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Exploring Campus Anti-Drug Activities through Online News Reports: A Hybrid Manual and Automated Content Analysis Approach

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Title: Exploring Campus Anti-Drug Activities through Online News Reports: A Hybrid Manual and Automated Content Analysis Approach
Language: English
Authors: Ching-Hao Chang (ORCID 0009-0005-3932-5026), Chiu-Mieh Huang (ORCID 0000-0001-9143-9972), Kah-Yew Lim (ORCID 0009-0008-1083-5913), Fen-He Lin (ORCID 0000-0002-7300-4622), Kuei-Yu Huang (ORCID 0000-0002-6296-0061), Jong-Long Guo (ORCID 0000-0002-3120-014X)
Source: SAGE Open. 2025 15(2).
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 17
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: News Reporting, Prevention, Risk, Drug Use, Drug Abuse, Correlation, Stakeholders, Information Retrieval, Drug Education, Content Analysis, Internet, Television, Drug Rehabilitation, Adolescents, Safety, Public Health, Communications, Foreign Countries
Geographic Terms: Taiwan
DOI: 10.1177/21582440251334852
ISSN: 2158-2440
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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1477131
Database: ERIC
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  Value: <anid>AN0186372412;[kbz6]01apr.25;2025Jul07.02:57;v2.2.500</anid> <title id="AN0186372412-1">Exploring Campus Anti-Drug Activities Through Online News Reports: A Hybrid Manual and Automated Content Analysis Approach </title> <p>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.</p> <p>Plain language summary: Exploring campus anti-drug activities through online news reports: an automated content analysis Illegal drug use is a major global problem that has increased significantly over the last decade. To better understand government efforts to reduce drug use among young people, this study used a combination of manual and automated content analysis techniques to identify news reports related to campus anti-drug activities. The researchers focused on campus anti-drug news published by Taiwanese online media organizations between January 2019 and October 2021, totaling 17,698 articles. Using the computer-assisted platform, DiVoMiner, this study effectively organizes and categorizes extensive textual data and identifies four major themes within these news reports. This study sheds light on the major themes highlighted in online news coverage of campus anti-drug activities aimed at adolescents. Its findings provide valuable insights for educators, researchers, and policymakers regarding the major themes emphasized in news narratives, helping inform future strategies to address the issue of drug use among adolescents.</p> <p>Keywords: students; adolescents; campus anti-drug; online news; automated content analysis; text mining</p> <hd id="AN0186372412-2">Introduction</hd> <p>The detrimental consequences of illegal drug use and its profound impact on health have placed a substantial burden on public health ([<reflink idref="bib38" id="ref1">38</reflink>]; [<reflink idref="bib42" id="ref2">42</reflink>]; [<reflink idref="bib45" id="ref3">45</reflink>]). According to the World Drug Report published by the United Nations Office on Drugs and Crime, approximately 275 million people worldwide used illegal drugs in 2020, a notable increase of 22% from 2010. Furthermore, an estimated 38.6 million individuals were afflicted with drug use disorders in 2020 ([<reflink idref="bib75" id="ref4">75</reflink>]). The use of illegal drugs adversely affects physical and mental health ([<reflink idref="bib3" id="ref5">3</reflink>]; [<reflink idref="bib59" id="ref6">59</reflink>]) and carries a heightened risk of lethal outcomes ([<reflink idref="bib59" id="ref7">59</reflink>]; [<reflink idref="bib76" id="ref8">76</reflink>]). It is closely associated with increased incidents of accident injury ([<reflink idref="bib30" id="ref9">30</reflink>]; [<reflink idref="bib59" id="ref10">59</reflink>]), theft, violence ([<reflink idref="bib21" id="ref11">21</reflink>]; [<reflink idref="bib31" id="ref12">31</reflink>]), and suicide ([<reflink idref="bib3" id="ref13">3</reflink>]; [<reflink idref="bib35" id="ref14">35</reflink>]).</p> <p>Owing to public awareness of and concern about the abovementioned harms, government policy has long focused on addressing campus drug use practices, recognizing that drug use often occur during adolescence ([<reflink idref="bib46" id="ref15">46</reflink>]; [<reflink idref="bib84" id="ref16">84</reflink>]). To effectively control adolescent use of illegal drugs, Taiwan's Ministry of Education and its affiliated agencies offer subsidies for school-based anti-drug programs. These initiatives incentivize schools to create and implement diverse intervention programs and provide educational and advocacy activities on campus. They aim to improve students' anti-drug awareness, use prevention, and treatment. Moreover, these activities seek to enhance public awareness on the harm of illegal drugs, in addition to the anti-drug strategies and methods available on campus.</p> <p>Information on government-endorsed programs and activities for reducing the harm of illegal drug use on campuses is frequently disseminated through various news media. Pervasive and intense persuaders, the news media affect public perceptions, beliefs, and attitudes ([<reflink idref="bib8" id="ref17">8</reflink>]). Their ability to influence public opinion is particularly crucial in issues and policies such as those related drug use ([<reflink idref="bib13" id="ref18">13</reflink>]). Furthermore, the media significantly influences the public opinion regarding drugs and drug users ([<reflink idref="bib27" id="ref19">27</reflink>]). In today's environment, mass media plays a vital role, providing a unified platform for all public health communication, health education guidelines, and for retaining social ties while following social distancing measures ([<reflink idref="bib2" id="ref20">2</reflink>]). The growing number of online news reports on this topic provides ample daily information on illegal drug use, allowing researchers to use such content to analyze program efficacy, reach, and related concerns.</p> <p>Text mining is an effective and efficient method for processing massive amounts of data. It is an application of data technology whose primary purpose is to determine the implied meaning in a text and obtain potentially practical and understandable knowledge from it. Text mining analysis includes word segmentation, word frequency calculation, and keyword extraction ([<reflink idref="bib1" id="ref21">1</reflink>]). Research that used text mining methods on topics related to drug use has primarily focused on two areas: investigating research trends ([<reflink idref="bib15" id="ref22">15</reflink>]; [<reflink idref="bib62" id="ref23">62</reflink>]; [<reflink idref="bib67" id="ref24">67</reflink>]; [<reflink idref="bib80" id="ref25">80</reflink>]), and exploring and analyzing web forum data concerning illegal drug use ([<reflink idref="bib7" id="ref26">7</reflink>]; [<reflink idref="bib37" id="ref27">37</reflink>]; [<reflink idref="bib51" id="ref28">51</reflink>]). One particular text mining investigation scrutinized English-language discussions about Synthetic Cannabinoid Receptor Agonists (SCRA) across three drug-focused online forums. This study uncovered a growing trend in mentioning the adverse effects associated with SCRA in forum discussions. ([<reflink idref="bib37" id="ref29">37</reflink>]). Another study extracted insights from social media content to discern public concerns surrounding the opioid crisis, highlighting the most prevalent opioids related topics on social platforms ([<reflink idref="bib51" id="ref30">51</reflink>]). Most of the aforementioned text mining studies predominantly sourced their textual data from forums or social media, few utilized online news reports. Additionally, the focus was not specifically on the adolescent population. Past research has primarily explored the perspectives of the public on the use of illegal drugs, analysis of how adolescent drug use or campus drug use prevention activities are represented in news reports is lacking. Therefore, this study intends to explore adolescent drug use and campus drug use prevention in the context of online news reports.</p> <hd id="AN0186372412-3">Literature Review</hd> <p>Adolescent drug use increases the likelihood of encountering mental, physical, and social challenges later in life. Young people who engage in recreational drug consumption are susceptible to mental health problems such as anxiety, concentration difficulties, aggression, hallucinations, and mental stress ([<reflink idref="bib57" id="ref31">57</reflink>]; [<reflink idref="bib63" id="ref32">63</reflink>]). Moreover, drug use adversely impacts the cognitive functions of memory, learning, impulse control, and stress management ([<reflink idref="bib70" id="ref33">70</reflink>]). During adolescence, the prefrontal cortex is not fully developed and, under the influence of drugs, is prone to misjudgment, impulsiveness, emotional instability, and behavioral issues ([<reflink idref="bib66" id="ref34">66</reflink>]). The U.S. Centers for Disease Control and Prevention reported that in 2019, approximately 15% of high school students used certain illegal drugs ([<reflink idref="bib10" id="ref35">10</reflink>]). The National Institute on Drug Abuse's ("Monitoring the Future") long-term surveillance of illegal drug use among adolescents revealed that reported substance use decreased dramatically between 2020 and 2021, after the onset of the COVID-19 pandemic and related changes like school closures and social distancing. In 2023, 10.9% of 8th, 19.8% of 10th, and 31.2% of 12th graders reported using illegal drugs in the past year ([<reflink idref="bib55" id="ref36">55</reflink>]). These results indicate that the prevalence of illegal drug use increases with age. In Taiwan in 2017, a national campus survey found that drug use among high school students was prevalent in 0.23% of juniors and 0.73% of seniors ([<reflink idref="bib26" id="ref37">26</reflink>]). Another study in Taipei found that the lifetime prevalence of illegal drug use in truant adolescents was 15.0% to 17.9%, with a corresponding estimate of 3.1% to 3.4% for adolescents without truancy ([<reflink idref="bib16" id="ref38">16</reflink>]).</p> <p>Studies have demonstrated that peers and parents have the greatest influence on adolescents' substance use behavior ([<reflink idref="bib18" id="ref39">18</reflink>]; [<reflink idref="bib73" id="ref40">73</reflink>]). Furthermore, the youth may be more vulnerable to drug use during the summer months, considering their increased free time or participation in social activities that could promote drug exposure ([<reflink idref="bib54" id="ref41">54</reflink>]). A study analyzing the description of substance use in online news pointed out that legal-criminal aspects were the most frequent themes; nearly 60% of reports had pessimistic headlines and portrayed drug use or drug users negatively, and a quarter of articles used stigmatizing language ([<reflink idref="bib24" id="ref42">24</reflink>]).</p> <p>Negative news, such as accidents, violence, and self-harm, tends to leave a more lasting impression on people regarding adolescent drug use. However, positive news reports in this context also exist. It is possible to explore the use of language in both positive and negative news reports through text mining techniques. Understanding how adolescent drug use is presented in news reports can help comprehend how the media influences public perceptions of youth drug use.</p> <p>The earlier the onset of illegal drug use, the higher the susceptibility to addiction and subsequent health detriments; therefore, failing to undertake early detection and intervention can compound the challenges of rehabilitation. In addition to ensuring a safe and healthy learning environment, numerous countries have formulated policies and intervention programs for drug prevention in schools. For example, drug education has been widely implemented in schools and disseminated through the media in the United States, with Drug Abuse Resistance Education (DARE) and Project ALERT being the most commonly used drug prevention education programs ([<reflink idref="bib58" id="ref43">58</reflink>]). Similarly, "Unplugged" is a European school-based prevention program that comprises 12 units taught by teachers to adolescents aged 12 to 14. The program has proven to be effective in reducing smoking, alcohol abuse, and short-term marijuana use in the European Drug Addiction Prevention trial ([<reflink idref="bib78" id="ref44">78</reflink>]). A cluster randomized controlled trial on a Brazil school-based drug prevention program held over 12 classes called # Tamojunto 2.0 found that the program can reduce alcohol initiation among adolescents ([<reflink idref="bib60" id="ref45">60</reflink>]). Drug prevention policy and campus programs may be disseminated to the public through online news reports, revealing information about the government's efforts to combat drug use on campuses and achieving a promotional effect by making the public aware of these initiatives.</p> <hd id="AN0186372412-4">The Goal of This Study</hd> <p>Focusing on campus anti-drug news reports, this study aims to mine the relationship between such textual features using massive textual data analytics. Analyzing online news through text mining will enrich our understanding of the associated campus anti-drug practices. This study presents a hybrid manual and automated content analysis method ([<reflink idref="bib11" id="ref46">11</reflink>]) designed to identify illegal campus drug prevention practices by applying keyword-based mining.</p> <hd id="AN0186372412-5">Methods</hd> <p></p> <hd id="AN0186372412-6">Data Source</hd> <p>The data used for this study were online news media coverage of anti-drug activities on campus from January 2019 to October 2021, obtained from online news websites of hundreds of Taiwanese news media, including newspaper media (e.g., Liberty Times, China Times, and United Daily News), television media (e.g., TVBS, SET News, Formosa News, and EBC News), Internet News (e.g., ETtoday, Nownews, and Newtalk) and news forwarding platforms (e.g., Yahoo, Line, and PChome). The uMax Data database collects all news from the aforementioned news platforms daily and selects relevant news from the database using keywords based on the study objective.</p> <p>A key step in data retrieval was identifying keywords related to campus and drug use. Our research team, consisting of drug use experts and data mining engineers, followed the process outlined by [<reflink idref="bib29" id="ref47">29</reflink>] to generate a list of relevant keyword ([<reflink idref="bib29" id="ref48">29</reflink>]). First, drug use experts provided an initial list of seed keywords (e.g., "drug prevention,""anti-drug"). Using these, we employed an automated platform to retrieve news reports. We then reviewed and added any unclear or missing terms from the results to the seed list. Afterward, domain experts refined the list by removing irrelevant keywords. The same process was applied with seed terms like "campus,""Ministry of Education," and "Bureau of Education" to generate campus-related keywords. The final keyword queries are presented in Table 1.</p> <p>Table 1. Keyword Queries.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="left" /></colgroup><thead><tr><th align="left">Group</th><th align="left">Keywords</th></tr></thead><tbody><tr><td>Group 1 (drug-related terms)</td><td>Drug, anti-drug, drug awareness, drug rejection, laughing gas, illegal drug, drug use, drug abuse, drug prevention, drug use prevention, and drug abuse prevention</td></tr><tr><td>Group 2 (campus-related terms)</td><td>Campus, on-campus, Ministry of Education, Education Administration, K-12 Education Administration, Bureau of Education, Education Department, off-campus organizations, and off-campus guidance and counseling institutes</td></tr></tbody></table> </ephtml> </p> <p>Team members manually reviewed and refined the seed keywords to enhance accuracy. News reports were collected based on the inclusion and exclusion criteria outlined in Table 2. If a news headline contained both "drug use-related" and "campus-related" keywords, the article was directly included. However, if the body text of the news contained these keywords, a manual re-evaluation was conducted. For instance, some news reports mentioned government officials visiting campuses for anti-drug activities, but the actual content pertained to COVID-19 prevention and vaccination efforts. Additionally, there are cases where news reports address computer antivirus efforts rather than drug prevention – due to the identical Chinese terms for "anti-drug" (反毒) and "antivirus" (反毒) – were noted. In total, 26,541 relevant news reports were collected, followed by data cleaning.</p> <p>Table 2. Examples of Inclusion and Exclusion Criteria for the Manual Verification.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="left" /><col align="left" /><col align="left" /></colgroup><thead><tr><th align="left">Area</th><th align="left">Type</th><th align="left">ID</th><th align="left">Criteria</th></tr></thead><tbody><tr><td rowspan="4">Drug use</td><td rowspan="2">Inclusion</td><td>I1</td><td>Headlines contain related seed keywords</td></tr><tr><td>I2</td><td>Body text contains related seed keywords</td></tr><tr><td rowspan="2">Exclusion</td><td>E1</td><td>Not a news report</td></tr><tr><td>E2</td><td>No campus-related keywords were mentioned</td></tr><tr><td rowspan="4">Campus</td><td rowspan="2">Inclusion</td><td>I1</td><td>Headlines contain related seed keywords</td></tr><tr><td>I2</td><td>Body text contains related seed keywords</td></tr><tr><td rowspan="2">Exclusion</td><td>E1</td><td>Not a news report</td></tr><tr><td>E3</td><td>No drug-related keywords were mentioned</td></tr></tbody></table> </ephtml> </p> <hd id="AN0186372412-7">Analysis Tools and Process</hd> <p>Automated content analysis was conducted using the DiVoMiner platform (uMax Data Technology CO, LTD., Taiwan), an online tool for content analysis and data mining. DiVoMiner employs machine learning coding for content analysis, utilizing a word segmentation algorithm trained on a comprehensive corpus of millions of industry-related words. It integrates a unique 10-year data set from China, Hong Kong, Macau, and Taiwan, using the Hidden Markov Model (HMM) for Chinese word segmentation ([<reflink idref="bib87" id="ref49">87</reflink>]). The HMM-Ngram segmentation model was optimized with N-shortest path results for enhanced accuracy. To efficiently extract data related to campus drug use from unstructured texts, the model was trained to include semantic knowledge. To verify the model's feasibility and reliability, three coders randomly labeled 300 news reports, achieving over 99% classification accuracy, which was considered acceptable ([<reflink idref="bib12" id="ref50">12</reflink>]). Accuracy was calculated using the following formula:</p> <p></p> <ulist> <item> <emph>A</emph> = belongs to category <emph>A</emph> and coded as <emph>A</emph>;</item> <p></p> <item> <emph>B</emph> = belongs to category <emph>A</emph> but coded as Not <emph>A</emph>;</item> <p></p> <item> <emph>C</emph> = belongs to category Not <emph>A</emph> but coded as <emph>A</emph>; and</item> <p></p> <item> <emph>D</emph> = belongs to category Not <emph>A</emph> and coded as Not <emph>A</emph>.</item> </ulist> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi mathvariant="normal">Accuracy</mi><mo>=</mo><mrow><mo>(</mo><mi mathvariant="normal">A</mi><mo>+</mo><mi mathvariant="normal">D</mi><mo>)</mo></mrow><mo stretchy="false">/</mo><mrow><mo>(</mo><mi mathvariant="normal">A</mi><mo>+</mo><mi mathvariant="normal">B</mi><mo>+</mo><mi mathvariant="normal">C</mi><mo>+</mo><mi mathvariant="normal">D</mi><mo>)</mo></mrow></mrow></math> </ephtml> </p> <p>Graph</p> <p>Figure 1 illustrates the manual and automated content analysis workflow for DiVoMiner's computer-assisted classification. The data analysis proceeded as follows:</p> <p></p> <ulist> <item> (<reflink idref="bib1" id="ref51">1</reflink>) <emph>Data Preparation</emph>: The collected data were imported into the DiVoMiner platform. For each article, defining, sampling, and screening were conducted on keywords, phrases, or statements related to drugs and campus. A total of 26,541 news reports were initially gathered.</item> <p></p> <item> (<reflink idref="bib2" id="ref52">2</reflink>) <emph>Data Management</emph>: DiVoMiner's computational function explored the collected news data by storing, cleaning, filtering, and preprocessing. We adopted a hybrid approach to extract the desired data accurately, combining manual review and computational techniques. We manually examined specific news reports, identified criteria for inappropriate articles, and translated these criteria into computer screening commands for the batch selection of noise. This process was undertaken to avoid unnecessary noise data affecting the analysis. Subsequently, 17,698 reports were included as the primary sample. The titles, content, publications, and publication dates of the news articles were crawled and stored in Excel files. The research team randomly selected 1,000 news articles from the sample for preprocessing. Several terms were frequently reported in the news. For example, articles commonly reported that the reasons adolescents used illegal drugs were because of curiosity or because they dropped out of school. These news items are often accompanied by terms, such as risk factors, influencing factors, underlying causes, and possible causes. Consequently, a theme of "potential risk factors associated with illegal drug use" was developed to collectively categorize "curiosity" and "dropping out of school."</item> <p></p> <item> (<reflink idref="bib3" id="ref53">3</reflink>) <emph>Data Analysis</emph>: Both manual assistance and algorithm coding were used for data processing, including pilot coding, subsequent revision, and double coding. Coders manually reviewed news reports and, based on their domain knowledge and research objectives, developed a codebook with keywords for each coding category. After setting the keywords, the platform's corpus performed automatic word segmentation, followed by algorithmic coding to classify the content. Manual spot checks were then conducted to review the machine coding results, with continuous adjustments made to incorrect keywords to enhance accuracy. For instance, when reviewing news articles, we found the phrase "teenagers are exposed to drugs because they join gangs," indicating gang involvement as a risk factor for teenage drug use. The keyword "gangs" was selected, and the DiVoMiner platform classified all articles mentioning this term as addressing the gang-related risk factor for drug exposure.</item> <p></p> <item> (<reflink idref="bib4" id="ref54">4</reflink>) <emph>Result Visualization</emph>: After the machine coding was completed, we obtained automated message trends and keyword cloud for comprehensive data and further visualized the analysis results on the platform.</item> </ulist> <p>Graph: Figure 1. Hybrid and automatic content analysis workflow for DiVoMiner's computer-assisted classification.</p> <hd id="AN0186372412-8">Hybrid Reliability Test</hd> <p>To ensure the validity and reliability of the machine analysis, a manual technique was used to enhance the reliability of the computational process for extracting output text, including frequent terms, classification, and topics ([<reflink idref="bib86" id="ref55">86</reflink>]). The research team retrieved and reviewed the results during a data inspection meeting. Texts with thematic errors, such as news reports unrelated to campuses, were also identified. Subsequently, the identified texts were handed over to three coders with a background in data mining for manual review to increase intercoder reliability. To ensure the coders understood the content and standards of the coding framework, the research team provided them with coding framework explanations and instructions. Before starting formal reliability testing, the coders coded at least 50 data texts for different variables and then recorded and discussed coding process problems with the research team members. Consequently, the research team members confirmed that the coding accuracy was above 95% before proceeding with manual encoding.</p> <p>This study used the [<reflink idref="bib32" id="ref56">32</reflink>] method, a variation on percentage agreement, as the primary method for intercoder internal reliability. An agreement level of 80% or greater amongst coders has been proposed as acceptable in previous research ([<reflink idref="bib43" id="ref57">43</reflink>]). The Holsti method formula ([<reflink idref="bib81" id="ref58">81</reflink>]) is as follows:</p> <p></p> <ulist> <item> <emph>PA</emph> = The percentage of agreement between three coders</item> <p></p> <item> <emph>A</emph> = The number of two coders' consensus judgments</item> <p></p> <item> <emph>N</emph>1, <emph>N</emph>2, <emph>N</emph>3 = The number of decisions made by each coder</item> </ulist> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi mathvariant="normal">PA</mi><mo>=</mo><mn>3</mn><mi mathvariant="normal">A</mi><mo stretchy="false">/</mo><mrow><mo>(</mo><mi mathvariant="normal">N</mi><mn>1</mn><mo>+</mo><mi mathvariant="normal">N</mi><mn>2</mn><mo>+</mo><mi mathvariant="normal">N</mi><mn>3</mn><mo>)</mo></mrow></mrow></math> </ephtml> </p> <p>Graph</p> <p>In this study, the variables encoded through both manual and computer methods included potential risk factors for illegal drug use, stakeholders involved, the content and strategies of anti-drug activities, and drug prevention treatment goals and strategies. Coders extracted news reports to compare the consistency between human and machine interpretations. For errors in machine coding, manual adjustments were made on the platform. Additionally, we applied the Holsti method to calculate internal reliability, with reliability coefficients for the coding variables ranging from 0.82 to 0.90, indicating significant intercoder reliability.</p> <hd id="AN0186372412-9">Content Analysis</hd> <p>Content analysis is a widely used method to scrutinize news coverage content ([<reflink idref="bib14" id="ref59">14</reflink>]; [<reflink idref="bib64" id="ref60">64</reflink>]) and is a suitable approach for capturing the portrayal of campus anti-drug news reports. Therefore, a content analysis method was adopted to code the collection of 17,698 news reports and convert the text content into quantifiable data. The developed codebook was checked and discussed by the research team multiple times to improve its appropriateness.</p> <hd id="AN0186372412-10">Results</hd> <p></p> <hd id="AN0186372412-11">Keyword Cloud and Distribution Trend</hd> <p>Figure 2 shows the keyword cloud used to visualize the distribution of the collected keywords. The larger the keyword, the more frequently it appears. The most used terms were "illegal drugs,""anti-drug,""campus,""activities,""students,""adolescents,""advocacy,""education,""safety," and "epidemic prevention."Figure 3 shows the distribution frequencies of campus anti-drug-related news articles. November had the highest number of campus anti-drug news reports, followed by a second peak of news reports around the summer vacation period between late June and August.</p> <p>Graph: Figure 2. Keyword cloud.</p> <p>Graph: Figure 3. Publication distribution trend (by month).</p> <hd id="AN0186372412-12">Four Major Themes</hd> <p>Four major themes were identified from the analysis: potential risk factors associated with illegal drug use, stakeholders involved, content and strategies of anti-drug activities, and treatment goals and strategies for drug prevention. The results were based on the educational system segmentation in Taiwan, which is categorized as follows: elementary school (6–12 years), middle school (13–15 years), high school (16–18 years), and college (19 years and above).</p> <p>Figure 4a shows the primary potential factors contributing to the highest proportion of risk factors for drug use among students, which were "curiosity" (33.29%) and "curiosity combined with external factors" (29.95%), both under the category of individual factors. Numerous external factors involve drug dealers disguising new psychoactive substances as products, such as beverages (e.g., coffee bags) or snacks (e.g., jelly and gummy candies), and employing marketing strategies to entice adolescents to try them. Furthermore, middle school students appear to be most susceptible to the influence of interpersonal factors, such as peers and off-campus gangs. Regarding elementary school students, pressure factors seem to have the least impact on them.</p> <p>Graph: Figure 4. Four major themes emerged from the content analysis results (n = news reported numbers). Theme 1 : (a) potential risk factors associated with illegal drug use; Theme 2 : (b) stakeholders involved; Theme 3 : (c) content of anti-drug activities and (d) anti-drug activity strategies; Theme 4 : (e) drug prevention treatment goals and (f) drug prevention treatment strategies.(continued)</p> <p>As shown in Figure 4b, the top three stakeholders involved were "family,""teachers," and "classmates." Regarding content, Figure 4c shows that the most frequently used were "anti-drug and campus safety campaigns." Regarding anti-drug educational and advocacy strategies, the most common were "tailored programs" and "campus safety activities" as shown in Figure 4d. Moreover, as illustrated in Figure 4e, the primary treatment goal for illegal drug prevention in adolescents was "social rehabilitation." Regarding treatment strategies, the top three items with the highest proportions were "encouraging statement,""caring," and "nonjudgmental support," as shown in Figure 4f.</p> <hd id="AN0186372412-13">Discussion</hd> <p>Text mining enables the automatic analysis of large text datasets, significantly reducing time compared to labor-intensive manual coding ([<reflink idref="bib51" id="ref61">51</reflink>]). While it offers higher accuracy and consistency, it lacks certain contextual understanding, which can be supplemented by expert manual analysis ([<reflink idref="bib15" id="ref62">15</reflink>]). Text mining has been widely applied to process large volumes of social media data ([<reflink idref="bib36" id="ref63">36</reflink>]; [<reflink idref="bib61" id="ref64">61</reflink>]); however, to the best of our knowledge, few studies have focused on online news related to campus drug use issues. A text mining study on Brazilian syphilis news highlighted the media's crucial role in public health communication ([<reflink idref="bib56" id="ref65">56</reflink>]). This study utilizes a hybrid approach, combining manual and automated content analysis, to efficiently manage large datasets and structure information. It provides comprehensive insights into issue development through visual representation, highlighting how the media portrays campus drug use.</p> <p>Using a data mining approach, we delved deeper into campus anti-drug efforts through online news reports. While past research highlights sensation seeking as a key risk factor for adolescent drug use ([<reflink idref="bib82" id="ref66">82</reflink>]), our findings identified curiosity as a major factor. Additionally, we found that external influences, such as the marketing and packaging of emerging drugs, have amplified teenagers' curiosity. We also expanded the key stakeholders influencing adolescent drug use to include teachers and principals, whereas previous studies primarily focused on family and peers ([<reflink idref="bib83" id="ref67">83</reflink>]). Campus anti-drug programs are crucial for preventing student drug use, and our findings underscore comprehensive safety initiatives tailored to students at different educational stages, aligning with current research and practical trends ([<reflink idref="bib40" id="ref68">40</reflink>]; [<reflink idref="bib71" id="ref69">71</reflink>]). In summary, this study this study enhances understanding of campus drug use, offering valuable insights for health professionals, researchers, government officials, and educators.</p> <p>In the context of campus anti-drug news reports, the findings derived from the keyword cloud analysis indicate that keywords associated with campus anti-drug efforts were intertwined with the critical goal of ensuring student safety. Notably, the timeframe during which the online news reports were examined coincided with the COVID-19 pandemic, resulting in the prominence of the word "safety" as one of the most frequently occurring terms. Another critical finding emerged when "epidemic prevention" appeared as a significant keyword. This finding is particularly noteworthy as it establishes a meaningful link between campus anti-drug initiatives, safety concerns, and the imperative efforts of epidemic prevention.</p> <p>The heightened frequency of illegal drug use may be associated with seasonal effects. When analyzing the observed trends in the months when news coverage of campus drug use was published, we found a peak in related news coverage around November. Studies have indicated that substance use among adolescents increases during the summer months of June and July ([<reflink idref="bib54" id="ref70">54</reflink>]). The second peak of news reports aligns with this finding. A study funded by the National Institute on Drug Abuse indicated the influence of seasonal effects on initial drug use ([<reflink idref="bib54" id="ref71">54</reflink>]). Another study revealed that specific seasonal periods might render students more susceptible to drug use initiation, as evidenced by the trends in November and December ([<reflink idref="bib41" id="ref72">41</reflink>]). For example, previous research suggests that cold weather increases the risk of substance use and even death due to drug-use parties celebrating the Christmas holidays or cold weather causing sensory numbness and drug overdose ([<reflink idref="bib25" id="ref73">25</reflink>]; [<reflink idref="bib77" id="ref74">77</reflink>]). These findings are consistent with those of the present study's peak months of adolescent drug use in the news.</p> <p>The study also found that in the news, curiosity is considered the highest potential risk factor for adolescents' illegal drug use, consistent with a national survey conducted in Taiwan which indicated "curiosity" as a prevalent initial factor for drug use among adolescents ([<reflink idref="bib22" id="ref75">22</reflink>]). The finding also aligns with a prior study that found a significant association between curiosity, use intentions, and substance use ([<reflink idref="bib65" id="ref76">65</reflink>]; [<reflink idref="bib79" id="ref77">79</reflink>]). Multiple studies have also identified sensation seeking as a risk factor and predictor for substance use ([<reflink idref="bib9" id="ref78">9</reflink>]; [<reflink idref="bib50" id="ref79">50</reflink>]). The curiosity construct encompasses two dimensions: exploration (reflecting the desire to seek novel experiences and knowledge) and absorption (indicating deep engagement and absorption in such activities). Curiosity exhibits a dual nature associated with adaptive and harmful behaviors. Thus, several studies have incorporated sensation seeking into the curiosity construct ([<reflink idref="bib34" id="ref80">34</reflink>]). Intertwining sensation seeking with curiosity has added depth to our understanding of the psychological mechanisms of drug use.</p> <p>This study revealed that family members had the highest level of involvement in adolescents' drug-related news, followed by peers. This is consistent with previous research that emphasized the significant association between family and adolescent drug use ([<reflink idref="bib4" id="ref81">4</reflink>]; [<reflink idref="bib6" id="ref82">6</reflink>]). A study from the European School Survey Project on Alcohol and Other Drugs found that adolescent drug use is closely associated with lower socioeconomic status and lower parental education ([<reflink idref="bib23" id="ref83">23</reflink>]). Moreover, peer-influence interventions have shown effectiveness in predicting and preventing adolescent drug use ([<reflink idref="bib44" id="ref84">44</reflink>]). Therefore, integrated prevention programs should encompass family and peer levels to address adolescent drug use effectively.</p> <p>Categorizing the content of anti-drug efforts revealed that they include anti-drug and campus safety campaigns, identification, and recognition of drugs in disguise, crime prevention and law education, and interdisciplinary drug education. This is because drug use in Taiwan is not only a problematic behavior but also a criminal act, and illegal drugs are divided into four categories with different penalties. From the educational system's perspective, the anti-drug activity content in elementary schools is more focused on law education and identifying and recognizing drugs in disguise, while colleges present the most interdisciplinary content. It is recommended that responsive strategies be adopted based on the age and needs of the target students, which is consistent with the International Standards on Drug Use Prevention ([<reflink idref="bib74" id="ref85">74</reflink>]). A study also noted that adopting an adolescent-centered model to implement the intervention would have a more significant effect ([<reflink idref="bib33" id="ref86">33</reflink>]).</p> <p>Moreover, the results indicate that new forms of drugs are frequently disguised as snacks and drinks, easily deceiving adolescents who mistakenly consume them. Such packaging reduces adolescent awareness of illegal drugs and encourages their use. This finding is consistent with a national report indicating that new forms of disguised drugs are used mainly by adolescents, presenting a critical concern ([<reflink idref="bib22" id="ref87">22</reflink>]). Drugs disguised as candies and other items are usually a mix of various drugs, which can cause unexpected harm due to unknown ingredients. Compared to adults, the central nervous system of children and adolescents may be more susceptible to the effects of such drugs in disguise ([<reflink idref="bib53" id="ref88">53</reflink>]). Monitoring the Future (MTF) in America demonstrated that essential attitudes and beliefs regarding drug use are critical determinants of consumption patterns, particularly the perceived level of risk to the user associated with specific drugs and disapproval of their use for adolescents ([<reflink idref="bib47" id="ref89">47</reflink>]). Thus, it is suggested that the severe harm of disguised drugs should be emphasized in school-based intervention programs ([<reflink idref="bib85" id="ref90">85</reflink>]).</p> <p>Our results indicate that the two most frequent strategies for anti-drug educational activities on campus are tailored programs and campus safety activities. This is because Taiwan's Ministry of Education provides an annual subsidy for developing a tailored campus anti-drug program based on its own needs assessment. In addition, since campus anti-drug efforts are under the purview of the School Safety Division of the Ministry of Education ([<reflink idref="bib48" id="ref91">48</reflink>]), anti-drug education and advocacy activities are often linked to safety activities and are reported in online news reports. Past studies also pointed to the three aspects of school culture, policy, and curriculum when designing evidence-based campus substance use intervention programs ([<reflink idref="bib68" id="ref92">68</reflink>]).</p> <p>From the big data of anti-drug activities, the characteristics of diversified categories were identified, such as sports (e.g., baseball, basketball), videos (e.g., self-media, short film), outdoor activities (e.g., camp, hiking), exhibitions, curriculum, dance (e.g., street dance), fun games (e.g., escape room, flash mob activities), transfer (e.g., mobile bus), e-games and technology (e.g., AR, VR, AI module), consultation, social activities (e.g., school fair), and arts and culture (e.g., drama, singing competition). A meta-analysis of school-based substance use prevention programs suggests that health education and social learning models exhibit significant effectiveness, particularly when combined with oral, written, and audiovisual forms of support ([<reflink idref="bib20" id="ref93">20</reflink>]). The alignment of these anti-drug activities with the existing evidence is precise. They not only resonate with student interests but also amplify interactivity, consequently fostering heightened student engagement. Therefore, these meticulously crafted strategies and methodologies have emerged as pivotal tools for developing campus anti-drug efforts ([<reflink idref="bib17" id="ref94">17</reflink>]; [<reflink idref="bib69" id="ref95">69</reflink>]).</p> <p>In the context of schools' three-tiered prevention model ([<reflink idref="bib19" id="ref96">19</reflink>]) for adolescent drug use, primary prevention focuses on raising awareness and educating the entire school community. Secondary prevention involves early screening for high-risk students and tailored counseling, while tertiary prevention refers to students engaged in drug treatment ([<reflink idref="bib72" id="ref97">72</reflink>]). An integrated intervention strategy, known as positive behavior support (PBS), could be employed, including all three prevention levels and emphasizing individual support to address problematic behaviors ([<reflink idref="bib5" id="ref98">5</reflink>]). According to the Campus Substance Use Counseling Reference Manual issued by Taiwan's Ministry of Education ([<reflink idref="bib49" id="ref99">49</reflink>]), motivational interviewing is the most commonly used counseling intervention model in Taiwan. Studies have explored applying motivational interviewing to adolescent substance use counseling ([<reflink idref="bib39" id="ref100">39</reflink>]). This study found that "encouraging statements,""caring," and "nonjudgmental support" were the top three strategies in adolescents' drug treatment, which are consistent with the principles of motivational interviewing emphasizing rapport building, resisting the righting reflex, understanding the patient's motivations, and listening with empathy ([<reflink idref="bib28" id="ref101">28</reflink>]). Establishing a positive and caring atmosphere on campus can aid high-risk students in reducing drug use by enhancing their social connections and protective factors ([<reflink idref="bib52" id="ref102">52</reflink>]).</p> <hd id="AN0186372412-14">Implications</hd> <p>This study illustrates the potential of a manual and automated hybrid approach for content analysis with text mining of online news reports to identify campus anti-drug activities. Regarding theoretical implications, this study enhances our understanding of adolescent illegal drug use within the context of news reporting. It introduces a theoretical framework to analyze how adolescent drug use and campus anti-drug initiatives are depicted in news reports, thereby enriching our understanding of how the news media shape public perceptions. Regarding practical implications, the substantial increase in online news reports has led to an overwhelming amount of textual data. The proposed method offers a practical approach for extracting insights more efficiently than manual analysis when faced with these vast data volumes. Educators can benefit from the insights obtained from this study into prevalent risk factors, stakeholders, and effective strategies for anti-drug activities in tailoring their programs to meet specific needs. This study revealed that the highest potential risk factor for drug use is curiosity, and family and peers are the most common stakeholders, which can be included as a reference for educators in formulating drug use prevention programs. Moreover, the most discussed drug use education plans in the news are tailored programs that are diverse in activity forms, highly interactive, and suitable for students' ages and needs, with positive behavior support and motivational interviews being frequently mentioned strategies in campus anti-drug news.</p> <p>As for the regulatory implications, regulatory agencies can use this study's findings to evaluate the effectiveness of existing anti-drug campaigns and consider adjustments to better align with established themes and prevalent strategies. Policymakers can consider the higher incidence of school drug use news towards the end of the year and during summer, which correlates with seasonal and school holiday patterns, and devise regulations aimed at preventing new forms of disguised drugs to which adolescents are easily exposed. Future studies may apply this method to identify prospective categories and investigate issues of interest. Compared with manual or traditional content analysis research, this method aids in conducting in-depth analysis, offering a rapid and inexpensive alternative to acquiring an overview of big data in written texts.</p> <hd id="AN0186372412-15">Limitations</hd> <p>This study had several limitations. First, in textual analysis, the researcher crawls and filters the data to extract relevant information on the topic of interest. However, this approach might exclude potentially valuable insights. Moreover, the scope of this study did not extend to anti-drug-related activities within campus settings that are not covered by online news reports. Finally, the findings might not fully represent the entire campus's anti-drug news reports because the study did not include news reports on social media. Future research should address these issues to improve the quality of the research methods.</p> <hd id="AN0186372412-16">Conclusions</hd> <p>This study investigated adolescent drug use and campus drug use prevention in the context of online news reports, identifying the portrayal of adolescent drug use and campus anti-drug initiatives in news reports. This study used a hybrid manual and automated content analysis approach to identify adolescent drug use and campus drug use prevention from Taiwanese online media. We identified four major themes in news coverage: 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. Our study provides insights from online reports coverage on adolescent drug use and campus anti-drug news for adolescents. Future research could follow the methodology of this study, substituting campus-related keywords with those related to the family, to explore how families are represented in news reports on adolescent drug use, as families are one of the critical stakeholders.</p> <hd id="AN0186372412-17">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-sgo-10.1177_21582440251334852 for Exploring Campus Anti-Drug Activities Through Online News Reports: A Hybrid Manual and Automated Content Analysis Approach by Ching-Hao Chang, Chiu-Mieh Huang, Kah-Yew Lim, Fen-He Lin, Kuei-Yu Huang and Jong-Long Guo in SAGE Open</p> <p>This article was subsidized by the National Taiwan Normal University (NTNU), Taiwan, Republic of China. 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Springer Singapore.</bibtext> </blist> </ref> <ref id="AN0186372412-19"> <title> Footnotes </title> <blist> <bibtext> Ching-Hao Chang</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0009-0005-3932-5026 Chiu-Mieh Huang</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-9143-9972 Kah-Yew Lim</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0009-0008-1083-5913 Fen-He Lin</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-7300-4622 Kuei-Yu Huang</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibl id="bib10" idref="ref35" type="bt"></bibl> <bibtext>https://orcid.org/0000-0002-6296-0061 Jong-Long Guo</bibtext> </blist> <blist> <bibl id="bib11" idref="ref46" type="bt"></bibl> <bibtext>Graph https://orcid.org/0000-0002-3120-014X</bibtext> </blist> <blist> <bibtext> Human Research Ethics Committee of National Taiwan Normal University approved this study (Ref. NO. 202304HM024).</bibtext> </blist> <blist> <bibtext> JLG, CMH, and KYL conceived and designed the study. CHC, CMH, KYL, FHL, KYH, and JLG provided administrative, technical, and material support. KYL, CMH and CHC performed data management and statistical analysis. All authors were responsible for the drafting of the manuscript. All authors have read and approved the final version of the manuscript.</bibtext> </blist> <blist> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a grant from the Taiwan Food and Drug Administration (Grant #112TFDA-N-004).</bibtext> </blist> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The datasets generated or analyzed during this study are available from the corresponding author upon reasonable request.</bibtext> </blist> <blist> <bibtext> Chiu-Mieh Huang and Jong-Long Guo.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Ching-Hao Chang; Chiu-Mieh Huang; Kah-Yew Lim; Fen-He Lin; Kuei-Yu Huang and Jong-Long Guo</p> <p>Reported by Author; 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  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ching-Hao+Chang%22">Ching-Hao Chang</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0005-3932-5026">0009-0005-3932-5026</externalLink>)<br /><searchLink fieldCode="AR" term="%22Chiu-Mieh+Huang%22">Chiu-Mieh Huang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9143-9972">0000-0001-9143-9972</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kah-Yew+Lim%22">Kah-Yew Lim</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0008-1083-5913">0009-0008-1083-5913</externalLink>)<br /><searchLink fieldCode="AR" term="%22Fen-He+Lin%22">Fen-He Lin</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7300-4622">0000-0002-7300-4622</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kuei-Yu+Huang%22">Kuei-Yu Huang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6296-0061">0000-0002-6296-0061</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jong-Long+Guo%22">Jong-Long Guo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3120-014X">0000-0002-3120-014X</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22SAGE+Open%22"><i>SAGE Open</i></searchLink>. 2025 15(2).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 17
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22News+Reporting%22">News Reporting</searchLink><br /><searchLink fieldCode="DE" term="%22Prevention%22">Prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Risk%22">Risk</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+Use%22">Drug Use</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+Abuse%22">Drug Abuse</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Stakeholders%22">Stakeholders</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Retrieval%22">Information Retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+Education%22">Drug Education</searchLink><br /><searchLink fieldCode="DE" term="%22Content+Analysis%22">Content Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Internet%22">Internet</searchLink><br /><searchLink fieldCode="DE" term="%22Television%22">Television</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+Rehabilitation%22">Drug Rehabilitation</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescents%22">Adolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Safety%22">Safety</searchLink><br /><searchLink fieldCode="DE" term="%22Public+Health%22">Public Health</searchLink><br /><searchLink fieldCode="DE" term="%22Communications%22">Communications</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Taiwan%22">Taiwan</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/21582440251334852
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2158-2440
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1477131
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1477131
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/21582440251334852
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
    Subjects:
      – SubjectFull: News Reporting
        Type: general
      – SubjectFull: Prevention
        Type: general
      – SubjectFull: Risk
        Type: general
      – SubjectFull: Drug Use
        Type: general
      – SubjectFull: Drug Abuse
        Type: general
      – SubjectFull: Correlation
        Type: general
      – SubjectFull: Stakeholders
        Type: general
      – SubjectFull: Information Retrieval
        Type: general
      – SubjectFull: Drug Education
        Type: general
      – SubjectFull: Content Analysis
        Type: general
      – SubjectFull: Internet
        Type: general
      – SubjectFull: Television
        Type: general
      – SubjectFull: Drug Rehabilitation
        Type: general
      – SubjectFull: Adolescents
        Type: general
      – SubjectFull: Safety
        Type: general
      – SubjectFull: Public Health
        Type: general
      – SubjectFull: Communications
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Taiwan
        Type: general
    Titles:
      – TitleFull: Exploring Campus Anti-Drug Activities through Online News Reports: A Hybrid Manual and Automated Content Analysis Approach
        Type: main
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            NameFull: Ching-Hao Chang
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            NameFull: Chiu-Mieh Huang
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            NameFull: Kah-Yew Lim
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            NameFull: Fen-He Lin
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            NameFull: Kuei-Yu Huang
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            NameFull: Jong-Long Guo
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          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2025
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            – Type: issn-electronic
              Value: 2158-2440
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              Value: 15
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              Value: 2
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            – TitleFull: SAGE Open
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