Exploring Campus Anti-Drug Activities Through Online News Reports: A Hybrid Manual and Automated Content Analysis Approach
Year: 2025
Type: article
Abstract: Illegal drug use is a major global problem that has increased significantly over the last decade. Since it is often initiated in adolescence, government programs have targeted this demographic to reduce drug use. To analyze the reach of such campaigns, the study presents a hybrid manual and automated content analysis approach to identify campus anti-drug news reports by applying keyword-based mining. The DiVoMiner automated text analysis engine was used to identify themes within reports addressing campus anti-drug news. The data sources were obtained from online news media, including newspaper media organizations, television media, Internet News, and news forwarding platforms in Taiwan between January 2019 and October 2021 ( N = 17,698). Four major themes were identified: potential risk factors associated with illegal drug use, stakeholders involved, the content and strategies of anti-drug activities, and treatment goals and strategies for drug prevention. Curiosity (33.29%) emerged as the foremost risk factor for adolescent drug use, with family members (29.77%) being the most prevalent stakeholders. Regarding anti-drug activities, the most frequently discussed content was anti-drug and campus safety campaigns (43.27%), whereas tailored programs (23.17%) represented the prevailing strategy. The primary treatment goal of drug prevention was to facilitate adolescents’ social rehabilitation (60.22%), which was frequently achieved through supportive relationships; encouraging statements (33.02%) were the most prevalent practice. This study provides insights from online news coverage of campus anti-drug activities for adolescents. It offers educators, researchers, and policymakers valuable information regarding the major themes emphasized in news narratives.
Source: SAGE Open
Authors: Ching-Hao Chang, Chiu-Mieh Huang, Kah-Yew Lim, Fen-He Lin, Kuei-Yu Huang, Jong-Long Guo
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,600
Funder: Food and Drug Administration
Award: G3065010556
OpenAlex ID: W4410715047
