Parental Regulation and Online Activities: Examining factors that
influence a Youth’s potential to become a Victim of Online
Harassment
Robert Moore
Naga Tarun Guntupalli
Tina Lee
University of Tennessee – Martin, USA
Abstract
Online harassment
has been defined as an overt act of aggression committed against a
person through use of a variety of online communication tools (i.e.
e-mail, website, etc.). The current study examined adolescent
Internet-related behaviors and parental regulations to determine
which, if any, factors influenced a young person’s reporting of
online harassment victimization. The results of this analysis
revealed that adolescent females were more likely to report being a
victim of online harassment. There were no differences in the
victimization reporting among youths based on race and family
income. In examining the Internet behaviors that were found to
influence online harassment victimization, youths who used the
Internet to engage in instant messaging, chatting, blogging, and
downloading music files were more likely to report online
victimization. Factors related to parental regulation of Internet
use were found to have no effect on a respondent reporting
victimization from online harassment. Possible explanations for
these findings are discussed, as are recommendations for future
research in this emerging area.
Keywords:
Online
harassment, Cyber bullying, Harassment, Internet Crime.
Introduction
Interest in research related to
Internet-based deviance has increased dramatically over the last
decade. One area in which interest has increased equally among both
academics and practitioners alike is the realm of online harassment,
which is sometimes referred to as cyberbullying. With some estimates
claiming that as many as 97% of all youth use the Internet on a
regular basis (Ybarra & Mitchell, 2007), it is easy to understand
why the topic is gaining interest. It is believed that these youths
are using the Internet for playing games and communicating with
friends (Ybarra & Mitchell, 2004b; Subrahmanyam & Greenfield, 2008),
maintaining online blogs concerning their lives and interests
(Mitchell, Wolak, & Finkelhor, 2008), and using social networking
sites to develop and maintain relationships (Ybarra & Mitchell,
2008; Dilmac, 2009). Each of these behaviors could potentially lead
a young person to encounter harassment or bullying. The Internet
behaviors of young people could potentially cause them severe harm,
with some recent media reports linking cyberbullying and online
harassment to suicide-related deaths and attempted suicides among
juveniles (Bhat, 2008; Ruedy, 2008).
In discussing online harassment and
cyberbullying it is important to understand what is meant by the
terms. Hinduja and Patchin (2008) define cyberbullying as “willful
and repeated harm inflicted through the medium of electronic text”
(p.131), while Ybarra and Mitchell (2004) define online harassment
as “an overt, intentional act of aggression towards another person
online” (p.1308). For purposes of the current work the term online
harassment will be used, but it should be noted that studies in this
area have used the terms interchangeably, with the exception being
Wolak, Mitchell, and Finkelhor’s (2007) argument that online
harassment does not meet the traditional definition of bullying
because of an absence of repetition and a lack of physical
aggression in terms of the online behaviors. The current work seeks
to add to an emerging body of literature by examining several
Internet-related activities and parental regulation behaviors in
order to determine whether these factors influence the likelihood of
a youth indicating that they have been a victim of online
harassment. Before examining the current analysis, however, it is
important to briefly examine the literature on online harassment and
the various factors that have been found to influence online
victimization.
Past Studies of Online Harassment
Past studies on the frequency of
online harassment have found differing rates of victimization and
offending. For example, Bhat (2008) reported that studies of
Australian youth indicated as many as 42% of youth had been harassed
online, while Hinduja and Patchin (2008) found that 36% of girls and
32% of boys indicated that they have been victims of online
harassment. Wolak, Mitchell, and Finkelhor (2007) were more
conservative in their findings, indicating that approximately 9% of
their nation-wide sample indicated that they had been victimized by
online harassment. These differences could be attributable to
sampling methodologies but it also just as likely that these
differences could be attributed to what Wolak, Mitchell and
Finkelhor (2007) discussed as a lack of “standard definitions of
online harassment” (p. S51).
With the increasing numbers of youth
moving their activities to the Internet there is believed to be a
growing number of potential victims being introduced to the
technology every day (Gillespie, 2006). Keith and Martin (2005)
noted that parents have begun providing cellular telephones to their
children in an effort to make them more accessible and better
prepared to handle emergency situations. In the course of doing
this, however, they may have unintentionally provided a new tool to
be used in the electronic harassment of their child or someone
else’s child. While historically online harassment may have been
viewed as a nuisance or a mere inconvenience there is a growing
acceptance that online harassment may move beyond mere annoyance and
actually cause serious psychological harm to youthful victims, up to
and including a deterioration of their physical health (Ybarra &
Mitchell, 2004; Wang, Iannotti, & Nansel, 2009). Shariff (2005) has
noted that cyberbullying and online harassment may even sometimes be
viewed as more traumatizing than offline harassment. The argument is
that an individual who is victimized in person may be embarrassed or
become upset, but at least the victim knows who has harassed them
and they have an awareness of who witnessed the incident. With
online harassment the identity of the perpetrator may be unknown and
the number of witnesses may be in the millions, depending on which
website or web forum is involved in the harassment of the victim (Shariff,
2005).
These authors would add an additional
consideration given that online harassment involving the posting of
a harassing comment, picture or video may result in recurring
victimization in the sense that the image or comment may never be
completely removed from the Internet. Therefore, years after the
initial incident a person could still suffer from harassment
associated with posting harassing materials on the Internet. Perhaps
the best example of this would be Ghyslain Raza, the Canadian
teenager whose filmed version of himself portraying a Star Wars Jedi
knight engaged in a light saber fight (while holding a golf ball
retriever as his weapon) was uploaded to the Internet. As a result
of this video Raza was labeled ‘The Star Wars Kid,” and was on the
receiving end of numerous criticisms and embarrassing parodies of
his activity. The young man reportedly suffered from such severe
emotional distress that he was forced to withdraw from school and
seek treatment at a juvenile psychiatric facility (Auerbach, 2009).
In considering the timelessness of the Internet, the video was
uploaded in 2003 and is now over 7 years old. However, the video is
still occasionally encountered on television programs that conduct
countdowns of the more popular Internet videos to be viewed or
downloaded – most of which include comedians or commentators making
crude comments about the young man. One could easily argue that Raza
is still being harassed almost a decade later, and while his
original harassers may have forgotten the incident there appears to
be a number of comedians and television hosts who continue to take
their place.
This is not to say that all victims
of online harassment will respond in a similar manner. Cassidy,
Jackson, and Brown (2009), found that 91% of youth who encountered
online harassment indicated little or no fear associated with the
behavior. Interestingly enough 42% of the respondents in their study
indicated that cyberbullying and online harassment had become a
routine part of online communication, and as such they did not
maintain much fear that the behavior would carry over into their
offline activities. More troublesome was perhaps their finding that
32% of these youth indicated that they saw nothing wrong with online
harassment, although many of the respondents did indicate that if
the behavior was going to be addressed then the best approach would
be for school districts to develop education and awareness
campaigns. Agatston, Kowalski, and Limber (2007) found that female
students, more than their male counterparts, viewed cyberbullying as
a problem but did not often report victimization to teachers or
educators. Their reasoning for not reporting the harassment was that
the majority of the behaviors reportedly occurred after the school
day had ended, and as such was not a problem for the school to
handle and was not perceived as serious enough for law enforcement
to become involved. Any harassment that occurred during the day was
primarily committed by cellular phone – an electronic device that
the students were not supposed to have in their possession while on
the grounds of the school. Therefore, reporting the harassment would
have required admitting that the victim was violating school rules
as well.
The lack of concern for online
harassment in the above studies is troubling and perhaps indicative
of a dangerous shift in attitude towards the acceptance of such
behaviors. Dilmac (2009) found that 22.5% of surveyed youth
indicated that they had engaged in cyberbullying or online
harassment at least once in their lifetime, with 87% of these
individuals claiming to have first been a victim of online
harassment themselves. Ybarra and Mitchell (2007) found similar
results with 29% of respondents indicating that they had harassed
someone online within the last year, with 82% of these harassers
indicating that they were victims of online harassment prior to
their becoming a perpetrator of the behavior. Should these trends
continue then it would not be inconceivable for the problem to
continue to increase in frequency as more and more online
opportunities are presented to young people.
One final factor worth examining
involves the impact race and gender has had on past studies of
harassment behaviors. Researchers have found differing results in
terms of which youth are more often victims of online harassment or
cyberbullying. Li (2006) and Hinduja and Patchin (2008) found no
significant differences between the rates of victimization when
comparing male youth with female youth. However, Dilmac (2009) and
Kowalski and Limber (2005) found that a greater percentage of
females were victimized by cyber bullies. Hinduja and Patchin (2008)
also noted no differences in victimization when considering for race
of the youth, while other studies either found similar results or
did not include race as a variable in their analysis. These studies
are excellent foundations for future research on differential
experiences with online harassment when considered for gendered and
racial differences. With that being stated, these early studies have
certainly indicated that there are some interesting differences
between victims of online harassment and victims of traditional
victims of school-yard bullying. It would appear that each of these
past studies agree that victims, and sometimes perpetrators, of
cyberbullying and online harassment may be more diverse than victims
of more traditional forms of bullying.
If the problem of online harassment
is so great, and the frequency of the behavior is increasing, then
the question becomes one of what can be done to prevent the
behavior. With an increased number of online harassment and
cyberbullying incidents being reported in the media there are some
organizations that have developed recommendations for preventing
victimization. The U.S. Department of Health and Human Services has
released several recommendations through a website developed by the
Health Resources and Services Administration (HRSA). These
recommendations for parents include: 1) keeping home computers in
public family rooms, 2) have rules about Internet use and
consequences, 3) monitor your child’s online communications, and 4)
install monitoring programs on children’s computers (HRSA, 2010).
Hinduja and Patchin (2008) have recommended the use of filtering
programs to monitor activity, but have also noted that communication
between parent and child should also be used to prevent
victimization. Others have recommended that school personnel such as
resource officers become more involved in prevention and awareness
given that many harassment situations may begin at school (Wolak,
Mitchell, & Finkelhor, 2007). All of these individuals, however,
agree that none of the above recommendations are likely to be
effective if they are used alone. Instead a program that combines
these factors with increased awareness and education of potential
victims has been mentioned as a possible solution.
The Current Study
Data for the current analysis was
obtained from the Pew Internet and American Life Project. The data
making up the dataset was collected from October to November of 2006
as a part of Pew Internet’s Teens and Internet project. The data was
collected by Princeton Data Source, LLC and was part of a nationwide
telephone survey. Interviewers initially obtained 3514 cooperating
households to be considered for inclusion in the data collection
procedures. However, of that 3514 households only 1182 households
were determined to be eligible for inclusion in the survey. Reasons
for exclusion included lack of an adult in the household, difficulty
in obtaining someone in the household who could overcome a language
barrier and the absence of a child between the ages of 12 and 17 in
the household. Of these 1182 households, 935 households were able to
provide meaningful data by completing the entire survey without
interruption.
Questions asked of respondents were
designed to collect data on the parents’ age, race, sex, marital
status, and education level. Additionally, each parent who took part
in the survey answered a series of questions on geographic location,
the numbers and types of computers in the residence, the location of
these computers, the presence of rules for Internet usage, and the
types of Internet filters or Internet monitoring programs in
operation on these computers. Questions asked of youth included
requests for information related to the kids’ age, gender, race,
number of electronic communication devices used, activities engaged
in while online, experience with various online harassment
behaviors, and what type of information they made available to
others on their web pages and social networking websites.
The data for this survey was
collected and analyzed using a variety of descriptive statistical
analyses in the Pew Internet and American Life’s report
“Cyberbullying” (Lenhart, 2007). For the purpose of the current
analysis the dataset, made available to us in SPSS format, had to be
recoded for more advanced analysis. Many of the questions were
worded and answered in a format designed to elicit “yes” or “no”
responses. These responses were converted into a series of
dichotomous variables (0 = no, 1 = yes). Several questions asked
respondents to respond “yes” or “no” to a selection of questions
that were closely related. For example, one question asked
respondents to indicate whether or not they: owned a computer, owned
a laptop, owned a cellular phone, or owned a Smartphone device (i.e.
Blackberry, iPhone, etc.). Each individual question was converted to
a dichotomous variable (0 = no, 1 = yes) and a sum variable, labeled
“Computers Used” was created, with values ranging from 0 to 4 – each
yes answer to the aforementioned technology ownership questions
increased the “Computers Used” variable by a value of 1. Higher
values for this variable were indicative of a respondent who
utilized multiple forms of computer technology in their Internet
activities. By recoding the data in this format the current analysis
was able to move beyond descriptive analyses to include a series of
logistical regression models designed to examine which factors
associated with demographic variables, parental regulation of
Internet activities, and actual Internet activities influenced a
respondent’s likelihood of reporting online harassment
victimization.
In determining whether or not a
respondent was a reported victim of online harassment, participants
were asked a series of questions relating to whether or not they had
experience with: 1) someone spreading a false rumor about them
online, 2) someone posting an embarrassing picture of them on the
Internet, 3) someone sending the respondent a threatening or
aggressive electronic communication, or 4) someone spreading a
private and embarrassing communication to others without permission.
Each of these questions were converted to a dichotomous variable (0
= No and 1 = Yes). A new dichotomous variable labeled “Online
Harassment Victim” was created and coded with a “0” if the
respondent had experienced none of the aforementioned behaviors and
with a “1” if the respondent had experienced any of the
aforementioned behaviors. The questions asked by Pew Internet and
American Life, as well as the use of dichotomous variables for
logistic regression analyses, were consistent with the methods
employed in other studies of online harassment and cyberbullying
(see Marcum, 2009; Mitchell, Wolak, & Finkelhor, 2008; Hinduja &
Patchin, 2008).
Results of the Current Analysis
In examining the youth respondents in
the study, 94.8% (n = 886) indicated that they used the Internet,
with 90.3% (n = 844) of the respondents’ parents also identifying
themselves as Internet users. Respondents were relatively equal in
terms of gender, with 50.4% (n = 471) being male and 49.6% (n = 464)
being female. The majority of respondents indicated their race as
being White (84.7%, n = 789), while the remaining respondents were
6.8% (n = 63) Black, 5.5% (n = 51) Hispanic, and 3.1% (n = 29)
classified themselves as Other. The median age for youth surveyed
was 15 years of age, with the youngest respondent being 12 and the
oldest being 17. When examining other demographic variables, the
majority of respondents indicated that they lived in suburban
communities (51.8%, n = 484) with family incomes less than $75,000
(50.9%, n = 476). In terms of online harassment victimization, the
majority of respondents indicated that they had not been harassed
online (71.3%, n = 667), while the remaining 28.7% (n = 268)
indicated that they had experienced at least one behavior indicative
of online harassment. For more detailed information on these
demographic data please see table 1 below.
Table 1:
Demographic Data for
Respondents
Variable n
% Variable n %
Gender:
Age:
Male 471
50.4 12 140 15.0
Female 464
49.6 13 134 14.3
14 157 16.8
Race:
15 149 15.9
White 789
84.7 16 183 19.6
Black 63
6.8 17 172 18.4
Hispanic 51
5.5
Other 29
3.1 Community Type:
Rural 221 23.6
Income:
Suburban 484 51.8
Less than $10,000 20
2.1 Urban 230 24.6
$10,000 to under $20,000 30 3.2
$20,000 to under $30,000 60
6.4 Adult Internet User:
$30,000 to under $40,000 90
9.6 No 91 9.7
$40,000 to under $50,000 91
9.7 Yes 844 90.3
$50,000 to under $75,000 185 19.8
$75,000 to under $100,000 164
17.5 Child Internet User:
$100,000 or more 204
21.8 No 49
5.2
Refused Answer 91
9.7% Yes 886 94.8
Parents Married:
No 193
20.6
Yes 741
79.3
Refused 1
.1
Victim of Online
Harassment:
No 667
71.3
Yes 268
28.7
Next, a series of cross-tabulations
were conducted to examine online harassment victimization when
considering for the variables race, age, gender, and family income.
The results of these cross-tabulations revealed that there was a
significant difference when comparing reported victimization of
males versus females. Thirty five percent of female respondents (n =
161) indicated that they had been victims of online harassment when
compared with 22.7% (n = 107) of male respondents (X2.05(1)
= 16.408, p < .001). There were no significant differences in
harassment victimization when comparing respondents’ race (X2.05(3)
= .314, p = .957) or respondents’ family income levels (X2.05(8)
= 5.872, p = .662).
In examining respondents’ age and
online victimization, significant differences were found among
respondents. Generally, older respondents in the current analysis
were more likely to report being a victim of online harassment than
were their younger counterparts. Specifically, respondents who were
15 and over indicated greater levels of online victimization than
did respondents between the ages of 12 and 14. Thirty eight percent
(n = 57) of 15 year old respondents indicated that they had been a
victim of online harassment, 29.5% (n = 54) of 16 year olds
respondents indicated that they had been a victim of online
harassment, while 32.6% (n = 56) of 17 year old respondents
indicated that they had been a victim of online harassment. Only
18.6% (n = 26) of 12 year old respondents, 25.4% (n= 34) of 13 year
old respondents, and 26.1% (n = 41) of 14 year old respondents
indicated that they had been victims of online harassment (X2.05(5)
= 16.225, p = .006). For more information on these analyses please
see table 2 below.
Table 2:
Cross-tabulations of Victimization by Gender, Race, Age, and
Income
No Yes Chi-Square p
n = 667 n =
268
Gender:
Male
45.4 22.7
Female
54.6 34.7 16.408 .000***
Race:
White
71.0 29.0
Black
71.4 28.6
Hispanic
74.5 25.5
Other
72.4 27.6 .314 .957
Age:
12
81.4 18.6
13
74.6 25.4
14
73.9 26.1
15
61.7 38.3
16
70.5 29.5
17
67.4 32.6 16.225 .006**
Income:
Less than $10,000
80.0 20.0
$10,000 to under $20,000 63.3 36.7
$20,000 to under $30,000 73.3 26.7
$30,000 to under $40,000 70.0 30.0
$40,000 to under $50,000 80.2 19.8
$50,000 to under $75,000 70.8 29.2
$75,000 to under $100,000 69.5 30.5
$100,000 or more 70.1
29.9
Refused Answer
70.3 29.7 5.872
.662
All values reported are percentages
* significant at .05 level ** significant at .01
level *** significant at less than .001
Finally, a series of logistic
regression models was created to examine the impact various
demographic variables, parental regulation factors, and
Internet-related activities had on respondents’ harassment
victimization. In examining demographic variables the only significant
predictor of online victimization was gender, with female respondents
being 1.7 times more likely to report being a victim of online
harassment (Odds Ratio = 1.752, p = .007). Race and Income, two other
demographic variables normally discussed in the online harassment
literature, were found to be non-significant in the current models. In
examining factors associated with regulation of household computers,
the number of computers owned was found to be a significant predictor
of online harassment in the second model. However, this variable was
found to be non-significant in the final model, which included a
variety of Internet activities (Odds Ratio = 1.159, p = .233).
However, the other regulatory variables such as: having monitoring
programs installed on the computer, having Internet filters installed
on the computer, maintaining and enforcing rules associated with
Internet use, and the presence of parental oversight were not found to
be significant variables in either of the models these variables were
included in.
The final logistic model included a
series of Internet activities that were examined for their influence
on the reporting of online harassment victimization. Four variables
were found to be significant predictors of the increased likelihood of
adolescent exposure to online harassment. Respondents who used
instant messenger programs were 2.9 times more likely to report being
a victim of online harassment (Odds Ratio = 2.951, p = .001), while
respondents who used chat software or engaged in online chat room
sessions were 2.1 times more likely to indicate victimization (Odds
Ratio = 2.151, p = .003). Another online communication activity, which
involves maintaining an online blog, was found to be a significant
predictor of online harassment with respondents who engaged in the
activity being 1.3 times more likely to report being a victim of
online harassment (Odds Ratio = 1.388, p < .032). Downloading music
was the fourth Internet activity found to increase the likelihood of
victimization, with respondents who downloaded music being 1.7 times
more likely to report being a victim of online harassment (Odds Ratio
= 1.742, p = .015). For more information on these analyses please see
table 3 below.
Discussion
The results from this analysis provide
continued support for several past studies, while providing several
new insights into factors that can influence online harassment
victimization. In support of Hinduja and Patchin’s (2008) work on
cyberbullying and online harassment the current analysis found that
there were no differences in victimization rates when considering for
the racial makeup of the youth, providing support for the argument
that online harassers and cyber bullies do not appear to discriminate
on the basis of their victim’s race. These results are not surprising
given the anonymity associated with most online behaviors. Individuals
may maintain long term relationships with people they meet online and
never know the other person’s race until a picture is transferred or
the individual’s race is discussed during a conversation. With this in
mind it is possible that perpetrators of online harassment may not
know, and may not care to know, their victim’s racial identity.
Hinduja and Patchin (2008) also found that there was no difference
between victimization rates for males and females, where the current
analysis supported Dilmac’s (2009) and Ybarra and Mitchell’s (2007)
findings in that female youth in the current analysis were more likely
to identify themselves as victims of online harassment (although
Ybarra and Mitchell did note that males were more likely to engage in
higher rates of victimization).
These findings concerning female
victimization could potentially be linked to the fact that females
generally view the spreading of gossip and rumors as a more legitimate
means of harassing other females. Past studies on female bullying have
found gossiping and the generation of rumors to be tools used more
often by female bullies (Boulton, Trueman, & Flemington, 2002; Liepe-Levinson
& Levinson, 2005). Harassment via electronic communications is more
verbal and not physical. As such it is possible that female victims of
online harassment are encountered at greater rates because
perpetrators may more often be female. It is also possible that
females engage in the behaviors that lead to victimization at greater
rates than their male counterparts, in that females are more likely to
engage in more hours of blogging, chatting, and other more high risk
Internet-related behaviors.
Although in the current analysis age
was not found to have a significant influence on reported
victimization, there were slight differences found between
victimization rates when respondents were broken down into age groups.
Youth in the current study who were of high school age (15 to 17) were
more likely to indicate that they were victims of online harassment.
These findings are similar to Ybarra and Mitchell’s (2007) finding
that frequency of harassment increases with age. Unlike traditional
schoolyard bullying, engaged in by youth on the playground, teenagers
may opt to use electronic communications to embarrass a victim rather
than use physical force to harm the individual. Older teenagers may be
more familiar with the technology and may have access to more
electronic communications tools, resulting in increased potential for
online harassment.
Examining Internet-related activities
is important in understanding potential online victimization. Several
studies have recently been conducted in order to test whether
increased online participation is linked to increased likelihood of
victimization. The results of these studies have found support for
routine activities theory as an explanation for online victimization,
finding that increased participation in a variety of online behaviors
has an impact upon victimization by harassers or online bullies
(Marcum, Higgins & Ricketts, 2010; Marcum, Ricketts & Higgins, 2010).
Routine activities theory argues that for crime or deviant behavior to
occur there must be a convergence of three factors: 1) a motivated
offender, 2) a suitable target, and 3) an absence of a capable
guardian. Each of these three factors can be examined in relation to a
variety of different online behaviors (Marcum, 2009).
In examining the specific Internet
activities that influence harassment victimization, past studies have
found that youth who engage in blogging-related activities are as much
as 2.5 times more likely to become a victim of online harassment
(Mitchell, Wolak, & Finkelhor, 2008). Results from the current
analysis provide some continued support for these results, with
respondents who engaged in blogging being 1.3 times more likely to
indicate being a victim of online harassment. As noted by other
researchers who have studied online blogging (Godwin-Jones, 2003;
Jordan, 2005), the activity brings individuals into contact with a
greater number of people who may or may not agree with the opinions
expressed in the blogs. The individuals who make up a blogger’s
audience may come from a variety of backgrounds and many of these
individuals may find themselves at odds with the blogger’s opinions
and views. When a disagreement occurs then the offended party may turn
to various electronic communication tools as a means of furthering the
disagreement.
Also found to increase the likelihood
of a respondent reporting victimization was the downloading of music
to a user’s personal computer or MP3 player. Respondents who
downloaded music were 1.6 times more likely to report being a victim
of online harassment. While there have been several studies to address
digital piracy and online file sharing (Higgins, 2007; Hill, 2007;
Hinduja, 2007), there has been little in these studies to indicate how
the behaviors could lead to online victimization. That being stated,
anecdotal evidence by these authors could provide some insight into
these findings. Many bit torrent websites, which are websites designed
to allow users to download music and movie files without charge, as
well as many other file sharing programs allow users to communicate
with others who are downloading files. Past experience has shown that
individuals who do not upload music or movie files are frowned upon
and may even be labeled by some file sharers as “leeches” because of
their desire to take from others without giving much new material back
to the group. Some websites may go so far as to remove individuals who
do not share files of their own. With this in mind it is possible that
youth who use these services are becoming introduced to a more
aggressive online element, many of which who may become upset with the
activities or behaviors of youthful users and therefore may engage in
online harassment of these youth. This is especially true if the young
person is a frequent file sharer. Certainly, future studies on digital
file sharing should examine online communities in an effort to
determine how applicable this argument could be to the problem of
online harassment.
While blogging and downloading music
were found to be significant predictors of online harassment
victimization, the two best predictors in the current analysis
involved synchronous communication tools. The use of chatting software
was found to be the second best predictor of online harassment
victimization, with respondents who used chat software being 2.1 times
more likely to identify themselves as being a victim of online
harassment. These findings are consistent with what others who have
studied cyberbullying and online harassment have found (Ybarra &
Mitchell, 2004; Patchin & Hinduja, 2006). Because the use of these
programs introduces youth to a variety of individuals with varying
personalities it stands to reason that they could interact with
someone who does not agree with their comments, ideas, or shared
beliefs. It is for these same reasons that it was not surprising that
the best predictor of online harassment victimization was the use of
instant messaging programs, with users of the technology being almost
3 times more likely to report victimization if they used instant
messaging programs. As the number of persons that a youth interacts
with online increases, their chances of coming into contact with
someone who develops a level of dislike or anger for them also
increases. If the anonymity factor is present then there is the
development of a situation in which two or more parties could become
embroiled in a heated discussion or debate that could lead to one or
more of the parties becoming a target for future harassment.
Perhaps the most important contribution
of this analysis lies in the examination of parental regulation
factors that protect young people. Websites and training materials
often recommend that parents take an active role in protecting their
children from online harassment by ensuring that the use of the
computers are monitored and regulated. However, findings from the
current analysis support an argument made by Marcum, Higgins, and
Ricketts (2010) that regulatory behaviors do not impact online
victimization. At first these findings may be confusing given that so
much focus in the past has been on regulation and monitoring of
Internet activity. However, if we examine the one variable that was
found to be significant in the second model (number of computers owned
and used by respondents) then at least one possible explanation can be
found. Parents may maintain software controls on their children’s
computers and they may develop and regulate the rules of those devices
being used. However, the fact that today’s cellular phones allow for
more online activities, as well as the fact that there are more
places that youth can gain access to computers, means that such
programs and rules at home may have little or no impact on behaviors
outside the home.
Take for example the current
capabilities of smart phones. Blackberry has its own built in instant
messenger program, and while the program requires users to add
contacts before they can communicate there are many youth on social
networking sites who post the access codes to their blackberries and
other Smartphone devices. This means that anyone who can see the young
person’s page can get the access code and add that person to their
instant messenger program. Youth would then have to approve the new
contacts but if they post the information on a public page or public
profile page then it would stand to reason that they may approve
almost any request for a new contact connection. Also, even if a youth
is monitored at home that does not mean that they will be monitored at
school or at their friend’s house or at the local coffee house that
provides Internet connectivity.
With this in mind the results of the
current analysis support a belief that perhaps it is time to begin
re-evaluating how to address online harassment and cyberbullying. The
answer may not lie in regulating our children’s access to these
programs or even regulating access to the Internet. After all, not all
Internet activities were found to impact a young person’s likelihood
of becoming a victim. Individuals who used the Internet to gather
information on news, movies, or educational materials may not expose
themselves to the types of individuals who would engage in online
harassment. If the answer does not lie in physical regulation, and
the use of computer-based regulation is also not very effective, then
the answer may lie in education and awareness. Given that this
analysis, as well as others, have found some support for the role of
age in victimization it is recommended that educational programs not
be generic and instead be customized to particular ages (Wolak,
Mitchell, & Finkelhor, 2007) and gender. Making young people more
aware of the potential long term consequences associated with engaging
in these behaviors, could potentially increase young people’s
understanding of the behaviors and thereby increase reporting of
victimization (Cassidy, Jackson, & Brown, 2009). Then, it would be up
to adults to respond to these complaints and work with children to
minimize any harm that comes from online harassment.
Limitations and Conclusion
The current analysis was conducted on
data that was collected by the Pew Internet and American Life project.
As such we had little control over the questions that were asked and
the methods associated with the collection of data. However, the
techniques used by Pew Internet and American Life were very similar to
techniques employed by other researchers who have conducted research
on the topic of online harassment (specifically see Ybarra & Mitchell,
2004a, 2004b; Wolak, Mitchell, & Finkelhor, 2007; Mitchell, Wolak, &
Finkelhor, 2008). Further, this methodology resulted in a relatively
representative sample in terms of gender, regional location, family
income, and age. Future studies on online harassment could benefit
from an increased focus on youth Internet users of varying races. The
vast majority of the current sample was White and therefore results
should be interpreted accordingly.
The current analysis provided support
for several past studies on online harassment, while providing insight
into new areas for future research. Using Internet software to engage
in chatting and blogging activities increased the likelihood of online
harassment victimization in the current study, while age, race, and
family income had little impact on reported victimization. Further,
youth who used the Internet to engage in activities that introduce
them to more people or introduce them to a more deviant group appeared
to increase their likelihood of becoming a victim of online
harassment. On the other hand, activities that are more private and
related to activities of education or awareness, such as reading
online news articles or using the Internet to shop for materials goods
appeared to have little effect on a young person’s likelihood of
reporting online harassment. Of perhaps greater importance was the
finding that none of the parental regulation factors commonly
discussed in relation to prevention of online harassment were found in
the current analysis to have any impact on online harassment. It is
argued here that future solutions to the problem should seek to ensure
that young people are more aware of the consequences of their
behaviors and more aware of how to properly use the various Internet
technologies available to them. The answer to regulating these
behaviors appears to lie less in the area of enforcement and perhaps
more in the area of education.
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_______________________________________
Assistant Professor of Criminal Justice, Division of Criminal Justice and Social Sciences, Troy University, Troy, Alabama, United States of America. Email: RMoore8668@troy.edu
Graduate Assistant, Division of Criminal Justice and Social Sciences, Troy University, Troy, Alabama, United States of America. Email: Nguntupalli@troy.edu
Assistant Professor of Criminal Justice, Department of Sociology, Anthropology, Social Work and Criminal Justice, University of Tennessee – Martin, Martin, TN, United States of America. Email: Tlee@utm.edu
