments on Facebook and Twitter.

Additional information

Notes on contributors

Zhan Xu

Zhan Xu is Assistant Professor at School of Communication at Northern Arizona University. Her areas of expertise include health communication, crisis and risk communication, and new technologies, with a focus on social media analytics and big data analysis utilizing computational methods. Recent publications have appeared in journals such as Health Communication, Health Education & Behavior, and Internet Research.