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Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
Youth exposure to pornography and violent web sites
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Youth exposure to pornography and violent web sites

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  • The majority of the data I’ll be talking about today are recent and were just collected this past December…
  • Copies of the reports for the YISS-1 and YISS-2 can be found free of charge at: http://www.unh.edu/ccrc/internet-crimes/papers.html
  • Unfortunately, the question about movies for 2007 was not asked in a parallel fashion, and as such is not included here. For Internet and magazines however, you can see that there is not much change over the 12-months.
  • We now have data from four samples that allow us to map the frequency of intentional exposure to x-rated material over time. When we do so (above), we very clearly see a pattern for increased exposure rates as age increases. Note that this age group is also normatively and developmentally appropriately becoming curious about sex. Note also that GuwM W1 and W2 are the same youth, whereas YISS and GuwM are completely different samples
  • Friends are by far the most common source of information about x-rated web sites.
  • Data
  • Exposure to hate and death sites across time and age, based upon three data sets (note that GuwM W1 and W2 are the same youth, whereas YISS and GuwM are completely different samples)
  • Again, note the influence that friends have on learning about these types of web sites.
  • We looked at frequencies of visiting violent web sites at Wave 2 versus Wave 1, and we looked specifically at those who said they had never heard of these web sites at Wave 1: were they more likely to visit the web site at Wave 2? Did we inadvertently create exposures? The answer is no. These youth are no more likely at Wave 2 than youth who were aware of violent web sites at Wave 1 to visit violent web sites by Wave 2…
  • Transcript

    • 1. Internet Safety Technical Taskforce2nd Meeting, June 20 2008,Washington, DCYouth exposure to pornography andviolent web sitesMichele L. Ybarra MPH PhDInternet Solutions for Kids, Inc.* Thank you for your interest in this presentation.  Pleasenote that analyses included herein are preliminary. Morerecent, finalized analyses may be available by contactingCiPHR for further information.
    • 2. Roadmap for today’s discussion Exposure to x-rated material Examination of unwanted and wanted exposure Comparisons of online and offline exposure Exposure to violence online Hate sites Death sites Sites depicting scenes of war, death,terrorism Cartoon sites If it’s built, will they come??
    • 3. Growing up with Media Survey 1,588 households Online Survey Baseline data: August and September, 2006 Follow-up: October – December, 2007 (76% rr) Eligibility criteria: Youth: Between the ages of 10-15 years Use the Internet at least once a month for the last 6 months English speaking Adults Member of the Harris Poll OnLine Equally or most knowledgeable about youth’s media use Funded by the CDC (U49/CE000206)
    • 4. Youth Internet Safety Surveys 1,500 households were surveyed Random digit dial telephone survey Eligibility criteria: Youth: Between the ages of 10-17 years Use the Internet at least once a month for the last 6 months English speaking Adults Equally or most knowledgeable about youth’s Internet use YISS-1 conducted 1999-2000; YISS-2 conductedin 2005 by Dr. David Finkelhor and colleagues atUNH
    • 5. Unintentional exposure to x-ratedmaterial
    • 6. YISS DefinitionIn the last 12 months:Have you been on a website that showedpictures of naked people or of people havingsex when you did not want to be on such asite?Have you opened an email or instant messagewith advertisements or links to x-rated websites when you did not want to receive them.
    • 7. Demographic profile of youthreporting unwanted exposure to pornAmong 1,501 10-17 year olds surveyed in 2005 YISS-2: 34% reported an unwanted exposure (40% reported ANYexposure) 54% were boys Most (76%) were older youth (14-17)Where did the unwanted exposures happen? 83% happened while surfing the web 40% occurred when doing online searches 17% clicked on links within sites 12% were from misspelled web addresses 18% came in the form of an email or IMWolak, Finkelhor and Mitchell, 2006
    • 8. Demographic profile of youthreporting unwanted exposure to pornSimilarly, in the UK… 57% of 9-19 year olds who use the Internet weeklyhave been exposed to pornography. As age increases, the likelihood of exposure alsoincreases: 21% of 9-11 year olds, 58% 12-15 yearolds, and 76% of 16-17 year olds Most is unintentional exposure: 38% from a pop-up 36% accidentally founds themselves on a website 25% received pornographic junk mailLivingstone & Bober, 2005
    • 9. What does it mean to be “unwanted”21% in YISS2 said they could tell it was x-ratedbefore entering (Wolak, Finkelhor, Mitchell, 2006) Perhaps they didn’t understand the term “x-rated” untilthey *saw* it Perhaps they saw a different type (e.g., moreextreme) of pornography then they were expecting
    • 10. Other important event characteristics There is significant overlap of youth reportingunwanted and wanted exposure Those reporting unwanted exposure are 2.8times more likely to report wanted exposure thanthose not reporting unwanted exposure to sexualmaterial online. 2% report going back to the web siteWolak, Finkelhor and Mitchell, 2006
    • 11. Synopsis Unwanted exposure is reported more commonlyreported than wanted exposure Older youth are more likely to report unwantedexposure to sexual material Exposure occurs during a web search versus anemail/IM link about 4:1 Those who report unwanted exposure are morelikely to report wanted exposure to x-rated material
    • 12. Intentional exposure to x-ratedmaterial
    • 13. DefinitionIn the last 12 months, have you: Gone to or seen an X-rated or “adult”website where the main topic is sex Watched an X-rated movie at a friend’shouse, your house, or in the theater wherethe main topic was sex? Looked at an X-rated magazine on purpose,like Playboy, where the main topic was sex?Definitions based upon those fielded in YISS-1
    • 14. Frequency of intentional exposure:GuwM10%11%13% 13%10%0%2%4%6%8%10%12%14%16%18%20%2006 (n=1588 10-15 yo) 2007 (n=1206 11-16 yo)InternetMagazinesMovies
    • 15. Wacky Internet data?Youth Internet Safety Survey 1:8% reported looking at x-rated material online12% reported looking at x-rated material offline 7% reported looking at x-rated material in magazines 8% reported looking at it in movies or videosYouth Internet Safety Survey 2:13% reported looking at x-rated material online (no data foroffline exposure)YISS-1 data: Ybarra & Mitchell (2005) Exposure to Internet pornography among children andadolescents: A National Survey. CyberPsychology and Behavior 8(5), 473-486.YISS-2 data: Wolak, Mitchell, and Finkelhor (2006) Online Victimization of Youth: Five YearsLater. Available online at: http://www.unh.edu/ccrc/internet-crimes/papers.html
    • 16. …online exposure across age and time0% 0%2%6%10% 10%12%14%1% 2%3%10%12%17%22%19%1%7%6%8%22%13%1%6%10%18%13%12%0%5%10%15%20%25%10 11 12 13 14 15 16 17YISS-1 (2000)YISS-2 (2005)GuwM W1 (2006)GuwM W2 (2007)
    • 17. Demographic profile of youth lookingat internet pornAmong 1,206 11-16 year old youth in GuwM 2 (Oct-Dec,2007): 80% male (OR = 4.2, p<.001) 14.4 years old (OR = 1.3, p<.001)How did they hear about the site? (top 5): From a friend: 53% Search engine: 30% Another web site: 29% Typed in an address to see what would come up: 22% Pop-up ad: 22%
    • 18. Psychosocial profile of Internet pornseekers (GuwM2)Physical bullying (OR = 4.6, p<0.001)Getting into fights (OR = 3.1, p<.001)Poor academic performance (OR = 2.7, p<.002)Carrying a weapon to school in the past 30 days (OR = 6.6,p=0.01)Poor relationship with caregiver (poor monitoring: 1.3, p=0.006:poor emotional bond: 1.2, p=0.006)Substance use (alcohol: 7.9, p<0.001; cigarettes: 6.6, p<.001;Marijuana: 5.5, p<.001)Seriously violent behavior: OR = 7.1 p <.001These youth are significantly more likely tohave a lot of things going onMeasures refer to ‘ever in the last year’. Caregiver-child relationship variables are a summation of relatedindicators (e.g., “how often do your caregiver know where you are when you’re not at home”)
    • 19. Synopsis Older youth and boys are more likely to report looking atx-rated material. Children are not likely to look at porn(<1%) The Internet is not the most common access point for x-rated material. Youth reporting exposure to x-rated material are morelikely to also report a myriad of other concurrentpsychosocial problems. It is possible that x-ratedmaterial is a marker for concern for some youth whereasin other cases it is a marker for developmentallynormative sexual curiosity.
    • 20. Exposure to violent web sites
    • 21. Definitions A “hate” site is one that tells you to hate agroup of people because of who they are,how they look, or what they believe. A “death” website that shows pictures ofdead people or people dying. Some peoplecall these “snuff” sites.Definitions based upon YISS-1
    • 22. Definitions A website, including news-related sites, thatshows pictures of war, death, “terrorism” A website (that’s not an online game) thatshows cartoons, like stick people or animals,being beat up, hurt, or killedDefinitions specific to GuwM
    • 23. Frequency of exposure GuwM Wave 22% 4%51%42%47%54%0%10%20%30%40%50%60%Hate sites Death sitesYesNoI dont know what youre talking about
    • 24. Frequency of exposure GuwM Wave 222%18%49%46%29%36%0%10%20%30%40%50%60%News sites Violent cartoon sitesYesNoI dont know what youre talking about
    • 25. …online exposure across age and time5% 5%8%10%14%7%6%9%2%7% 7% 6%10%7%1%5%3%9%7%3%0%5%10%15%20%25%10 11 12 13 14 15 16 17YISS-1 (2000): 8%GuwM W1 (2006): 7%GuwM W2 (2007): 5%
    • 26. Demographic profile of youth lookingat hate and death sitesAmong 1,206 11-16 year old youth in GuwM 2 (Oct-Dec, 2007): 50% male (OR = 0.9, p=0.83) 13.4 years old (OR = 1.3, p=0.009) 74% are White (OR = 1.1, p=0.91) 15% are Hispanic (OR = 1.0, p=0.96)Of those who went to a site, how did they hear about it?(top 3) Hate sites: Friend (50%), Link from another site (22%),Typed it in (17%) Death sites: Friend (71%), search engine (31%), email(30%)
    • 27. Psychosocial profile of seekers of hateand death web sites in 2007 Physical bullying (OR = 4.4, p<0.001) Getting into fights (OR = 4.3, p<.001) Carrying a weapon to school in the past 30 days (OR =7.0, p=0.007) Poor relationship with caregiver (poor monitoring: 1.4,p=<.001: poor emotional bond: 1.3, p<.001) Substance use (alcohol: 6.0, p<0.001; cigarettes: 6.4,p<.001; Marijuana: 5.4, p<.001) Seriously violent behavior: OR = 10.1, p <.001These youth are significantly more likely tohave a lot of things going onMeasures refer to ‘ever in the last year’. Caregiver-child relationship variables are a summation of relatedindicators (e.g., “how often do your caregiver know where you are when you’re not at home”)
    • 28. Synopsis Older youth are more likely to seek outviolent web sites, but there are no apparentdifferences between boys and girls The 1-year prevalence rates of exposure todeath sites and hate sites are low: 2-4% In addition to exposure to violent web sites,these youth are significantly more likely tohave other challenges going on in their lives
    • 29. If it’s built,Will they come?
    • 30. NO..or at least, not always..
    • 31. What happened to kids who didn’t*know* about these sites? Just *knowing* about a web site is not enoughfor kids to go to them – even if it’s a “new” typeof site that some youth might find intriguing It seems that there are other factors thatinfluence one’s decision to visit these sites This is *good* news for us (bad news forresearchers, who are struggling to figure outhow to get kids to go to their health sites, andkeep them coming back!)
    • 32. Final thoughts Given that knowledge about unsavory web sites isinsufficient to predict whether a youth has visited thesite Figuring out *why* some youth seek out / visit violent websites whereas others don’t seems likely to be a key toprevention efforts. Based upon youth-report, the Internet does not appearto be a ‘risk environment’ for x-rated exposuresdifferently than the offline environment (movies andmagazines). Only when we are clear about the influence the Internet isand is not having on youth behavior will we be able to affectappropriate intervention strategies.
    • 33. *After* slidesWhat is an “OR”?An odds ratio (OR) is the ratio of the odds that someone exposed (e.g., toa violent web site) will report the behavior (e.g., bullying) versus theodds that someone not exposed will report the behaviorFor example, let’s say:Behaviorreported (e.g.,bullying)No behaviorreported (notbullying)Exposure (e.g., visiting atype of violent web site)8 teens 2 teensNo exposure (e.g., notvisiting the web site inquestion)12 teens 78 teens
    • 34. *After* slidesThus, (from the grid on the previous slide):8 out of 10 youth exposed also report thebehavior (so the odds are 8:2 or 4)12 out of 90 youth not exposed also report thebehavior (so the odds are 12:78 or 0.15)The ratio of these odds is 26 (4/0.15)
    • 35. *After* slidesThe ratio of the odds = odds ratio = OR = 26Those with the exposure are 26 times morelikely to report the behavior than thosewithout the exposure.Note: as you can see in the grid, this does notmean that all youth with the exposure willreport the behavior, or that those who reportthe behavior always report the exposure.But, the odds are higher that the two will co-occur than not…
    • 36. *After* slidesSo, what’s the relevance to the current slides?Well, for example the odds that youth who report going tohate and death sites engage in externalizing behaviors aresignificantly higher than youth who do not (e.g., the OR =10 related to seriously violent behavior with respect toyouth visiting hate/death sites versus not).Again: not all youth who have visited hate and death sites inthe past year report these externalizing behaviors. And,not all youth who report these externalizing behaviors alsovisited these web sites. But, the odds that they co-occurare higher than that they don’t….

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