Internet Prevention Messages: Targeting the Right Online Behaviors
Abstract
BACKGROUND: Internet safety programs urge youth to avoid sharing personal information and talking with "strangers" online. OBJECTIVE: To examine whether sharing personal information and talking with strangers online or other behaviors are associated with the greatest odds for online interpersonal victimization. DESIGN: The Second Youth Internet Safety Survey was a cross-sectional random digit-dial telephone survey. SETTING: United States. PARTICIPANTS: A total of 1500 youth aged 10 to 17 years who had used the Internet at least once a month for the previous 6 months. MAIN EXPOSURE: Online behavior, including disclosure of personal information, aggressive behavior, talking with people met online, sexual behavior, and downloading images using file-sharing programs. OUTCOME MEASURE: Online interpersonal victimization (ie, unwanted sexual solicitation or harassment). RESULTS: Aggressive behavior in the form of making rude or nasty comments (adjusted odds ratio [AOR], 2.3; P<.001) or frequently embarrassing others (AOR, 4.6; P = .003), meeting people in multiple ways (AOR, 3.4; P<.001), and talking about sex online with unknown people (AOR, 2.0; P = .02) were significantly related to online interpersonal victimization after adjusting for the total number of different types of online behaviors youth engaged in. Engaging in 4 types of online behaviors seemed to represent a tipping point of increased risk for online interpersonal victimization (OR, 11.3; P<.001). CONCLUSIONS: Talking with people known only online ("strangers") under some conditions is related to online interpersonal victimization, but sharing personal information is not. Engaging in a pattern of different kinds of online risky behaviors is more influential in explaining victimization than many specific behaviors alone. Pediatricians should help parents assess their child's online behaviors globally in addition to focusing on specific types of behaviors.
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Sharing personal photos or details online increases vulnerability to attacks, and visiting unverified websites can expose personal information, attracting cyberbullying.
“For example, sharing personal photos or details online increases vulnerability to attacks163,164, and visiting new websites without verifying their safety can expose personal information, attracting cyberbullying165.”
From Umbrella review of meta-analyses on the risk factors, protective factors, consequences and interventions of cyberbullying victimization (2024) · cites this paper as [164]
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- Authors
- Ybarra, M. L., Mitchell, K. J., Finkelhor, D., & Wolak, J.
- Venue
- Archives of Pediatrics and Adolescent Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2007-02-01
- Volume
- 161
- Issue
- 2
- Pages
- 138-145
- ISSN
- 1072-4710 (ISSN-L)
- OpenAlex
- W2167665512
- Cited by
- 206
- References
- 7
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 5 · 2023: 6 · 2022: 6 · 2021: 7 · 2020: 6 · 2019: 10 · 2018: 8 · 2017: 5 · 2016: 8 · 2015: 16 · 2014: 23 · 2013: 15 · 2012: 13
- FWCI
- 22.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Archives of Pediatrics and Adolescent Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Bullying, Victimization, and Aggression
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Physical Sciences › Computer Science › Information Systems › Cybercrime and Law Enforcement Studies
- Keywords
- online harassment, personal information disclosure, online sexual behavior, file sharing
- MeSH terms
- Adolescent, Adolescent Behavior, Adult, Aggression, Child, Child Behavior, Cross-Sectional Studies, Female, Humans, Interpersonal Relations, Deception, Surveys and Questionnaires, Risk-Taking, Sex Offenses, Psychological Distance, United States, Confidence Intervals, Odds Ratio, Crime Victims, Internet
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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