The popularization of “social media” has raised questions of how and why young people use these various technologies in their daily lives. This exploratory study proposes a classification system based on Rogers’ concept of technology clusters, which posits that likelihood of adoption is based around similar perceived characteristics of a technology or medium. Results from a survey administered to 401 undergraduates at a large southern university indicated that social and non–social technology cluster use is correlated with psychological, affective, and behavioral factors (extroversion, self–disclosure, computer anxiety and self–efficacy). One particularly popular type of “many–to–many” social media is the social network site (SNS). MySpace members were significantly more likely to use both other many–to–many social technologies as well as one–to–many. Gender differences were also found, as MySpace members were more likely to be female, and females had significantly higher levels of extroversion and self–disclosure. Implications for future research, marketing efforts, and online safety are discussed.
“Social media” broadly includes blogs, user–generated videos and pictures, social networking sites, message boards, and wikis (Li and Bernoff, 2008). Ninety–three percent of American teens use the Internet, and 64 percent have taken part in these activities (Lenhart, et al., 2007). boyd and Jenkins (2006) describe the motivation of these younger, technology–savvy individuals as, “looking for ways to leave their mark on the world and they are seeking places where they can socially interact with minimal adult interference.”  Yet, outside of demographics, few studies have investigated what defines “social media” through psychological measures (such as personality traits) that describe why individuals favor certain media over others.
Social media are a challenge to research with either computer–mediated communication theory (CMC) or mass communication theory; CMC tends to differentiate by task or examine specific technologies, while traditional distinctions of “mass” or “interpersonal” communication have restrictive connotations of directionality and audience size. Similarly, traditional distinctions of innovation adoption, such as “high” technology users or early adopters, do not capture meaningful comparisons across groups of technologies. Rogers’ (1995) concept of the technology cluster (part of innovation diffusion theory) provides a flexible way to examine motivation for adoption and use based on perceived useful qualities, similar to the technology acceptance model (Davis, 1989) or the uses and gratifications perspective (Katz, et al., 1973).
Rogers describes the adoption of related technologies as a “technology cluster” which “consists of one or more distinguishable elements of technology that are perceived as being closely interrelated.”  For example, through technology clusters, cell phones were found to facilitate person–to–person interaction and were connected through clusters to online chatting (Leung, 2001), and personal computer users were more likely to adopt videotext services (Lin, 1998). In this way, technology clusters have been shown to be a simple yet flexible method of organizing technologies based on perceived similarities.
The current study therefore 1) proposes clusters organized around central qualities of technologies that fall under the concept of “social media”; 2) theorizes that personal psychological qualities and affective tendencies may relate to perceived usefulness of these clusters; and, 3) performs quantitative analyses based on validated scales measuring these psychological and affective constructs. Results from this study demonstrate meaningful differences in the fundamental attraction to these technologies, and provide valuable information for those seeking to make online communities safer, integrate multiple technologies to cultivate online communities, improve online reputation, and propagate information in advertising campaigns.
The term “social media” encompasses a variety of different technologies. Technology clusters were created first around the type of communication the technology engenders: one–to–one communication (such as through the telephone) or many–to–many communication (such as on a message board). Some technologies, such as devices (laptop, cellphone, PDA) and downloading (images, movies) were considered to be multi–use devices that offered functional advantages rather than engendering certain types of connectivity. For instance, “information seeking” behavior encompasses both active and passive searching on the Web (Choo, et al., 2000) that is now common through sites such as Google, and “devices” was created around specific types of mobile or portable devices (Lin, 1998).
Table 1: Technology cluster composition. Cluster type Technologies included in cluster
Each question is a 5–item Likert scale in response to the question, “Please circle a response to indicate how frequently you use these products, features, or software, from 1 (never) to 5 (very frequently).”
One–to–one Chatting with people online using AIM or similar
Using text messaging on cell phone
Many–to–many Taking and sharing digital pictures
Writing or leaving comments on a blog
Using social network sites (SNSs)
Using the SNS Facebook
Filming and sharing movies
Posting and reading product reviews
Posting and reading messages on discussion boards
Downloads Downloading images or movies to a cell phone
Downloading music or movies to a computer
Devices Laptop computer
PDA (personal digital assistant)
Info–seeking Looking up directions on MapQuest
Searching for things using Google
Reading news on CNN or similar Web site
Weaver (2000) stated that, “given that the selection and use of the mass media has become ... an integral part of most individuals’ social environment ... the expectation that personality characteristics should be directly linked to our orientation toward and perceptions of the mass media seems prudent.”  Finn (1997) found support for relationships between personality traits and certain media: openness and reading for pleasure; extroversion and negative pleasure reading; and, openness and negative television viewing. Paul, et al. (2004) found support for correlations between some of the “big five” personality traits (neuroticism, extroversion, openness, agreeableness, and conscientiousness) and dependency on television. Internet users have increased access to a variety of media and ways to interact with others online, so the impact of such traits on media use may be even more relevant online than with television or print.
One of the primary personality traits is extroversion, a trait dichotomous with introversion and exhibited by those seeking meaning in life outside themselves (Jung, 1976). It was included in Eysenck, et al.’s (1985) EPQ–R scale, and is a personality trait closely related to desire for socialization with others and is correlated with self–disclosure. Extroverts are described as sociable, lively, active, assertive, care–free, dominant, venturesome and sensation–seeking . Costa and McRae (1988) described extroverts as having, “needs for social contact, attention, and fun.”  Extroverts are concerned with their appearance to the outside world and how others interpret them. Therefore, extroverts may be more likely to be interested in activities that involve interacting and being around others, such as social network sites, writing on a blog, and sharing digital pictures.
Self–disclosure is an act required by most interpersonal relationships. McKenna, et al. (2002) define the act of self–disclosure as expression of “the identity–important yet usually unexpressed aspects of oneself.”  Archer (1980) identifies self–disclosure more generally as “the act of revealing personal information to others.”  On the topic of MySpace, Stern states, “Our everyday culture definitely celebrates self–disclosure. Kids are picking up on that. It gives them every indication that this is what we value from people.” (Metz, 2006). For the purposes of this study, “self–disclosure” is interpreted to generally refer to the ease of expression of personal information. This may affect how useful an individual is likely to find a Web site such as MySpace or other many–to–many environments such as blogs where revealing of such information affects perceived usability.
Computer anxiety and self–efficacy
Computer anxiety is defined as the negative, affective response of some individuals to computer technology (Barbeite and Weiss, 2004). Self–efficacy is a cognitive ability defined as the belief in one’s capability to organize and execute a particular course of action (Bandura, 1997). Specifically as concerns the Internet, computer self–efficacy is defined as the “belief in one’s actions on the computer” , which is how these Web sites (with the exception of mobile technologies) are typically accessed. In other words, individuals who have a high degree of computer self–efficacy find computers to be useful to achieve their goals. Computer self–efficacy also has a negative correlation with computer anxiety. Barbeite and Weiss (2004) developed scales of self–efficacy and anxiety specifically for computer use, and found that computer self–efficacy measures were the best predictor of Internet use.
Eastin and LaRose (2000) concluded self–efficacy and self–disparagement were possible factors in explaining the “digital divide” as conceived by Norris (2001). That is, who uses Internet resources is dependent on an individual’s ability to easily use computers. Durndell and Haag (2002) found lower computer anxiety and higher self–efficacy to be significantly correlated with higher reported use of the Internet and positive attitudes about the Internet in a sampling of East Europeans.
Given that factors of computer self–efficacy and computer anxiety have been shown to relate to Internet and computer usage, these factors may also have correlates with specific online and computer–based technologies.
RQ1: How are extroversion, self–disclosure, self–efficacy and computer anxiety correlated with technology cluster membership?
One particularly popular type of social media that blurs interpersonal and mass media is the “social network site” (boyd and Ellison, 2007), or SNS. At the time of this survey’s administration, MySpace was the most popular Web site in the United States, claiming some 83 million total users (Metz, 2006) and 12 million unique visitors per day, as well as the most popular SNS (comScore.com, 2007). On SNSs, activities include sending of private messages, viewing of profiles, and leaving public comments. Boyd responds to this variety of activities by stated that privacy on these sites is not clearly delineated, and that, “social convergence occurs when disparate social contexts are collapsed into one.”  Similarly, SNSs, particularly MySpace, also increasingly “collapse” other forms of mass media as they serve as venues and vehicles for online media. MySpace promotes music and video through placement on the site, and funding of original video series is an area of expansion for the company. In late 2007 they funded two series, the MySpace–created “Roomates” and the dramatic series “Quarterlife.” In the same year, MySpace musician and model Tila Tequila received her own series, “A Shot at Love with Tila Tequila” on MTV. SNSs are increasingly used as part of media campaigns with both traditional and other new “social technologies” (Li and Bernoff, 2008). Advertisements, as games, videos, or other presentations, are frequently deployed in embeddable widget form to speed their propagation online generally, and through SNS profiles specifically. Given the popularity of MySpace, and the new ways it is being integrated with advertising and video, it was included in analyses to determine what other technologies these users are likely to use.
RQ2: What technology clusters do MySpace members use frequently?
Gender was selected for examination, as it is a demographic variable that describes significant differences in how individuals use social media. In a national study, content creators were found to be more likely (55 percent) to be female, and females were more likely to have used an online social network (70 percent) compared to males (54 percent) (Lenhart, et al., 2007). Jackson, et al. (2001) concluded that, “males and females used the Internet equally often, but used it differently.”  Similarly, Hargittai (2007) found that, “when SNS usage statistics are considered in the aggregate, the results only show a relationship of gender to SNS use.” Thelwall (2008) found a “small majority” of females in a content analysis of MySpace profiles, and Magnuson and Dundes (2008) found a similarly slight majority of females in a convenience sampling (51 females, 49 males). Therefore, the current study examines how gender relates to MySpace membership and the above factors (extroversion, self–disclosure, computer anxiety, computer self–efficacy). Given these findings, some of which specifically relate to SNSs, the current study examines the relationship of gender to both MySpace membership and factors affecting technology cluster usage.
RQ3: How does gender relate with MySpace membership and extroversion, self–disclosure, self–efficacy and computer anxiety?
To obtain results, a survey was administered to a convenience sampling of college undergraduates aged 18 and older. Participants were recruited from eight undergraduate classes in two departments, one from the college of arts and one from the college of sciences. Faculty were not present for the survey administration, and were offered no incentive for participation. Access to the student populations was obtained through researchers and faculty members. The survey was administered at the beginning of a class session and contained four questions on demographics (gender, race, age, class year), 10 questions on extroversion (Eysenck, et al., 1985), 10 questions on self–disclosure (Wheeless, 1976), three questions on MySpace use, and 16 questions on computer efficacy and computer anxiety (Barbeite and Weiss, 2004). The survey was approved by the University’s IRB, and administered with a waiver of consent. The survey was anonymous, and no identifiable information was collected about the students, such as name or university ID. No credit or compensation was offered for participation. A total of 401 undergraduates participated in the study.
MySpace use was collected, as at the time of data collection it was the most popular social network site (SNS). MySpace members were significantly more likely to use both one–to–one technologies, t(401) = 5.812, ρ < .01, and many–to–many technologies, t(401) = 8.485, ρ < .01. That is, the appeal of this cluster may be related to both talking directly with another person or to a group of people.
Table 2: Statistics for social technology cluster usage by MySpace membership. Cluster type MySpace membership N Mean Standard deviation Standard error One–to–one Member 301 11.07 2.42 .14 Non–member 95 9.42 2.43 .25 Many–to–many Member 301 16.34 4.42 .25 Non–member 95 11.89 4.67 .48
Extroversion, self–disclosure, computer self–efficacy and computer anxiety had weak to moderate correlations with frequency of technology cluster use.
Table 3: Correlations of psychological, affective, and behavioral factors with technology clusters (ρ < .01, * = N.S.) Factor One–to–one Many–to–many Info seeking Devices Downloading Extroversion (α=.90) .230 .214 * .182 .121 Self–disclosure (α=.78) * .171 * * * Computer anxiety (α=.92) * * -.211 * -.139 Computer self–efficacy (α=.86) * * .255 * .231
To examine the gender of MySpace members as compared to non–members, a weighted variable was created to account for the greater number of males (N=170) than females (N=231) in the sampling. With this weighted variable, it was revealed that females were more likely to be MySpace members, while males were more likely to be non–members. Males had on average more MySpace friends (M = 193.74) as compared with females (M = 149.69), but females had been a member of MySpace for longer (M = 13.57) as compared with males (M = 15.02). Males and females both actively used the site for an average of 1.3 hours per day. Several significant differences in psychological, affective, and behavioral factors were found, revealing that females had on average more computer anxiety, less computer self–efficacy, more extroversion and more ability to self–disclose information.
Table 4: Gender frequency of MySpace members as compared with non–members. MySpace members Non–members Male 47.9 56.6 Female 52.1 43.4
Table 5: Psychological, affective, and behavioral factors by gender. Factor M (female) M (Male) t ρ Extroversion 36.19 34.46 -2.30 ≤.05 Self–disclosure 33.58 32.01 -2.83 ≤.05 Computer anxiety 11.26 9.62 -3.61 ≤.05 Computer self–efficacy 28.65 32.52 6.58 ≤.05
This paper explored characteristics of social technology users. Extroversion and self–disclosure were positively correlated with the many–to–many and one–to–one clusters, and self–disclosure was correlated only with the many–to–many cluster. MySpace users were specifically more likely to be users of one–to–one as well as many–to–many technology clusters.
These findings run somewhat counter to previous findings in CMC and warrant discussion. Extroverts have typically considered media a poor substitute for real–life interaction on media such as television (Finn, 1997) and the Internet in general (Amichai–Hamburger, et al., 2002). Low–cue environments have even been found to coax introverts to create bonds more quickly than extroverts (Walther, 1996). This discrepancy may be explained by detailing key differences SNSs (and other many–to–many technologies) have with previous forms of Internet–based communication. These sites are multimedia, centered around social activities such as cultivating lists of friends and sending messages, and are popular with the majority of young Americans. If the most popular uses of the Internet are social (Lenhart, et al., 2007; Magnuson and Dundes, 2008), and friendships created by young people are maintained through a combination of online and offline activities, social media such as SNSs may be likely to be more attractive to extroverts than a decade ago when such sites were text–based and less popular. Additionally, SNSs and related sites are multimedia, containing video, images, and audio, which are likely to be a more comfortable environment to those comfortable with self–disclosure. However, extroverts and those willing to self–disclose may be using these cutting–edge resources at the expense of introverts, who may prefer more less revealing, more visually anonymous, and more text–based environments. These results also touch on the paradox of self–disclosure; young people value the freedom of self–expression, but may not weigh the consequences of putting their entire lives online. An area that remains to be examined is whether individuals are aware of this “privacy paradox” (Barnes, 2006) with regard to other factors in media use decisions.
Computer anxiety was negatively correlated and computer self–efficacy was positively correlated with both information–seeking and downloading technology clusters. These data indicate that one–to–one and many–to–many technologies are relatively easy to use, and finding information and downloading media may be activities that are more accessible to computer–savvy individuals. One–to–many and many–to–many technologies are relatively easy to use, and do not cause anxiety or require particularly strong computer skills. Many–to–many technologies may be easier to use and less imposing than download or information–seeking technologies, which may be more complex. Torrents, one form of peer–to–peer downloading particularly popular for sharing movies and programs, is relatively complex and requires downloading a program, setting up of the program, retrieving of a separate torrent file for each download, and possibly setting up a router to allow for certain ports to remain open. Similarly, information–seeking technologies such as Google present a host of choices for users to evaluate, which may be imposing for those not well versed in how to use them. As pertains to marketing, the lack of correlation between both one–to–one and many–to–many technologies with computer self–efficacy or computer anxiety means that companies may use these social technologies without fear of alienating mainstream teens.
Although not explicitly examined, other studies reveal the declining number of young newspaper readers (Li, 2006) and consumers of real–world music products (NPD Group, 2008), while amateur–produced content increases (Jenkins, 2006). This relationship is likely the next chapter of evolution in mass media, a “de–massification” that represents an evolution in media. Social media such as SNSs are a launching platform as well as advertising base for music, video, and games. These media are completely unlike traditional programming, built for young, high–tech demographics with short attention spans. Several actors and shows have started online, propagated through SNSs, and eventually secured cable television shows. Tila Tequila, a popular MySpace member who is a model and music producer, crossed over into traditional media by way of “A Shot At Love With Tila Tequila.” This reality dating show on MTV received high ratings, and has run for two seasons.
Other attempts at crossing over from the Internet have been less long–lived. Quarterlife, an online video series with an integrated social network site produced by Marshall Herskovitz and Ed Zwick, premiered on MySpace in late 2007. Its buzz, combined with a dearth of new shows due to the screen guild writer’s strike, led to it being picked up by NBC. It was the first Web–based show to cross over to television. According to Nielsen, 3.2 million viewers watched Quarterlife’s premiere television debut — on par with Tila Tequila. But for NBC these numbers did not equate to a success, and led to the show being immediately pulled (Goldstein, 2008). The Hollywood Reporter described the ratings for the show as the worst in 17 years for its time slot (Hibberd, 2008) and the future of the costly Quarterlife site itself is unclear.
Understanding what other media SNS members are attracted to and why, and the extent to which consumers will follow shows across media, will help prevent such missteps in the future. The light and fluffy fare of Tila Tequila played well on MTV for their demographic, which likely included extroverted, self–disclosure friendly MySpace members. By contrast, critics and fans greeted Quarterlife’s move to NBC by contrast with skepticism, if not outright hostility. There was a mismatch with theme, venue, and demographics, and Quarterlife failed as primetime material; the comparatively dramatic and serious 20–something intrigue of blogs, flings, and ennui was not able to cross over. Another possibility was that there was simply no reason for early adopters to watch a show that had already premiered on their preferred medium. Regardless, the lesson here is that careful consideration must be paid to sequencing of trans–media experiences with anything less well–funded than a “shotgun” approach (e.g., the video game, Internet, and movie franchise based on the Matrix trilogy).
Females were more likely to be MySpace members, which echoes other findings on gender usage of social networking sites (Hargittai, 2007; Jackson, et al., 2001; Lenhart, et al., 2007; Magnuson and Dundes, 2008; Thelwall, 2008). Moreover, the finding of a female–heavy MySpace membership, despite higher computer anxiety and lower self–efficacy, mirrors other studies that found a female–heavy population on other “many–to–many” social technology users, such as IRC (Komborough, 1999).
The female majority, extroverted, and self–disclosure friendly MySpace can be seen as another strike against the idea of the digital divide (Norris, 2001), and an Internet populated primarily by middle–class Caucasian males. Yet, extroverts and those willing to self–disclose may be using certain cutting–edge resources at the expense of introverts, who prefer anonymous and text–based modes. These issues can only be fully examined by researching how identities are created online and how they compare to real–world relationships, for instance, by fusing this research on likelihood of adoption with concepts of social capital and weak ties (Granovetter, 1973; 1983). Are online friendships weak and convenient “drive–by” relationships (Putnam, 2000) being facilitated by social technologies?
The lack of correlation between one–to–one and many–to–many technology clusters and computer anxiety or computer self–efficacy suggests that although females are less likely to be traditional “computer geeks,” this does not affect their ability to use MySpace or other many–to–many technologies. However, if females are less computer–savvy, they may be at greater risk for solicitation from online solicitation and harassment. Although this study examined undergraduates, similar results are likely present in slightly younger populations (older adolescents). Research in this area is still new, but revealing of personal information on an SNS does not appear to be a risk factor in online solicitation of minors . Chat is the dominant mode of choice for online sexual solicitation of minors, and females receive the majority of solicitations (Wolak, et al., 2006). Still, Ybarra and Mitchell (2008) found that 27.1 percent of 10 to 17 year olds who received unwanted sexual solicitations in the last year, received them over a social networking site. Girls are also targeted online at up to twice the rate of boys (Finkelhor, et al., 2000). Although only a minority of those youth contacted find the experience distressful (Finkelhor, et al., 2000), if girls do not know how to block these entreaties due to lack of computer self–efficacy and high computer anxiety, they may be less able to take actions such as blocking a member or reporting the contact to an authority. The risks of solicitation and harassment in minority groups remains an open area for research (Schrock and boyd, 2008).
The finding that MySpace has a small majority of female membership echoes previous studies where SNS members were found to be majority female (51–54 percent) through profile content analysis (Hinduja and Patchin, 2008; Thelwall, 2008). This is somewhat contradictory to how males had larger friend networks than females, yet still used the site for a similar amount per day on average (1.3 hours). These results in tandem would seem to indicate that males use SNSs to meet new people and expand their friend network (for example, for dating), while females use them for maintaining existing networks. Although the type of usage is different, there is no difference in overall time spent on the site.
However, we are still watching how SNSs are being integrated into modern society; a single administration of a survey is a snapshot of a moving target. Similar to cultivation theory (Gerbner and Gross, 1976), individuals who enter adolescence today in the United States will likely grow up an immersive media environment, but one based on the Internet more than television. Postman (1993) stated, with regard to how technology is incorporated into society, that “technological change is not additive; it is ecological.”  Lehman–Wilzig and Cohen–Avigdor (2004) similarly posit that a new technology has a life span of adoption and use, during which it competes with past technologies. However popular MySpace is, and despite the relatively high amounts of dependent MySpace use by young people in the current study (1.3 hours of daily use), it is only one Web site is the wide range of technologies and media young individuals use to connect with others. For instance, Facebook has risen in ranking since this study was conducted, and according to some measures by the site Alexa, has even overtaken MySpace in ranking.
Future investigative work must be performed into the nature of these trans–media interactions, using richer, more contextual conceptions of use, and compared across global populations. As this is an exploratory study, follow–up is needed to create a more theoretically rigorous model of social media attraction and use. Regression analysis or modeling could be used, in tandem with similar results mentioned in this discussion, to create a coherent model of social media adoption and usage, perhaps based on a compatible theory such as uses and gratifications (Ruggiero, 2000) or the technology acceptance model (Davis, 1989).
About the author
Andrew Schrock is Assistant Director and part–time faculty at the Annenberg Program on Online Communities at the Annenberg School for Communication at the University of Southern California. He received his M.A. from the Nicholson School for Communication at the University of Central Florida. In December 2008 he concluded research with danah boyd at the Berkman Center for Internet and Society at Harvard, as part of the Internet Safety Task Force. His research interests include online communities, software production processes, technology adoption, and online safety.
The author expresses gratitude to Dr. Tim Brown, Dr. Sally Hastings, and Dr. Rick Kenney for invaluable assistance and feedback on this study.
1. boyd and Jenkins, 2006, p. 5.
2. Rogers, 1995, p. 15.
3. Weaver, 2000, p. 236.
4. Eysenck, et al., 1985, p. 15.
5. Costa and McRae, 1988, p. 261.
6. McKenna, et al., 2002, p. 11.
7. Archer, 1980, p. 183.
8. Barbeite and Weiss, 2004, p. 3.
9. boyd, 2008, p. 18.
10. Jackson, et al., 2001, p. 374.
11. Mitchell, et al., 2007; Ybarra, et al., 2007, p. 138; Ybarra and Mitchell, 2008.
Y. Amichai–Hamburger, G. Wainapel, and S. Fox, 2002. “‘On the Internet, no one knows I’m an introvert’: Extroversion, neuroticism, and Internet interaction,” CyberPsychology & Behavior, volume 5, number 2, pp. 125–128.
J. Archer, 1980. “Self–disclosure,” In: D. Wegener and R. Vallacher (editors). The self in social psychology. New York: Oxford University Press, pp. 183–204.
A. Bandura, 1997. Self–efficacy: The exercise of self–control. New York: W.H. Freeman.
F. Barbeite and E. Weiss, 2004. “Computer self–efficacy and anxiety scales for an Internet sample: Testing measurement equivalence of existing measures and development of new scales,” Computers in Human Behavior, volume 20, number 1, pp. 1–15.
S. Barnes, 2006. “A privacy paradox: Social networking in the United States,” First Monday, volume 11, number 9, at http://journals.uic.edu/fm/article/view/1394/1312, accessed 21 December 2008.
d. boyd, 2008. “Facebook’s privacy trainwreck: Exposure, invasion, and social convergence,” Convergence, volume 14, number 1, pp. 13–20.
d. boyd and N. Ellison, 2007. “Social network sites: Definition, history, and scholarship,” Journal of Computer–Mediated Communication, volume 13, number 1, at http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html, accessed 21 December 2008.
d. boyd and H. Jenkins, 2006. “MySpace and Deleting Online Predators Act (DOPA),” MIT Tech Talk (26 May), at http://www.danah.org/papers/MySpaceDOPA.html, accessed 21 December 2008.
C. Choo, B. Detlor, and D. Turnbull, 2000. “Information seeking on the Web: An integrated model of browsing and searching,” First Monday, volume 5, number 2, at http://journals.uic.edu/fm/article/view/729/638, accessed 21 December 2008.
comScore.com, 2007. “Social networking goes global: Major social networking sites substantially expanded their global visitor base during past year,” at http://www.comscore.com/press/release.asp?press=1555, accessed 12 March 2008.
P. Costa and R. McCrae, 1988. “From catalog to classification: Murray’s needs and the five–factor model,” Journal of Personality and Social Psychology, volume 55, number 2, pp. 258–265.
F. Davis, 1989. “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, volume 13, number 3, pp. 319–340.
A. Durndell and Z. Haag, 2002. “Computer self–efficacy, computer anxiety, attitudes towards the Internet and reported experience with the Internet, by gender, in an East European sample,” Computers in Human Behavior, volume 18, pp. 521–535.
M. Eastin and R. LaRose, 2000. “Internet self–efficacy and the psychology of the digital divide,” Journal of Computer–Mediated Communication, volume 6, number 1, at http://jcmc.indiana.edu/vol6/issue1/eastin.html, accessed 21 December 2008.
S. Eysenck, H. Eysenck, and P. Barrett, 1985. “A revised version of the psychoticism scale,” Personality and Individual Differences, volume 6, number 1, pp. 21–29.
D. Finkelhor, K. Mitchell, and J. Wolak, 2000. “Online victimization: A report on the nation’s youth,” at http://www.unh.edu/ccrc/pdf/jvq/CV38.pdf, accessed 21 December 2008.
S. Finn, 1997. “Origins of media exposure: Linking personality traits to TV, radio, print, and film use,” Communication Research, volume 24, number 5, pp. 507–529.
G. Gerbner and L. Gross, 1976. “Living with television: The violence profile,” Journal of Communication, volume 26, number 2, pp. 172–199.
P. Goldstein, 2008. “Old Media upstart ‘Quarterlife’ gets a Web smackdown,” Los Angeles Times (4 March), at http://articles.latimes.com/2008/mar/04/entertainment/et-goldstein4, accessed 21 December 2008.
M. Granovetter, 1983. “The strength of weak ties: A network theory revisited,” Sociological Theory, volume 1, pp. 201–233.
M. Granovetter, 1973. “The strength of weak ties,” American Journal of Sociology, volume 78, number 6, pp. 1360–1380.
E. Hargittai, 2007. “Whose space? Differences among users and non–users of social network sites,” Journal of Computer–Mediated Communication, volume 13, number 1, at http://jcmc.indiana.edu/vol13/issue1/hargittai.html, accessed 21 December 2008.
J. Hibberd, 2008. “NBC bombs with ‘quarterlife’ premiere,” Reuters, at http://www.reuters.com/article/televisionNews/idUSN2748604320080227, accessed 21 December 2008.
S. Hinduja and J. Patchin, 2008. “Personal information of adolescents on the Internet: A quantitative content analysis of MySpace,” Journal of Adolescence, volume 31, number 1, pp. 125–146.
L. Jackson, K. Ervin, P. Gardner, and N. Schmitt, 2001. “Gender and the Internet: Women communicating and men searching,” Sex Roles, volume 44, numbers 5–6, pp. 363–379.
H. Jenkins, 2006. Convergence culture: Where old and new media collide. New York: New York University Press.
C. Jung, 1976. Psychological types. Collected Works of C.G. Jung, volume 6. Edited and translated by Gerhard Adler and R.F.C. Hull. Princeton, N.J.: Princeton University Press.
E. Katz, J. Glumler, and M. Gurevitch, 1973. “Uses and gratifications research,” Public Opinion Journal Quarterly, volume 37, number 4, pp. 509–523.
D. Komborough, 1999. “On–line ‘chat room’ tutorials — An unusual gender bias in computer use,” Journal of Science Education and Technology, volume 8, number 3, pp. 227–234.
S. Lehman–Wilzig and N. Cohen–Avigdor, 2004. “The natural life cycle of new media evolution: Inter–media struggle for survival in the Internet age,” New Media & Society, volume 6, number 6, pp. 707–730.
A. Lenhart, M. Madden, A. Rankin Macgill, and A. Smith, 2007. “Teens and social media: The use of social media gains a greater foothold in teen life as they embrace the conversational nature of interactive online media,” Pew Internet & American Life Project (19 December), at http://www.pewinternet.org/pdfs/PIP_Teens_Social_Media_Final.pdf, accessed 5 July 2008.
L. Leung, 2001. “College student motives for chatting on ICQ,” New Media & Society, volume 3, number 4, pp. 483–500.
X. Li (editor), 2006. Internet newspapers: The making of a mainstream medium. Mahwah, N.J.: Lawrence Erlbaum Associates.
C. Li and J. Bernoff, 2008. Groundswell: Winning in a world transformed by social technologies. Boston: Harvard Business School Press.
C. Lin, 1998. “Exploring personal computer adoption dynamics,” Journal of Broadcasting and Electronic Media, volume 42, number 1, pp. 95–112.
M. Magnuson and L. Dundes, 2008. “Gender differences in ‘social portraits’ reflected in MySpace profiles,” CyberPsychology & Behavior, volume 11, number 2, pp. 239–241.
K. McKenna, A. Green, and M. Gleason, 2002. “Relationship formation on the Internet: What’s the big attraction?” Journal of Social Issues, volume 58, number 1, pp. 9–31.
C. Metz, 2006. “MySpace nation,” PC Magazine, volume 25, number 12 (July), pp. 76–87.
K. Mitchell, D. Finkelhor, and J. Wolak, 2007. “Youth Internet users at risk for the most serious online sexual solicitations,” American Journal of Preventive Medicine, volume 32, number 6, pp. 532–537.
P. Norris, 2001. Digital divide: Civic engagement, information poverty, and the Internet worldwide. New York: Cambridge University Press.
NPD Group, 2008. “iTunes continues to lead U.S. music retailers in first half of 2008” (5 August), at http://www.businesswire.com/, accessed 21 December 2008.
B. Paul, J. Shim, and Z. Wang, 2004. “Personality and individual media dependency goals,” paper presented at the Association for Education in Journalism and Mass Communications (AEJMC) 2004 (Toronto); abstract at http://www.aejmc.org/_events/convention/abstracts/2004/ctm.php, accessed 21 December 2008.
N. Postman, 1993. Technopoly: The surrender of culture to technology. New York: Vintage.
R. Putnam, 2000. Bowling alone: The collapse and revival of American community. New York: Simon & Schuster.
E. Rogers, 1995. Diffusion of innovations. Fourth edition. New York: Free Press.
T. Ruggiero, 2000. “Uses and gratifications theory in the 21st century,” Mass Communication & Society, volume 3, number 1, pp. 3–37.
A. Schrock and d. boyd, 2008. “Online threats to youth: Solicitation, harassment, and problematic content,” at http://zephoria.org/isttf/ISTTF-LitReviewDraft.pdf, accessed 28 December 2008.
M. Thelwall, 2008. “Social networks, gender, and friending: An analysis of MySpace member profiles,” Journal of the American Society for Information Science and Technology, volume 59, number 8, pp. 1321–1330.
J. Walther, 1996. “Computer–mediated communication: Impersonal, interpersonal, and hyperpersonal interaction,” Communication Research, volume 23, number 1, pp. 3–43.
J. Weaver, 2000. “Personality and entertainment preferences,” In: D. Zillmann and P. Vorderer (editors). Media entertainment: The psychology of its appeal. Mahwah, N.J.: Lawrence Erlbaum Associates, pp. 235–247.
L. Wheeless, 1976. “Self–disclosure and interpersonal solidarity: Measurement, validation, and relationships,” Human Communication Research, volume 3, number 1, pp. 47–61.
J. Wolak, K. Mitchell, and D. Finkelhor, 2006. “Online victimization of youth: Five years later,” at http://www.unh.edu/ccrc/pdf/CV138.pdf, accessed 21 December 2008.
M. Ybarra and K. Mitchell, 2008. “How risky are social networking sites? A comparison of places online where youth sexual solicitation and harassment occurs,” Pediatrics, volume 121, number 2, pp. e350–e357.
M. Ybarra, K. Mitchell, D. Finkelhor, and J. Wolak, 2007. “Internet prevention messages: Targeting the right online behaviors,” Archives of Pediatrics & Adolescent Medicine, volume 161, number 2, pp. 138–145.
Paper received 8 August 2008; accepted 10 December 2008.
“Examining social media usage: Technology clusters and social network site membership”
is licensed under a Creative Commons Attribution–Noncommercial–Share Alike 3.0 United States License.
Examining social media usage: Technology clusters and social network site membership
by Andrew Schrock
First Monday, Volume 14, Number 1 - 5 January 2009
A Great Cities Initiative of the University of Illinois at Chicago University Library.
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