Time spent online: Latent profile analyses of emerging adults' social media use

Abstract Studies of youth social media use (SMU) often focus on its frequency, measuring how much time they spend online. While informative, this perspective is only one way of viewing SMU. Consistent with uses and gratification theory, another is to consider how youth spend their time online (i.e., degree of engagement). We conducted latent profile analyses of survey data from 249 U.S. emerging adults (ages 18–26) to explore their SMU in terms of frequency and engagement. We derived separate 3-profile solutions for both frequency and engagement. High frequency social media users tended to be women and to have more Facebook friends. Highly engaged users (i.e., those most interactive online) tended to be White and more highly educated. Findings from this exploratory study indicate that youth SMU frequency and SMU engagement warrant separate consideration. As SMU becomes more ingrained into the fabric of daily life, it is conceivable that engagement may be a more meaningful way to assess youth SMU, especially in relation to the digital divide, since it can be used to meet important needs, including social interaction, information exchange, and self-expression.

[1]  Kathleen Beullens,et al.  Display of Alcohol Use on Facebook: A Content Analysis , 2013, Cyberpsychology Behav. Soc. Netw..

[2]  Brian Borsari,et al.  Descriptive and injunctive norms in college drinking: a meta-analytic integration. , 2003, Journal of studies on alcohol.

[3]  P. Best,et al.  Online communication, social media and adolescent wellbeing: A systematic narrative review , 2014 .

[4]  Cliff Lampe,et al.  Connection strategies: Social capital implications of Facebook-enabled communication practices , 2011, New Media Soc..

[5]  Amar Cheema,et al.  Data collection in a flat world: the strengths and weaknesses of mechanical turk samples , 2013 .

[6]  Michael D. Buhrmester,et al.  Amazon's Mechanical Turk , 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.

[7]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[8]  Anita Whiting,et al.  Why people use social media: a uses and gratifications approach , 2013 .

[9]  Maeve Duggan,et al.  Social Media Update 2016 , 2016 .

[10]  Matthew Pittman,et al.  Social media and loneliness: Why an Instagram picture may be worth more than a thousand Twitter words , 2016, Comput. Hum. Behav..

[11]  G. Celeux,et al.  An entropy criterion for assessing the number of clusters in a mixture model , 1996 .

[12]  E. Katz,et al.  On the use of the mass media for important things. , 1973 .

[13]  G. Margolin,et al.  Growing Up Wired: Social Networking Sites and Adolescent Psychosocial Development , 2014, Clinical child and family psychology review.

[14]  Leman Pinar Tosun.  Motives for Facebook use and expressing "true self" on the Internet , 2012, Comput. Hum. Behav..

[15]  Jesse J. Chandler,et al.  Inside the Turk , 2014 .

[16]  Cliff Lampe,et al.  The Benefits of Facebook "Friends: " Social Capital and College Students' Use of Online Social Network Sites , 2007, J. Comput. Mediat. Commun..

[17]  K. Davis.  Friendship 2.0: adolescents' experiences of belonging and self-disclosure online. , 2012, Journal of adolescence.

[18]  Laura M. Padilla‐Walker,et al.  Emerging in a Digital World , 2013 .

[19]  D. Rubin,et al.  Testing the number of components in a normal mixture , 2001 .

[20]  Tony Jung,et al.  An introduction to latent class growth analysis and growth mixture modeling. , 2008 .

[21]  Trish Gorely,et al.  A descriptive epidemiology of screen-based media use in youth: a review and critique. , 2006, Journal of adolescence.

[22]  Aaron Smith,et al.  Social Media & Mobile Internet Use among Teens and Young Adults. Millennials. , 2010 .

[23]  J. Schafer,et al.  Missing data: our view of the state of the art. , 2002, Psychological methods.

[24]  Libby N Brockman,et al.  A content analysis of displayed alcohol references on a social networking web site. , 2010, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[25]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[26]  Todd M. Gureckis,et al.  CUNY Academic , 2016 .

[27]  Panagiotis G. Ipeirotis,et al.  Running Experiments on Amazon Mechanical Turk , 2010, Judgment and Decision Making.

[28]  A. Lenhart.  Teens, Social Media & Technology Overview 2015 , 2015 .

[29]  H. Korda,et al.  Harnessing Social Media for Health Promotion and Behavior Change , 2013, Health promotion practice.

[30]  Dong-Mo Koo,et al.  Lonely People Are No Longer Lonely on Social Networking Sites: The Mediating Role of Self-Disclosure and Social Support , 2013, Cyberpsychology Behav. Soc. Netw..

[31]  Zheng Wang,et al.  A dynamic longitudinal examination of social media use, needs, and gratifications among college students , 2012, Comput. Hum. Behav..

[32]  Jens Eickhoff,et al.  Emergence and predictors of alcohol reference displays on Facebook during the first year of college , 2014, Comput. Hum. Behav..

[33]  Amandeep Dhir,et al.  Uses and Gratifications of digital photo sharing on Facebook , 2016, Telematics Informatics.

[34]  S. Sclove.  Application of model-selection criteria to some problems in multivariate analysis , 1987 .

[35]  N. Ellison,et al.  Social capital, self-esteem, and use of online social network sites: A longitudinal analysis , 2008 .

[36]  J. Arnett,et al.  Encyclopedia of children, adolescents, and the media , 2007 .

[37]  C. Bartneck,et al.  Comparing the Similarity of Responses Received from Studies in Amazon’s Mechanical Turk to Studies Conducted Online and with Direct Recruitment , 2015, PloS one.

[38]  Adam J. Berinsky,et al.  Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk , 2012, Political Analysis.

[39]  Shaunna L. Clark,et al.  Latent Class Analysis Results to Variables not Included in the Analysis , 2009 .

[40]  B. Muthén,et al.  Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study , 2007 .

[41]  Panagiotis G. Ipeirotis.  Analyzing the Amazon Mechanical Turk marketplace , 2010, XRDS.

[42]  Scott W. Campbell,et al.  Mobile Phones Bridging the Digital Divide for Teens in the US? , 2011, Future Internet.

[43]  D. McQuail.  Mass Communication Theory: An Introduction , 1983 .

[44]  Eyun-Jung Ki,et al.  Exploring influential social cognitive determinants of social media use , 2014, Comput. Hum. Behav..

[45]  Ben Beck,et al.  Regions of High Out-Of-Hospital Cardiac Arrest Incidence and Low Bystander CPR Rates in Victoria, Australia , 2015, PloS one.

[46]  E. Katz,et al.  ON THE USE OF THE MASS MEDIA AS “ESCAPE”: CLARIFICATION OF A CONCEPT , 1962 .

[47]  S. Hofmann,et al.  Why Do People Use Facebook? , 2012, Personality and individual differences.

[48]  Siddharth Suri,et al.  Conducting behavioral research on Amazon’s Mechanical Turk , 2010, Behavior research methods.

[49]  Stephanie M. Reich,et al.  Online and Offline Social Networks: Use of Social Networking Sites by Emerging Adults , 2008 .

[50]  David J. Hauser,et al.  Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants , 2015, Behavior Research Methods.

[51]  Sandra L. Calvert,et al.  College students' social networking experiences on Facebook , 2009 .

[52]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[53]  K. Carey,et al.  Peer influences on college drinking: a review of the research. , 2001, Journal of substance abuse.

[54]  B. Muthén,et al.  Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. , 2000, Alcoholism, clinical and experimental research.

[55]  Alexander von Eye,et al.  Race, Gender, and Information Technology Use: The New Digital Divide , 2008, Cyberpsychology Behav. Soc. Netw..

[56]  Andrew T. Perrin.  Social Media Usage: 2005-2015 , 2015 .