1. Top of page
  2. Abstract
  3. Introduction
  4. Data Collection & Analysis
  5. References

Building on previous research in computer-mediated communication, social and communication networks, and adolescent development, this article raises three issues regarding adolescent use of socially interactive technologies (SITs) and their relationship to offline social networks: 1) whether adolescents are creating more, but weaker ties using SITs, 2) to what extent adolescent SIT-facilitated networks overlap with friendship networks, and 3) whether SIT relationships are important for adolescents who have fewer offline peer ties. In order to investigate these questions, network data collection and analysis were integrated with more traditional questionnaire methodology and statistical analysis. The results show that the adolescents in the study were not creating more ties using SITs, nor were they necessarily creating weaker SIT-based ties; that there was little overlap between SIT-facilitated and offline social networks; and that socially isolated adolescents were less likely than other adolescents to use SITs.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Data Collection & Analysis
  5. References

Socially interactive technologies (SITs), such as instant messaging and text messaging, are beginning to redefine the social networks of today’s youth. By offering fast-paced, inexpensive, online communication, SITs allow for new online youth social networks to form and evolve. These online networks, in turn, may affect the offline social and friendship networks in which youth are immersed.

Much has been said about the prevalence of technology in the lives of adolescents. Reports in the press and surveys from parents find points of view that range from exuberant—discussing how socially interactive technologies can save youth from social isolation and depression—to alarming—focusing on how constant use of these technologies fosters antisocial behavior (Turow, 1999). The reality, of course, lies somewhere between these two extremes. As with the adoption and use of any other technology, there are a variety of factors that affect how SITs are used on an individual level, as well as group dynamics that come into play. This article focuses on both of these aspects of SIT use within one of the most influential networks in youths’ lives: the peer, or friendship, network.

Previous research on youth and SITs has tended to focus on who is using the technology and why, employing either in-depth ethnographic data with relatively small sample sizes (Eldridge & Grinter, 2001; Grinter & Eldridge, 2001, 2003; Grinter & Palen, 2002), or larger questionnaires focusing on basic user data (Lenhart, 2003; Lenhart, Madden, & Hitlin, 2005; Lenhart, Rainie, & Lewis, 2001). The main findings of such research have been threefold. First, youth are using SITs to enhance communication among friends and family, to make plans with one another, and to maintain social contact outside of their day-to-day face-to-face conversations (Grinter & Eldridge, 2001, 2003; Grinter & Palen, 2002; Lenhart, Madden, & Hitlin, 2005; Lenhart, Rainie, & Lewis, 2001; Schneider & Hemmer, 2005; Valkenburg & Peter, 2005). Second, these technologies have been adopted by teens relatively quickly because IMing and text messaging are more convenient, less expensive (especially in some countries), and faster than traditional technologies. The ability to time-shift and talk at nontraditional times are added incentives (Grinter & Eldridge, 2001; Kasesniemi & Rautianinen, 2002; Lenhart, Madden, & Hitlin, 2005; Lenhart, Rainie, & Lewis, 2001; Ling & Yttri, 2002). Finally, research in this arena has shown that although preference for using SITs to communicate is definitely on the rise, and the use of SITs has surpassed that of email in the past year, youth still tend to hold in-depth, important conversations offline (Grinter & Eldridge, 2003; Lenhart, Madden, & Hitlin, 2005).

Such research is vital to preliminary understandings of a new technology’s usage. However, it does not delve into the heart of some of the more interesting questions, such as what group dynamics influence youth to adopt particular technologies or to use them in a particular manner, or how using these technologies actually affects how children and adolescents communicate with one another. For example, do youth use these less rich media technologies to obtain emotional, psychological, and other forms of support from their peers? Do SITs reflect the same friendship networks that already exist? Part of the issue is that although social groupings of adolescents are often mentioned as being an important part of online and offline communication, research looking at social networks is relatively uncommon. Moreover, the few studies that have been conducted on the social networks facilitated by SITs have not collected or analyzed social network data (Kavanaugh, Carroll, Rosson, Zin, & Reese, 2005; Schneider & Hemmer, 2005); nor is there any network data or analysis in research on adolescent use of these technologies. Network approaches can be used to understand the communication dynamics of an entire network (e.g., a group of friends at school or in a chat room), of subsets of a network (e.g., a clique of “popular” kids at school and how they affect the network as a whole), and of individuals within the networks (e.g., early adopters of instant messaging). For this reason, network analysis is an important perspective to employ.

Another area of research that is underdeveloped concerns the effects of socially interactive technologies on teen and preteen individuals (Livingstone & Bober, 2005). The inclusion of preadolescents and adolescents is important because they incorporate technology-mediated communication more strongly into their social lives than do adults (Brown, Mounts, Lamborn, & Steinberg, 1993; Madden & Rainie, 2003). Moreover, although there has been considerable research about email communication and instant messaging, there has been relatively little research on text messaging. This is surprising since the low-cost, mobile nature of text messaging has made it very popular among adolescents in many areas of the world (Eldridge & Grinter, 2001; Grinter & Eldridge, 2001, 2003; Grinter & Palen, 2002). It appears as though youth may have similar social uses for text messaging as they have for instant messaging (IM), email, and mobile phones; text messaging may often be used in conjunction with these other technologies in multitasking (Lenhart, Madden, & Hitlin, 2005). The Pew Internet & American Life Project identified text messaging as an important future direction for research (Lenhart, 2003); the most recent report issued by the Project is the first to include this technology (Lenhart, Madden, & Hitlin, 2005).

This article addresses each of these concerns by integrating network theory, data collection, and analysis with research on adolescent SIT use to examine the types of ties adolescents are creating online and offline, and how those two types of relationships correlate. In order to address these issues, we build off of previous research in computer-mediated communication, social and communication networks, and adolescent development to generate a set of research questions that we begin to address through the presentation of our research findings. As one of the key thrusts of this article is to emphasize the need for network research in the area of adolescent technology use, we conclude with a discussion of the benefits and the challenges of this type of research.

Overlapping Networks: The Strength of Online Versus Offline Peer Ties

Young people’s use of technology to communicate with one another is certainly nothing new; consider the telephone in the 1950s and 1960s. What has changed in the past decade, however, is the form that communication takes. New text-based technologies are picking up where phones left off. Email and text messaging allow for rapid, asynchronous communication within one’s peer network; IM allows for synchronous communication among many friends at once. Moreover, these SITs are relatively inexpensive, especially when used to contact friends who would normally be a long-distance or international call away.

Adoption of socially interactive technologies is high among adolescents. Aside from email, the most often used Internet tool for peer communication is instant messaging. This is also a youth-preferential activity, with 74% of online adolescents in the U.S. having used instant messaging, compared with 44% of online adults (Lenhart, Rainie, & Lewis, 2001). Research in the U.K has produced similar findings (Livingston & Bober, 2005). Moreover, those youth who IM tend to do so regularly. In 2005, 65% of American teens, and 75% of American teens who were online, used IM (Lenhart, Madden, & Hitlin, 2005). Nearly half of teens who IM use it everyday. Most youth who IM use this application most regularly to maintain relationships, either with friends or family members, especially those that do not live nearby (Lenhart, Rainie, & Lewis, 2001). Gender-wise, girls use IM as a venue for socializing more than do boys (Jennings & Wartella, 2004). Moreover, although text messaging has been gaining popularity with teens, only one-third of American teens report sending text messages (although that number rises to 64% if one considers only teens who have mobile phones) (Lenhart, Madden, & Hitlin, 2005).

Today’s youth do not necessarily feel that using the Internet, email, IM, and text messaging takes time away from their friendships. Instead, many consciously use the Internet and SITs to influence their peer networks. According to a recent U.S. study, 67% of the youth surveyed felt that the Internet only helps “a little” or “not at all” when trying to make new friends (Lenhart, Rainie, & Lewis, 2001). In contrast, 48% of the respondents said that they use the Internet to improve their relationships with friends, and 32% said that they use the Internet to make new friends (Lenhart, Rainie, & Lewis, 2001). On the one hand, this supports the optimistic perspective that online communication promotes social support and expanded social interaction (Cole & Robinson, 2002; Katz & Rice, 2002; Kavanaugh, et al., 2005; Kestnbaum, Robinson, Neustadtl, & Alvarez, 2002) rather than isolation and depression (Kraut, Patterson, & Lundmark, 1998; Nie, Hillygus, & Erbring, 2002).1 On the other hand, it may also support Ito and Daisuke’s (2003) argument that adolescents are substituting poorer quality social relationships (weak ties) for better ones (strong ties).

There is some evidence to support this latter line of reasoning. Chan and Cheng (2004) found significant differences between relationships that are formed through computer-mediated communication and relationships that are formed offline, at least in the early stages. Online relationships are characterized by less depth, although this difference diminishes as the relationships continue to grow (Chan & Cheng, 2004). Moreover, if an individual belongs to an online or other community in which s/he forms computer-mediated relationships, s/he may eventually learn socially situated community norms that make the development of relationships easier and may increase the depth of relationships created online (Riva, 2002).

It seems likely that relationships that exist only over the Internet will have less depth but will provide connections that are external to the participants’ already existing social networks. In other words, people using the Internet will create less strong relationships, but there will be more of them. Two concepts in the existing social network literature that explain the existence of such ties are Granovetter’s weak-tie relationships (1973, 1983) and the concept of bridging (as opposed to bonding) relationships (Lin, 2001). Weak ties are considered to be acquaintances, as opposed to strong ties that might be close friends or family members. People who have more weak ties as part of their social network are likely to have access to greater amounts of information, because the weak ties will bring in novel information (whereas their strong ties are likely to have duplicate information) (Grannovetter, 1973, 1983). Thus online relationships, which are generally less strong than offline relationships, could provide adolescents with increased information and may enlarge their perspective on the world around them. This, of course, could be both a positive and a negative experience.

A bridging relationship involves an individual who is outside an individual’s usual interpersonal network. This relationship may involve a higher level of heterogeneity (i.e., the person in the bridging position may not be as similar to the individual as the individual’s usual friends) and a lower level of emotional intensity than a bonding relationship, which involves a close interpersonal relationship with emotional intensity and sharing (Lin, 2001).

The concepts of weak ties and bridging relationships are similar to what adolescents often experience in SIT-based relationships, at least according to the anecdotal evidence put forth in the mainstream media. The concern often expressed is that as adolescents spend more time using SITs to form relationships, they will create a greater number of relationships but these relationships will not provide the social support that strong, offline relationships provide. What has not been clear thus far is whether this trade-off between the number and depth of relationships is occurring. We therefore ask the following research question:

RQ1: Are adolescents creating more, but weaker, ties using SITs?

Do I Know You from Somewhere? How SIT and Offline Relationships Overlap

Another interesting, and thus far largely unaddressed, issue in the literature is that of the relationship between offline and online friendships. If there is high correlation between offline friendship networks and online SIT networks, we can assume that the online ties are mapping onto and strengthening the offline ties. If, however, there is not strong correlation between the networks, then the adolescents are looking outside their friendship network for communication partners and possibly social support. Moreover, because we are interested in the relative value of these ties (e.g., the closeness of friendship ties, the frequency of IM communication), we need to look at whether the ties are of corresponding strength. In this analysis, we examine the use of instant messaging and text messaging (or short-message-service/SMS) and their correlation with offline friendships.

Instant messaging and text messaging are both forms of technology-mediated communication that provide a way for individuals to communicate with one another and to create and reinforce social ties and friendships. Text messaging, however, is different from IM and many other forms of CMC because it is not anonymous. Because text messaging is usually facilitated through mobile phone technology, it is difficult to obtain a telephone number from an individual without at least having met the person or knowing their first name. Additionally, some research suggests that the use of text messaging may be perceived as a form of socially acceptable gift (Taylor & Harper, 2003). This would imply that individuals who engage in this type of behavior share a set of norms that would indeed make the exchange of text messages a gift, thus reinforcing the idea that text messaging is generally utilized to strengthen the preexisting network of an individual.

How adolescents use these SIT relationships to broaden and/or deepen their social networks remains unclear. Because it is common for adolescents to utilize SITs as a form of relationship maintenance and day-to-day communication (Gross, Juvonen, & Gable, 2002; Kreager, 2004; Wolak, Mitchell, & Finkelhor, 2003), we would expect users’ friendship networks to overlap significantly with their SIT communication networks. On the other hand, if youth are using these technologies to develop new relationships and create romantic relationships (Gross et al., 2002; Kreager, 2004; Wolak et al., 2003), we may see less overlap between the two. In order to understand better the dynamics between these networks, we ask the following research question:

RQ2: To what extent do adolescent SIT communication networks overlap with their friendship networks?

The Wallflower Becomes the Life of the Online Party?

Most of the previous discussion has focused on adolescents who have strong offline relationships. However, there is a second group of adolescents who describe themselves as having fewer or less deep friendships (Kreager, 2004). These more isolated youth may utilize IMing and text messaging to fulfill different needs than individuals who utilize SITs to strengthen existing relationships. These SIT-based relationships may provide essential social support and camaraderie for otherwise isolated youth, which are particularly vital during this stage of social development. Whether or not such relationships are being formed, however, is not clear from the current literature. Therefore, we ask:

RQ3: Are SIT-based relationships important for adolescents who have fewer offline peer ties?

Data Collection & Analysis

  1. Top of page
  2. Abstract
  3. Introduction
  4. Data Collection & Analysis
  5. References

The data for this article were collected from seventh-grade students at a middle school in a midwestern college town in the United States. All of the students were given questionnaires about their use of different media, focusing on their use of instant messaging, text messaging, and other technology (computer, Internet, email, television, telephones, etc.). In addition, the questionnaires asked the students whom they consider to be their friends and, if they use SITs, with whom they IM and text message. There were 40 respondents to the questionnaire, all of them between 11 and 13 years old. Eleven of the respondents were male and 29 were female.

For the open-ended friendship network questions, the participants were asked to list up to 25 people and then to identify those people as “close friends,”“good friends,” or just “friends.” For the IM and text message networks, they were asked to list up to 25 people with whom they communicate using each of these technologies and then to differentiate among those people with whom they communicated “most often,”“often,” or “occasionally.” Finally, the questionnaire asked the participants how they view these SITs as fitting within the social and emotional spheres of their daily life. The questionnaires thus yielded three types of data: 1) a set of three self-report ego-networks (peer, IM, and text message networks) for each participant, 2) self-report data regarding media usage and adoption that was used as attribute data for each of the participants (or nodes in the networks), and 3) self-report data regarding feelings of social isolation/belonging and social support. These data were coded and analyzed using network analysis software (UCINet), as well as more traditional statistical analysis methods.


General User Data

Although the primary focus of this analysis is on the network data, it is important to understand the general media and SIT environment of the participants in this research. All participants indicated that they had a television at home, and 94.7% also had a computer at home. Participants spent, on average, over four more hours per week watching television (14.55 hours) than using a computer (10.37 hours). After home use, the most popular places for using the computer were school (90.0%), the library (42.5%), and at the home of a friend or family member (25.0%). While they are online, the participants in the study spend time surfing the Internet (87.5%), working on homework (85.0%), playing computer games (85.0%), sending and receiving email (80.0%), and instant messaging (60.0%), among other activities.

On average, study participants who IM spend 2.2 hours per day online with this technology. Among the most popular reasons for IMing were to “keep in touch with friends” (92.0%), followed closely by to “make plans with friends” (88.0%). Other uses with more negative connotations included using IM to “play a trick on someone” (60.0%), to “write something you wouldn’t say in person” (42.0%), and to “break up with someone” (24.0%). See Table 1 for the complete list of IM activities.

Table 1.  What the participants do on IM
Keep in touch with friends92.0%
Make plans with friends88.0%
Play games with IM software61.5%
Play a trick on someone60.0%
Ask someone out44.0%
Write something you wouldn’t say in person42.0%
Send non-text information38.5%
Break up with someone24.0%

Text messaging was not as popular as IMing among our participants. Those who text message average only 2.82 hours per week on this activity. Adoption rates were similar for both SITs, however. Over 65% of participants have been using both instant messaging and text messaging technologies for more than one year. Only 3.8% had adopted either technology within the past month.

Data on social isolation and belonging were also gathered. Ninety percent of the participants indicated that they have “lots of friends,” while only 10.0% designated that they have “a few friends” or “no friends.” When asked about the intensity of their friendships, 42.5% of participants indicated they have “lots of close friends,” 52.5% have “a few close friends,” and 5.0% listed having “no close friends.” Mediated methods of communicating with friends, other than IMing and text messaging, included telephone (65.0%), email (35.0%), and chat rooms (10.0%).

Finally, participants reported a wide range of variation across all three forms of network data. When asked about friendship networks, the average number of friends listed was 17.33, with a range of 0 to 27,2 and the average number of close friends was 6.23, with a range of 0 to 16. When asked about IM networks, the 23 youths who currently use IM had an average of 12.04 people with whom they IM, with a range from 1 to 25 (the maximum allowed by the questionnaire), and an average number of frequent IM partners of 2.39, with a range of 0 to 5. In addition, over 42% of IM users indicated between one and 20 people on their IM “buddy list,” while 15.2% declared over 80 IM partners. Finally, when asked about text messaging networks, of the eight youths who currently text message, the average number of people with whom they do so was 8.63, with a range of 1 to 25 (the maximum allowed by the questionnaire), and an average number of frequent text message partners of 1.63, with a range of 0 to 5.

Network Data

In order to investigate the three research questions mentioned above, we conducted three separate analyses.

RQ1: Are adolescents creating more, but weaker, ties using SITs? The first part of this analysis looks at the total number of ties being created through the various forms of communication. We performed a pair-samples t-test between each pair of networks, looking at the total number of ties within the network and different forms of communication. There was a significant difference between total number of friends listed and total number of IM partners (t(df = 37) = 7.151, mean = 16.99, p < 0.001). There was also a significant difference between total number of friends listed and total number of text messaging partners (t(df = 6) = 3.390, mean = 7.11, p = 0.015). In both cases, however, number of friends was greater than the number of SIT-based relationships. There was no significant difference between total number of IM partners listed and total number of text messaging partners. Thus there is a significant difference between the two SIT forms of communication and interpersonal friendship networks, but not in the way previous research has suggested.

In order to address relational intensity of SIT communication relationships as compared to offline relationships, a paired-sample t-test was run comparing the intensity between each of the three types of relationships. Relationship intensity was defined as the average of the number of people the participant listed as close friends (or communicated with most frequently via IM or text messaging), divided by the total number of friends (or SIT partners) listed. This yielded a measure of intensity where participants’ friendship intensity or SIT intensity ranged from 0 to 1, with 1 being a very intense network where all friends were indicated to be close friends. There was no significant difference in relational intensity between friendship networks and text messaging networks, between friendship networks and IM partner networks, or between IM partner networks and text messaging networks. This implies that there is no significant difference in intensity between any of the network types.

RQ2: To what extent do adolescent SIT-facilitated networks overlap with their friendship networks? In order to test the relationship between the social networks with different forms of communication, we analyzed each participant’s valued ego-networks using quadratic assignment procedure (QAP) correlation analysis. QAP analysis calculates internetwork comparisons using Pearson’s correlation coefficient between corresponding cells of two matrices. It then permutes the rows and columns of one of the matrices and correlates it with the other matrix, repeating this process hundreds of times to calculate how often the random correlation is greater than (or equal to) the original correlation. A low proportion (< 0.05) indicates a strong relationship between the matrices (Borgatti, Everett, & Freeman, 2002).

Because we gathered valued network data, we were able to analyze these data in two ways. First, we dichotomized the data and correlated the networks in order to see if there was general overlap between the people listed on the pairs of networks. Overall, there was little correlation between the dichotomized friendship network and the 2 SIT networks. Only nine significant relationships were found across 23 participants who use IM and have friendship networks. The average correlation was −0.249, with a range of correlations between −0.801 and 0.567. Among the eight participants who use text messaging, no significant relationships occurred between text messaging and friendship networks. The range of correlations between these relationships was −0.376 and 0.281, with the average correlation being −0.190. Finally, across the eight participants who use both text messaging and IM networks, there were two significant relationships, with a range of correlations between −0.354 and 0.659. The average correlation was 0.008. Therefore, there is little overlap among the three networks.

The second QAP analysis looked at the valued data for evidence of a correlation between the strength of the relationships that participants had with each person in the different contexts. Again, we found little correlation between the strength of the online and offline relationships. Only four significant relationships were found across 23 participants who use IM and have friendship networks. The average correlation was 0.053, with a range of correlations between −0.674 and 0.529. Of those participants who text message, there was only one significant relationship with the friendship networks. The range of correlations between these relationships was −0.382 and 0.538, with the average correlation being −0.061. Finally, just as with the dichotomized data, there were two significant relationships between text messaging and IM networks, with a range of correlations between −0.278 and 0.924. The average correlation was 0.181. See Table 2 for a summary of these results.

Table 2.  Relationship between friendship and SIT networks
Networks correlatedAverage dichotomized correlation% of participants with significant dichotomized correlationAverage valued correlation% of participants with significant valued correlation
Friend/IM−0.24939% (78% negatively correlated)0.05317% (25% negatively correlated)

RQ3: Are online relationships important for adolescents who have fewer offline peer ties? The literature suggests that relatively isolated individuals might turn to SIT-based communication to augment their social interaction. Of the people who reported having friends, but have 10 or fewer friends, only 36% use IM, compared to 72% of people with more than 10 friends. For text messaging, 27% of the people who reported having friends, but have 10 or fewer friends, currently use text messaging, compared to 24% who have more than 10 friends. None of the adolescents who responded that they have few or no close friends used instant messaging or text messaging. Therefore, IM does not seem to provide an alternative source of social support for people who are more isolated within their peer network.


In response to research question 1, the participants in this research project do not seem to be creating either more or weaker ties using SITs. At first glance, this seems to contradict the literature on online relationships cited earlier, which says that people are substituting poorer quality online social relationships (weak ties) for better offline ones (strong ties). However, we first need to remember that the literature on online ties has thus far focused primarily on adults, not on youth, who may have integrated technology more seamlessly into their social lives. Second, when we look at how long the respondents have been using the technology, we see that 85% have been using IM for more than six months (and 65% have been using it for more than a year), and that 50% have been using text messaging for more than six months (with 30% using it for more than a year). The fact that the technology is no longer novel may mean that the desire to go online to create new relationships may also have dissipated. This finding is also supported by the finding of the Pew Internet Project that 67% of youth do not think that using the Internet is helpful in creating relationships (Lenhart, Rainie, & Lewis, 2001).

The analysis for research question 2 yielded similar results. For the participants in this study, very little overlap was found between their offline friendships and their SIT-based relationships. As regards whether the same ties existed in both the friend network and the IM communication network, only 39% of the participants had a significant correlation between the two networks. Looking at the similarity in the values of the relationships, we see an even greater difference, with only 17% of the participants’ networks significantly correlated. These results, considered together, show that even when there was overlap between the networks, which occurred relatively infrequently, the ties were of different strengths. Thus a respondent might list someone as a close friend, but only as an occasional IM partner.

In addition, it is interesting to note that although little significant difference was found between the networks, 78% of the significant dichotomous relationships were negative. This suggests that the participants in this study are unlikely to have the same friends online as offline. In conjunction with the low overlap found using the valued data, it suggests that they are more likely to spend time talking to acquaintances online. Although this may seem to be at odds with previous research on youth and SITs, it makes sense if one considers that youth still use technologies like the telephone to have in-depth conversations; presumably they would be more likely to have such conversations with their close friends.

An important issue to highlight regarding these results is that, particularly in this sample, the participants may be using IM but their friends may not. Although 60% of the participants who chose to complete the study use IM, the adoption rate may be lower in the general population. Without complete network data, which include attribute data as to whether the friends listed have adopted the same technology, we cannot know whether this is a mediating factor.

In the same way, the lack of correlation between friendship and text messaging networks suggests that the participants in this study are not text messaging their friends, and the slight overlap between IMing and text messaging suggests that they are using different SITs with different friends. This finding seems to be due primarily to the low adoption rate for the technology in this sample. Further research, possibly among older adolescents who are more likely to have their own mobile phones, should provide better data in these areas.

Coupled with the findings for research question 1, these network results indicate that the adolescents in this study were not creating more ties using SITs, were not necessarily creating weaker SIT-based ties, nor were they creating the same strength of tie across the three social networks. These findings point to a very complex set of social dynamics that requires further study. At the same time, they show that network data and analysis provide a useful lens for looking at teens’ online and offline interactions that may reveal aspects of those interactions that are not otherwise evident.

Finally, the sample for this project included a group of adolescents who have been targeted in recent years by the media and mental health professionals as being “at risk” for antisocial (even violent) behavior: the loners (or social isolates). Social support perspectives on new technologies purport that those who have not found many or strong friendship ties in their everyday, offline life may use online communication as a way of increasing their social networks (and thereby increasing their level of social support). The findings of this study, however, call into question this scenario for this group of adolescents. Instead of creating new SIT-based relationships, the socially-isolated youth in this study are not creating any SIT relationships at all (or very few). This finding is in line with the results for research question 1, which show that the study participants are not creating more or stronger SIT-based ties, but the results for this particular group are especially stark. In particular, their text messaging relationships are similar to their more “popular” peers, but the IM relationships are markedly different. This may point to different uses for each of these technologies, but further research is necessary to understand the disparity between the relationships formed using them.

The data collection and analysis presented in this article are not without limitations. The key limitation is the small number of participants, particularly those who use text messaging. The relatively low number of participants in this study is due to several factors. The first is that network data collection is more labor-intensive than traditional questionnaire data and that the collection of free recall ego network data, as were used in this study, is even more tedious for the participants. This inherent lack of user-friendliness in the methodology curtails response rates, particularly in populations that are not being coerced to participate by some higher authority. The students who participated in this study were eligible for a prize drawing for three $50 Best Buy gift certificates. In the future, it may generate more participation to offer a less valuable compensation that all respondents would receive, such as a movie pass.

In addition, the content of the questionnaire required participants to list their friends by name, so that the researchers could then code the social network data across the three interaction types. Because peer pressures are so intense during adolescence, the possibility that someone might know who you listed as your friends may have discouraged people to participate. This issue was raised when the researchers received a phone call from a parent and an email from a prospective participant alluding to this concern.

Moreover, the process of completing the questionnaire was complicated by the need to obtain parental consent. In order for students to fill out and return their questionnaire they had to take their questionnaire packets home for their parents to sign, fill them out, and then remember to bring them back to school to turn them in. This process, although necessary because the participants were minors, created multiple opportunities for the questionnaire not to make it back to the researchers.

Finally, as mentioned above, our set of respondents included very few text message users. This may be because of the age group that was being targeted. Adolescents 11 to 13 years old may not have their own mobile phones, and therefore may not have access to text messaging capabilities. They are relatively likely, however, to have access to a computer and therefore to IM. Future research should increase the overall number of participants or focus on a slightly older cohort in order to gather more complete text message data.

Future Directions

The results of this research point to a very complex dynamic between offline and SIT-based friendships. In order to garner a more complete understanding of these interactions we need overtime network data from a complete adolescent network. The overtime aspect of the data would allow us to see how these networks interact and coevolve. This may point to interesting and varying phenomena at different points in time, as well as yield some insight as to whether one network is the driving force for the other. Of course, the composition of the network of adolescents may change over time, complicating the data.

Moreover, we need to have access to complete network data. This is a particularly difficult issue. In the first place, friendships and SIT-based relationships are not geographically static; the people whom the adolescents will list as friends and SIT partners will not be contained within the population of participants. In preliminary data coding for this project, the ego-network from the 40 nodes garnered a complete network with over 500 nodes. If one were able to gather these data from an entire high school, one would have an unworkable number of nodes. Moreover, the sample would still not contain the ego network data for those friends and SIT partners who were not part of the population.

Another possibility would be to constrain the networks artificially. For example, one could create an online network data collection tool that would only allow participants to choose others within the population (e.g., all of the students at a high school) to list as part of their network. Although this would create a manageable set of network data, it would be artificial. In addition, part of the interest in the overlap between these networks is whether youths are going outside their everyday friendship networks for social support. Data for a constrained network would not provide information on those types of friendships.

This study was a first attempt to address these issues and to create a set of manageable, ego-network data for analysis. A vast amount of work remains to be done in this area. Technology is pervasive, and has become an integral, if not overpowering, part of the lives of today’s youth. By better understanding the interactions between the two, we can use these technologies more constructively to enhance the lives of young people.

  • 1

    That is not to say that SIT use has no negative effects. Bullying via SITs has become a problem worldwide (Magid, 2001; National Children’s Home, 2005), and SIT use in the classroom has become problematic, with students using the technologies to “pass notes” and cheat on exams (Bulliet, 2005; Magid, 2001).

  • 2

    Two participants listed more than the maximum number of friends (one listed 26 and the other 27).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Data Collection & Analysis
  5. References
  • Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for Social Network Analysis (Version 6.15) [Computer software]. Harvard: Analytic Technologies.
  • Brown, B. B., Mounts, N. S., Lamborn, S. D., & Steinberg, L. D. (1993). Parenting practices and peer group affiliation in adolescence. Child Development, 64(2), 467482.
  • Bulliet, M. (2005, March 30). School sneaks use gadgets to chat & cheat. New York Post, p. 3.
  • Chan, D.K.-S., & Cheng, G.H.-L. (2004). A comparison of offline and online friendship qualities at different stages of relationship development. Journal of Social and Personal Relationships, 21(3), 305320.
  • Cole, J., & Robinson, J. P. (2002). Internet use and sociability in the UCLA data: A simplified MCA analysis. IT & Society, 1(1), 202218.
  • Eldridge, M., & Grinter, R. (2001, April). Studying text messaging in adolescents. Paper presented to the Workshop on Mobile Communications: Understanding Users, Adoption & Design at the Conference on Human Factors in Computing Systems (CHI), Seattle, WA. Retrieved January 21, 2006 from
  • Granovetter, M. S. (1973). Strength of weak ties. American Journal of Sociology, 78(6), 13601380.
  • Granovetter, M. S. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1, 201233.
  • Grinter, R. E., & Eldridge, M. A. (2001). y do tngrs luv 2 txt msg? In W.Prinz, M.Jarke, Y.Rogers, K.Schmidt, & V.Wulf (Eds.), Proceedings of the Seventh European Conference on Computer Supported Cooperative Work, 16–20 September 2001, Bonn, Germany (pp. 219238). Dordrecht, Netherlands: Kluwer Academic Publishers.
  • Grinter, R. E., & Eldridge, M. A. (2003). Wan2tlk?: Everyday text messaging. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 441448). New York: ACM Press. Retrieved January 21, 2006 from
  • Grinter, R. E., & Palen, L. (2002). Instant Messaging in teenage life. Proceedings of the ACM Conference on Computer Supported Cooperative Work (pp. 2130). NY: ACM Press. Retrieved January 21, 2006 from
  • Gross, E. F., Juvonen, J., & Gable, S. L. (2002). Internet use and well-being in adolescence. Journal of Social Issues, 58(1), 7590.
  • Ito, M., & Daisuke, O. (2003). Mobile phones, Japanese youth, and the re-placement of social contact. Retrieved January 21, 2006, from
  • Jennings, N., & Wartella, E. (2004). Technology and the family. In A. L.Vangelisti (Ed.), Handbook of Family Communication (pp. 593608). Mahwah, NJ: Erlbaum.
  • Kasesniemi, E.-L., & Rautianinen, P. (2002). Mobile culture of children and teenagers in Finland. In J. E.Katz & M.Aakhus (Eds.), Perpetual Contact: Mobile Communication, Private Talk, Public Performance (pp. 170192). Cambridge: Cambridge University Press.
  • Katz, J. E., & Rice, R. E. (2002). Social Consequences of Internet Use: Access, Involvement and Interaction. Cambridge, MA: MIT Press.
  • Kavanaugh, A., Carroll, J. M., Rosson, M. B., Zin, T. T., & Reese, D. D. (2005). Community networks: Where offline communities meet online. Journal of Computer-Mediated Communication, 10(4). Retrieved January 21, 2006, from
  • Kestnbaum, M., Robinson, J., Neustadtl, A., & Alvarez, A. (2002). Information technology and social time displacement. IT & Society, 1(1), 2137.
  • Kraut, R., Patterson, M., & Lundmark, V. (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist, 52(9), 10171031.
  • Kreager, D. A. (2004). Strangers in the halls: Isolation and delinquency in school networks. Social Forces, 83(1), 251290.
  • Lenhart, A. (2003). Adolescents, parents and technology: Highlights from the Pew Internet & American Life Project. Paper presented to the Lawlor Group, Long Beach, CA.,ParentsandTechnology-Lawlor10.03.03a.nn.ppt [no longer available]
  • Lenhart, A., Madden, M., & Hitlin, P. (2005). Teens and Technology: Youth are Leading the Transition to a Fully Wired and Mobile Nation. Washington, DC: Pew Internet & American Life Project.
  • Lenhart, A., Rainie, L., & Lewis, O. (2001). Teenage Life Online: The Rise of the Instant-Message Generation and the Internet’s Impact on Friendships and Family Relationships. Washington, DC: Pew Internet & American Life Project.
  • Lin, N. (2001). Social Capital: A Theory of Social Structure and Action. Cambridge: Cambridge University Press.
  • Ling, R., & Yttri, B. (2002). Hyper-coordination via mobile phones in Norway. In J. E.Katz & M.Aakhus (Eds.), Perpetual Contact: Mobile Communication, Private Talk, Public Performance (pp. 139169). Cambridge: Cambridge University Press.
  • Livingstone, S., & Bober, M. (2005). UK Children Go Online: Final Report of Key Project Findings. London: Economic and Social Research Council.
  • Madden, M., & Rainie, L. (2003). America’s Online Pursuits: The Changing Picture of Who’s Online and What They Do. Washington, DC: Pew Internet & American Life Project.
  • Magid, L. (2001, December 13). Europe children cell use ahead of U.S.—for good, bad. The Mercury News, p. 3.
  • National Children’s Home. (2005). Putting U in the picture: Mobile bullying survey 2005. London. Retrieved January 24, 2006, from
  • Nie, N. H., Hillygus, D. S., & Erbring, L. (2002). Internet use, interpersonal relations, and sociability: A time diary study. In B.Wellman & C.Haythornthwaite (Eds), The Internet in Everyday Life (pp. 215243). Malden, MA: Blackwell.
  • Riva, G. (2002) The sociocognitive psychology of computer-mediated communication: The present and future of technology-based interactions. Cyberpsychology and Behavior, 5(6), 581598.
  • Schneider, S., & Hemmer, K. (2005, May). Telegraph lines in cyperspace? Identity, relationships, and group behavior in instant messaging communication. Paper presented at the International Communication Association, New York.
  • Taylor, A. S., & Harper, R. (2003). The gift of the gab? A design oriented sociology of young people’s use of mobiles. Computer Supported Cooperative Work, 12(3), 267296.
  • Turow, J. (1999). The Internet and the Family: The View From the Family, the View From the Press. Philadelphia, PA: Annenberg Public Policy Center. Retrieved January 21, 2006, from
  • Valkenburg, P., & Peter, J. (2005, May). Adolescents’ online communication and their closeness to friends. Paper presented at the International Communication Association, New York.
  • Wolak, J., Mitchell, K. J., & Finkelhor, D. (2003). Escaping or connecting? Characteristics of youth who form close online relationships, Journal of Adolescence, 26(1), 105119.
About the Authors
  1. J. Alison Bryant is an Assistant Professor in the Department of Telecommunications at Indiana University. Her research focuses primarily on integrating network theories and analysis into research on children’s media to try to understand the evolution of the children’s media industry and the ways that media, especially socially interactive technologies, affect youth.

    Address: Department of Telecommunications, Indiana University, 1229 East 7th Street, Bloomington, Indiana 47405 USA

  2. Ashley Sanders-Jackson is a graduate student in the Department of Telecommunications at Indiana University. Her research focuses primarily on the effects of mediated environments on social networks and the underlying motivational processes that effect mediated message processing.

    Address: Department of Telecommunications, Indiana University, 1229 East 7th Street, Bloomington, Indiana 47405 USA

  3. Amber M. K. Smallwood is a doctoral student in the Departments of Telecommunications and American Studies at Indiana University. Her research focuses on qualitative and quantitative approaches to studying popular culture and the burgeoning field of noncommercial (educational) media industries.

    Address: Department of Telecommunications, Indiana University, 1229 East 7th Street, Bloomington, Indiana 47405 USA