Gratifications on Social Networking Sites: The Role of Secondary School Students’ Individual Differences in Loneliness

Abstract

Social networking sites, such as Myspace and Facebook, have become popular online communities with large teenage user populations who engage in social media to interact, play, explore, and learn. Research based on uses and gratifications theory has revealed that among the gratifications on social media most sought specifically by adolescents between 12 and 18 years old are meeting new people, satisfying teenagers’ need to belong, and being entertained. Previous research also points to possible differences in the perception of gratifications among users of different media, depending on their level of loneliness. The current study examines whether associations between loneliness and gratifications obtained by adolescent social media users vary across different levels of loneliness. We conducted a survey among 344 Spanish secondary school students with an online profile on the Spanish friend-networking site Tuenti. Mixture modeling identified three classes of students with different levels of loneliness and distinct relationships between loneliness and gratifications for each class. This study contributes to more specific knowledge of the relationship between use of a friend-networking site by lonely teenagers and gratifications obtained and provides implications for the development of coping strategies for students of this age who suffer from loneliness. Further implications of findings for personalized educational computing and group management in on- and offline classrooms are discussed.

Keywords

social media social networking uses and gratifications theory loneliness quantitative analysis structural equation mixture modeling latent class analysis

Introduction

Using social media has become a favorite pastime for most people. Particularly for teenagers, interacting with peers through such channels has acquired a singular importance. Friend-networking sites, such as Myspace and Facebook, have become popular online communities with large teenage user populations, who engage in these social media to interact, play, explore, and learn (Ahn, 2011). Social media, therefore, are becoming increasingly relevant for education purposes, and educators need a deeper understanding of the social and cultural factors related to young users’ social media engagement (Greenhow, 2011).

According to uses and gratifications theory (UGT), people use media because they derive specific gratifications from media consumption. The term media refers to communication channels or tools, such as newspapers, radio, or television, and more recent new media, based on the Internet, including social media, which allow the creation and sharing of information by way of virtual communities and social networking sites. Social networking sites (SNSs) are applications based on the web that permit individuals to build a public profile and connect with other users. UGT argues that audiences seek diverse gratifications by selecting media and its content, particularly satisfaction of information needs, social interaction, and entertainment (Katz, Haas, & Gurevitch, 1973). In recent years, many studies have applied UGT in the context of the new media, including the Internet (Grant, 2005; Rubin, 2002; Ruggiero, 2000; Wolfradt & Doll, 2001), smart phones (Grant & O’Donohoe, 2007), and specifically, social media, such as social networking sites (e.g., Papacharissi & Mendelson, 2011). UGT-based research has revealed that among the gratifications in social media most sought specifically by adolescents are meeting new people (Kim, Yoo-Lee, & Sin, 2011; Park, Kee, & Valenzuela, 2009), satisfying teenagers’ need to belong (McKenna & Bargh, 1999), and being entertained with the aim of filling up spare time, taking a break, and having fun (Ellison, Heino, & Gibbs, 2006). Best, Manktelow, and Taylor’s (2014) review found that using social media has benefits, such as increased self-esteem, perceived social support, increased social capital, safe identity experimentation, and increased opportunity for self-disclosure.

Research has also shown that users’ psychological and sociological characteristics can enhance or mitigate media effects (Rubin, 2002) and determine the motivation for using social media, as well as gratifications obtained (Orchard & Fullwood, 2010). However, research on influences of users’ different psychological characteristics on individual perceptions of gratifications obtained through social media use is still scarce. The present research addresses this gap in the literature by studying the role of teenagers’ perceived loneliness in the gratifications obtained through the use of the friend-networking site type of social media, such as Facebook. Feeling lonely is quite common among adolescents (Lou, Nickerson, & McMorris, 2012) and is one of the most common complaints reported by students to health institutions and centers (Qualter et al., 2015; Schinka, Van Dulmen, Mata, Bossarte, & Swahn, 2013; Van Dulmen & Goossens, 2013). Previous studies that have investigated the influence of loneliness on the uses and gratifications derived from different media have yielded inconsistent results. Canary and Spitzberg (1993) explained this inconsistency by the complexity of loneliness, because people may experience different degrees and different durations of loneliness.

This study aims to provide an answer to the question whether an adolescent’s mindset when using social media, specifically the more or less pronounced feeling of loneliness, affects the gratifications obtained. This study aims to contribute to the scientific debate regarding the potential relief of loneliness prevalent in teenagers, via social interaction on SNSs. Findings may be applied to develop more effective coping strategies to help lonely teenage students and have important implications for on- and offline education.

Findings are based on a survey of 344 adolescent students conducted in three Spanish secondary school centers who had an active user profile on the Spanish friend-networking site Tuenti. Since its creation in 2006 and during several intervening years, Tuenti has been the most popular SNS among teenagers in Spain; in 2010, 78% of 12- to 17-year-old teenagers used social networks (Sánchez-Burón & Fernández-Martín, 2010; Sánchez-Franco & Roldán, 2010). This study applies latent class analysis (LCA) to classify individuals by their level of loneliness and structural equation mixture modeling (SEMM) to address the influence of the degree of loneliness on the gratifications obtained by the teenagers in the study. The proposed methodology is a combination of latent class analysis and factor analysis. In SEMM, the structure of the variables is categorical and continuous (Muthén & Muthén, 2009). This method allows simultaneous identification of classes with different levels of loneliness and study of the effect of gratification obtained for each level.

Theoretical Framework

Social Media Uses and Gratifications

UGT has been applied successfully to the study of the psychological and social antecedents and outcomes of social media consumption. UGT postulates that individuals adopt an active role in media consumption; that is, they choose to engage with specific media to obtain particular gratifications (Katz et al., 1973), such as satisfaction of information needs, social interaction, and entertainment (Diddi & La Rose, 2006; Rubin, 1983). Interaction with other users and the possibility to share user-generated media content make social media particularly appropriate for providing social gratifications (Diddi & La Rose, 2006; Dunne, Lawlor, & Rowley, 2010). Social gratifications and entertainment have been shown to motivate the use of friend-networking sites, such as MySpace and Facebook (Ko, Cho, & Roberts, 2005; Lee & Ma, 2012; Lee, Goh, Chua, & Ang, 2010; Leung, 2009; Park et al., 2009; Raacke & Bonds-Raacke, 2008). Facebook has been shown to lead to social capital building among university students (Raza, Qazi, & Umer, 2016).

Therefore, it is not surprising that social media have become very popular. More than 80% of Spanish adolescents use social media platforms, and the Spanish friend-networking site Tuenti is the most popular in Spain (Sánchez-Burón & Fernández-Martín, 2010). Tuenti has been accepted by Spanish teenagers as a platform for social relations (Sánchez-Franco & Roldán, 2010). Compared with Facebook, Tuenti is perceived by its users as simpler and more intuitive but also as more intimate, that is, as a channel for communicating with close friends, as opposed to wider acquaintances (Monge-Benito & Olabarri-Fernández, 2011).

The Relationship Between Loneliness and Social Media Gratifications

Uses and gratifications theory has also focused on the different social and psychological circumstances of media engagement (Rubin, 2002). Research has identified loneliness as a significant psychological and social factor affecting social media consumption, particularly in the case of adolescents (Lou et al., 2012). Loneliness is defined as the feeling of a lack of sufficient social interaction. Individuals can have a higher or lower level of social interaction. A low level of social interaction is termed as social isolation. Loneliness, however, is a subjective experience. An individual may not feel lonely while alone. Loneliness is experienced when a person feels that his or her actual social relations and interactions are deficient (Cacioppo & Patrick, 2008; Peplau & Perlman, 1982; Pittman & Brandon, 2016). The subjective experience of loneliness has been assessed with established measurement instruments, such as Russell’s (1996) UCLA Loneliness scale. Loneliness can lead to symptoms of depression and antisocial and self-destructive behavior, including suicide, particularly in adolescents (Cacioppo, Hughes, Waite, Hawkley, & Thisted, 2006). Loneliness affects teenagers strongly, because during adolescence, the individual’s social world experiences important changes and the sense of identity strengthens (Harter, 1999; Perlman, 1988). Asher and Paquette (2003) indicated that children and adolescents experience chronic loneliness when they have no quality relationships during a significant period of time. Although it is considered normal for teenagers to experience situational loneliness occasionally, for example, when they lack a friend to interact with (Asher & Paquette, 2003; Beck & Young, 1978), suffering loneliness as an adolescent might be worse than at other moments of life, as it might hinder personal growth or identity development (Lou et al., 2012). Loneliness in children and adolescents can be determined by the value, meaningfulness, and durability of their friendships. In this stage, friends become more influential to the detriment of parents’ importance (Larson, 1999). Therefore, social relationships start to spread out from the adolescents’ family (Giordano, 2003), and being included and accepted by friends becomes very important in the development of teenagers’ individuality (Erikson, 1963; Rokach & Neto, 2000).

Loneliness may induce teenagers to engage in social media communication, and it has been suggested that using social media may provide relief from loneliness by decreasing social isolation. Internet use, for instance, has been found to decrease loneliness and increase social support (Kraut et al., 2002; Shaw & Gant, 2002). Social media, such as Facebook, have been shown to reduce the feeling of loneliness of young users (Kim et al., 2011). For the specific case of the Tuenti SNS, in Sánchez-Burón and Fernández-Martín’s (2010) study, teenagers reported that they used Tuenti for social relationships and the feeling of belonging to a community. However, it is not clear whether social media use provides relief from loneliness. Social media use may also increase loneliness because users have less time for face-to-face contact with friends and family. Internet use has been shown to potentially lead to more loneliness, too, particularly if the usage is excessive, with users spending more time online than with real-life relationships (Kraut et al., 1998; Morahan-Martin, 1999; Morahan-Martin & Schumacher, 2000). Individuals may substitute the lack of offline relationships with social media communication to find relief from loneliness. Kim, LaRose, and Peng (2009) found that individuals who were lonely or did not have good social skills could develop strong compulsive Internet use behaviors resulting in negative life outcomes (e.g., harming other significant activities, such as work, school, or significant relationships). Often, lonely people feel they are unable to change their situation and therefore select avoidance coping strategies instead of problem-focused ones (Heinrich & Gullone, 2006; Vanhalst, Luyckx, Raes, & Goossens, 2012). Searching for social contacts exclusively in online communications constitutes such an avoidance coping strategy.

Overall, loneliness constitutes a significant factor determining social media engagement. Kippax and Murray (1980) and Palmgreen and Rayburn (1985) showed that the extent of gratifications obtained from media consumption depended on users’ expectations. Previous research has shown that the personality characteristic of extravert versus introvert individuals determines social gratifications, such as accumulation of social capital, with extraversion leading to an increase in bonding social capital (Weiqin, Campbell, Kimpton, Wozencroft, & Orel, 2016). Lonely social media users may have different expectations than less lonely individuals. Lonely individuals may, to a higher degree, expect that engaging in social media will provide relief from their loneliness. This expectation may, in turn, affect how the social media engagement is experienced with respect to the gratifications actually obtained. The fact that a number of studies that have researched the influence of loneliness on the uses and gratifications derived from different media have yielded inconsistent results may be explained by the complexity of loneliness, because loneliness may vary in degree and duration among individuals (Canary & Spitzberg, 1993). Those individuals who seldom feel lonely and may compensate for occasional loneliness with contact through different media, in addition to other ways of social interaction, might perceive different gratifications derived from media use compared with individuals suffering severe and enduring loneliness. The latter type of individuals may actually not find compensation in media consumption at all for their lack of social interactions, even if they are motivated to engage in social media anticipating relief from loneliness (Perse & Rubin, 1990).

Regarding the gratifications obtained from social media such as the friend-networking site Tuenti, the extent of loneliness teenage users experience when they engage in social media communication may affect the type and degree of these gratifications. Different levels of loneliness may lead to a more or less pronounced experience of gratifications from entertainment on social media and social gratifications, such as the feeling of belonging to a group and making new acquaintances.

Loneliness and social gratifications: Belonging to a group

Interaction on social media can satisfy people’s need to belong to a group (McKenna & Bargh, 1999). According to Leary and Cox (2008), the need to belong is defined as the need to be involved with, accepted by, and valued by others. Studying virtual worlds specifically developed for children, Reich, Black, and Korobkova (2014) showed that access to virtual communities contributes to a sense of membership or a sense of belonging to the group. In addition, a number of studies confirmed that SNS interaction leads to greater feelings of bonding and enhances individuals’ feeling of belonging to a group (Amichai-Hamburger & Ben-Artzi, 2003; Burke, Marlow, & Lento, 2010; Koh & Kim, 2004; LaRose, Mastro, & Eastin, 2001; Wolfradt & Doll, 2001). In addition, for the specific case of the Tuenti SNS, teenagers indicated that belonging to a group or community is one of the principal reasons to use this SNS (Apaolaza, Hartmann, Medina, Barrutia, & Echebarria, 2013; Monge-Benito & Olabarri-Fernández, 2011). Tuenti users have also been shown to develop community participation and experience feelings of belonging and identification with other members (Sánchez-Franco, Buitrago-Esquinas, & Hernández-Mogollón, 2012). Compared with Facebook, which is seen as a channel for communicating with wider acquaintances, Tuenti is regarded by Spanish adolescents as a more private SNS, that is, a place to interact online with close friends (Monge-Benito & Olabarri-Fernández, 2011). Spanish teenagers use Tuenti to maintain and strengthen previously established social relations (Sádaba & Bringué, 2011).

Cicognani, Klimstra, and Goossens (2014) found a negative association between the sense of community of adolescents and their feeling of loneliness. Lonely people in particular experience the need to belong to a group (Leung, 2002; Pretty, Andrewes, & Collett, 1994; Rubin, Perse, & Barbato, 1988). Lonely adolescents may be more drawn to social media because of their need to belong to a group. Consequently, because the motivation for social media engagement seems to be affected by the adolescent’s loneliness, the level of loneliness should also determine the outcome of social media use, that is, the gratifications obtained. Depending on the teenager’s level of loneliness, he or she will experience a higher or lower degree of belonging to a group. Teenagers who feel severely affected by loneliness may not obtain these social gratifications to the same extent as less lonely individuals. The fact that social media may stimulate the feeling of belonging to a group is particularly relevant for educational methods using such interactive communication channels. As Gee and Hayes (2012) argued with respect to educational computer games, virtual communities can create “nurturing affinity spaces” that are particularly supportive of learning.

Loneliness and social gratifications: Meeting new people

Meeting new people, that is, using social media to make new friends or initiate new connections, is another important gratification derived from online social networking (Ellison et al., 2006). SNSs allow teenagers not only to manage existing social relationships but also to build new ones (Kim et al., 2011; Wolfradt & Doll, 2001). Users can browse on the SNS and read others’ profiles and initiate new connections by sending messages or gossiping. Loneliness seems to affect the importance of meeting new people as a motive for social media engagement. McKenna and coworkers (McKenna & Bargh, 1999; McKenna, Green, & Gleason, 2002) conducted a series of studies and found that lonely people were more likely to use online communications to establish close relationships with strangers. Particularly for adolescents, Gross, Juvonen, and Gable (2002) showed that lonelier teenagers in school were inclined to communicate online more than offline with people they did not know. The degree of loneliness, therefore, also affects the level of gratifications obtained from social media use with respect to meeting new people. Lonely people possibly may benefit more from social media for the purpose of meeting new people than less lonely individuals.

Loneliness and entertainment gratifications

Entertainment is another gratification sought for and provided by using social media, allowing filling up spare time, taking a break, and having fun (Ellison et al., 2006). Students employ SNSs for pleasure and to exchange information, experience, music, or videos with their friends. More lonely individuals may have a stronger motivation to communicate online for entertainment purposes as they may have stronger needs to unwind, rest, or feel less tense compared with less lonely people (Rubin et al., 1988). Leung (2007) argued that children and adolescents engage on social media in particular in a search to alleviate loneliness with online entertainment. The entertainment value of using social media may, therefore, also depend on the level of loneliness experienced by the adolescent when engaging with social media.

As the review of extant literature indicates, findings of a number of previous studies support the proposition that variations in teenagers’ level of loneliness may affect the degree to which they experience gratifications from social media engagement. In line with Rubin, Perse, and Powell’s (1985) findings regarding conventional media use, the distinction between different types of loneliness may advance the understanding of each gratification obtained in a social media context. We propose the following research questions, which are addressed in the field study:

RQ1: Does the degree of loneliness have an effect on social gratifications (social inclusion and meeting new people) and entertainment gratifications obtained from SNS interaction?

RQ2: Does SNS use provide these gratifications to users who have a high degree of loneliness?

Materials and Methods

A total of 344 teenagers (aged 12–17 years, M_age = 13.75, SD_age = 1.58, 52% female and 48% male) from three Spanish school centers who were users of the SNS Tuenti were surveyed. Participants responded to an anonymous questionnaire assessing students’ levels of loneliness and gratifications obtained from using Tuenti. The authors secured the collaboration of the heads of the participating school centers, who contacted the parents of the students. Parents provided written consent for the participation of their children. Participants responded to the questionnaires during class hours at the beginning of a regular class in the presence of the corresponding teacher and one of the authors of the study.

Russell’s (1996) UCLA Loneliness scale was used to measure students’ level of loneliness. Participants rated their degree of agreement with a list of 10 statements. Exploratory factor analysis was conducted over all items. To measure the level of loneliness, a subset of three items with the highest factor loadings was selected: “I feel lonely” (y₁), “I have no company” (y₂), and “I feel isolated” (y₃). The items had four response alternatives: never = 1, rarely = 2, sometimes = 3, and very often = 4. A lower score indicated that students suffered less from loneliness, and a higher score indicated that students suffered more from loneliness. Cronbach’s alpha for the selected items of Russell’s UCLA Loneliness scale was .72.

To assess social media gratifications, respondents were given a list of items from the literature (Dunne et al., 2010; Peter, Valkenburg, & Schouten, 2006) regarding meeting new people (MNP), entertainment (ET), and social inclusion (SI). A subset of three items per gratification type was selected using exploratory factor analysis; three items regarding MNP (When I use Tuenti, “I feel that I can meet new people” [y₄], “I feel that I can make new friends” [y₅], and “I feel that people I do not know are interested in contacting me” [y₆]); another three items regarding ET (When I participate in Tuenti, “I enjoy gossiping with my friends” [y₇], “It pleases me” [y₈], and “It entertains me so I don’t get bored” [y₉]); and three items regarding SI (When I use Tuenti, “I feel I belong to a group” [y₁₀], “I feel I am a member of something” [y₁₁], and “I feel I belong to my friends’ chat group” [y₁₂]). Response options ranged from 1 (not at all) to 4 (very much). A higher score indicated that students perceived higher gratifications from using Tuenti. Cronbach’s alpha was .82 for MNP items and .74 for the ET and SI items. Furthermore, the questionnaire measured time spent on sports, reading, and offline relations. Response options ranged from 1 (less than 15 minutes daily) to 5 (more than 3 hours daily).

The aim of this research was to analyze the influence of loneliness on gratifications derived from online SNS interaction, taking into account different levels of self-reported loneliness. For this purpose, we employed first LCA to classify individuals by the responses given to the loneliness items and study the potential differences among lonely individuals. In LCA, the latent classes (categorical variables) explain the relationship among the items in a similar way to exploratory factor analysis (Muthén & Muthén, 2009). Then, SEMM including both categorical (latent classes) and continuous variables (loneliness and gratifications items) was conducted. All models were estimated using Mplus 5.0 (Muthén & Muthén, 2009).

Results

Regarding the number of classes identified with LCA, as Muthén (2003) suggested, to choose one from a set of plausible models, we used the strategy of checking them statistically and substantively, as the literature lacks a unique statistical indicator commonly accepted for determining the most adequate number of classes. Jung and Wickrama (2008) and Nylund (2007) recommended that the number of classes should also depend on a combination of factors, such as fit indices, parsimony, substantive justification, and interpretability. Following these authors, in terms of model selection, we employed the likelihood ratio test and the Bayesian information criterion (BIC) value. The number of classes can be tested using the likelihood ratio test between nested models (that is, models with unrestricted and restricted covariance matrices) and information criteria, such as the BIC (Schwarz, 1978), which selects also among a set of nested models and constitutes the most commonly used indicator for class constitution (Magidson & Vermunt, 2004; Nylund, Asparouhov, & Muthén, 2007). The BIC value allows for the comparison of models based on their model fit and parsimony (Magidson & Vermunt, 2004). The model with the highest likelihood and the lowest information criterion value is deemed to be the best-fitting model. Therefore, we used the absolute value of these fit indicators to determine the number of classes that provided the most meaningful and statistically sound results. In terms of classification quality or adequacy, another method for evaluating latent classes based on the separation of the classes is the entropy-based R², the percentage of participants included within each class and the average latent class probabilities for most likely latent class membership (Vermunt & Magidson, 2002). These model fit indexes identify the model with the least number of classes that best fits the data. The method is based on the proportional reduction of classification errors (Vermunt & Magidson, 2000). Entropy in the LCA context was introduced by Ramaswamy, Desarbo, Reibstein, and Robinson (1993) as a general measure used to measure the level of unclearness in class membership. The entropy-based R² indicates how well one can predict classes from the measures (Wedel & Kamakura, 2000). Entropy R² is near zero when the latent classes are not distinct, and entropy values close to 1 suggest that latent classes have clear distinctions. Thus, the predictions are better (Gudicha, Tekle, & Vermunt, 2016). Together, the fit indicators, log-likelihood and the BIC, as well as the effect size indicator entropy R², are used to identify the model with the least number of classes that best fits the data. Table 1 shows the estimated indicators for the six models estimated with the number of classes ranging from one to six.

Table 1.

LCA Fit Indexes for Loneliness Items.

Model	Log-likelihood	Parameter	BIC	Entropy R²
One class	−880.34	6	1795.56	–
Two classes	−647.57	10	1353.28	1
Three classes	−212.80	14	506.99	1
Four classes	−176.03	18	456.71	0.92
Five classes	−162.24	22	452.40	0.91
Six classes	−158.54	26	468.24	0.91

Note. BIC = Bayesian information criterion.

Classes

The model with the highest likelihood and the lowest value of an information criterion is deemed the best-fitting model; regarding classification adequacy, the entropy R² values near 1 are considered of high classification quality (Jung & Wickrama, 2008). Table 1 shows that as the number of classes increases, the model fit (considering the likelihood ratio test and the BIC value) improves. Nevertheless, as the number of classes increases, the number of individuals in each class also becomes smaller. Masyn, Henderson, and Greenbaum (2010) recommended also examining the class size and proportions, because an overextraction of classes can lead to small and nondistinct classes. Additional analyses examining these class sizes and proportions showed that with more than three classes, the percentage of individuals who belong to some of the classes is already lower than 3%, which adds very little significant information about the classes’ composition. In addition, a greater variation in the likelihood and the BIC value can be observed when the two- and three-class models are compared than in the rest of the model comparisons according to the number of classes. Regarding the entropy value R² with four and more classes, this value decreases slightly, and the latent class membership according to average latent class probabilities gives less clear results. For example, with four classes, the fourth class has a probability of .84 of belonging to the fourth class and a probability of .16 of belonging to the first class. Therefore, the quality of some classes’ assignment becomes questionable with more than three classes.

Consequently, the variation in students’ loneliness levels was best represented in a model comprising three loneliness classes. Table 2 presents the percentages of responses to the different loneliness items, classifying individuals according to their level of loneliness. Table 3 shows the mean values of the time dedicated to sports, reading, and offline relationships, depending on the level of loneliness.

Table 2.

Characteristics of Different Classes.

	Absence of loneliness	Intermediate level of loneliness	High level of loneliness
“I feel lonely”
Never	80%	34%
Rarely	17%	56%	46%
Sometimes	2%	8%	46%
Very often	1%	2%	8%
“I have no company”
Never	83%	39%	15%
Rarely	16%	50%	46%
Sometimes		11%	23%
Very often			15%
“I feel isolated”
Never	100%
Rarely		100%
Sometimes			92%
Very often			8%

Table 3.

Mean Values and Variances for Each of the Loneliness Levels and Time Dedicated to Sports, Reading, and Offline Relations.

	Absence of loneliness	Intermediate level of loneliness	Higher level of loneliness
“I feel lonely”	1.24 (0.54)	1.79 (0.66)	2.62 (0.65)
“I have no company”	1.18 (0.40)	1.72 (0.66)	2.39 (0.96)
“I feel isolated”	1.01 (0.09)	2.00 (<0.01)	3.08 (0.28)
Time dedicated to sports	2.68 (0.42)	2.07 (0.34)	1.61 (0.20)
Time dedicated to reading	1.44 (0.27)	1.54 (0.25)	1.77 (0.30)
Time dedicated to offline relationships	3.97 (0.58)	3.48 (0.28)	3.77 (0.33)

Absence of loneliness (Class 1)

This is the largest class, containing the majority of the individuals (75.7% of participants). The LCA grouped in this class those participants who reported never having felt lonely, lack of company, or isolation (Table 2). They were characterized by the lack of loneliness and spent more time, on average, on sports and offline relationships and less time dedicated to reading (see Table 3).

Intermediate level of loneliness (Class 2)

This class contained 21% of the sample and was characterized by an intermediate level of loneliness. Individuals in this class felt lonely, but without this circumstance as problematic or as acute as for the individuals in Class 3. This class was composed of those who mostly responded rarely to the categories of feeling lonely, lack of company, or feeling isolated. These SNS users spent less time, on average, on sports than the nonlonely but more time, on average, than those who had a higher level of loneliness. This class also spent less time on offline relationships. They devoted more time to reading than the nonlonely but less time than those who feel lonelier.

Higher level of loneliness (Class 3)

This class contained 3.3% of the sample and was characterized by the highest level of loneliness. This class was composed of those who mostly responded sometimes in terms of feeling lonely, lacking company, or feeling isolated. The classification is consistent with Russell’s (1996) results for the UCLA Loneliness scale, in which responding sometimes to the different items reflects a high level of loneliness. Individuals in this group spent the least amount of time on sports and devoted the greatest amount of time to reading.

In the next step of the analysis, a confirmatory SEM model was specified assuming three distinct classes that simultaneously included loneliness and gratification obtained. The analysis was conducted using SEMM with categorical and continuous latent variables. SEMM is a combination of LCA (Lazarsfeld & Henry, 1968) and factor analysis (Jöreskog, 1969) which identifies and estimates the pertinent parameters of the three different classes. The specified confirmatory model established that the gratification latent variable (F2) was regressed on loneliness (F1). The proposed model is depicted in Figure 1 for the MNP variables. The specification shown is valid for the three types of gratifications replacing the MNP indicator items with ET indicators, y₇, y₈, and y₉, and SI indicators, y₁₀, y₁₁, and y₁₂, respectively.

Figure 1.

Structural equation mixture model.

The solid arrows from the categorical latent variable c to F1 and F2 indicate that the mean of F1 and the intercept of F2 vary across classes. The broken arrow from c to the arrow from F1 to F2 indicates that the slope of the linear regression of F2 on F1 varies across classes too (Jedidi, Jagpal, & DeSarbo, 1997). To test whether the confirmatory model outperformed the exploratory one, we compared the SEMM goodness-of-fit coefficients for different specifications (SEMM1 and SEMM2) with the goodness-of-fit coefficients of the exploratory model (estimated through LCA) including loneliness and gratification items with no causal relation between them. In SEMM1, all the slopes are estimated freely in all the classes and in SEMM2, additionally, the intercepts are estimated freely. The likelihood ratio test and the BIC value were employed for the model comparison. The BIC values can be used to compare models with the same number of latent classes, with different parameterizations, or both. The results in Table 4 show that, in terms of log-likelihood and the BIC, the SEMM models (confirmatory models that included regression between loneliness and gratification; F2 regressed on F1) outperformed the LCA model (no regression specified between loneliness and gratification). Table 5 shows the path coefficient estimates between loneliness and the perceived gratifications.

Table 4.

LCA and SEMM Fits: Loneliness and Gratification Items.

	Log-likelihood	Parameter	BIC
Model (LN and social inclusion)
LCA	−1692.78	26	3536.58
SEMM1	−1511.17	29	3190.79
SEMM2	−1488.16	46	3243.49
Model (LN and meeting new people)
LCA	−1696.11	26	3543.24
SEMM1	−1459.21	29	3086.87
Model (LN and entertainment)
LCA	−1410.50	26	2972.17
SEMM1	−1302.15	29	2772.91

Note. BIC = Bayesian information criterion; LN = loneliness; LCA = latent class analysis; SEMM = structural equation mixture modeling.

Table 5.

Path Coefficients Between Loneliness and Each Gratification.

	Estimate	p
Social inclusion
Class 1 (C1 = 75.7%)	−1.02	.02**
Class 2 (C2 = 21%)	0.97	.04**
Class 3 (C3 = 3.3%)	−1.02	.10*
Meeting new people
Class 1 (C1 = 75.7%)	0.03	.04**
Class 2 (C2 = 21%)	0.01	.12
Class 3 (C3 = 3.3%)	<0.01	.64
Entertainment
Class 1 (C1 = 75.7%)	−0.60	<.01**
Class 2 (C2 = 21%)	−0.75	.460
Class 3 (C3 = 3.3%)	−0.80	.01**

Note. *p < .10. **p < .05.

Loneliness and meeting new people

The estimated path coefficient between loneliness and the gratification construct in relation to meeting new people through an SNS was statistically significant (p < .05). However, the value over all classes of this coefficient was nearly zero; therefore, the level of loneliness hardly predicted the gratification of meeting new people.

Loneliness and entertainment

Regarding entertainment, the coefficients for Classes 1 and 3 were statistically significant at the 5% level and negative. For Class 1 (individuals who did not feel alone), the lower the level of loneliness, the more the corresponding participants stated that they were entertained. For Class 3 (composed of individuals with the highest level of loneliness), the more they felt alone, the lower their feeling of being entertained by the SNS.

Loneliness and social inclusion

The path coefficients obtained in Classes 1 and 2 in relation to the gratification of being socially included were statistically significant at the 5% level, and the path in Class 3 was marginally statistically significant (p = .10). Because the path coefficient in Class 1 was negative, the lower the feeling of being alone, the greater the gratification of being socially included. In the case of Class 2, this coefficient was positive, and, therefore, the greater the feeling of loneliness, the greater the gratification of being socially included when using the SNS. In this latter case, SNSs could be regarded as an appropriate coping strategy for individuals who feel moderately lonely. Finally, in Class 3, the path coefficient was marginally statistically significant and negative, indicating that the higher the level of loneliness in these teenagers, the less they perceived that social media provided the gratification of being socially included. In this last case, SNS interaction cannot be regarded as an appropriate coping strategy for overcoming loneliness.

Discussion and Theoretical Implications

This study contributes to an enhanced understanding of the association between different levels of loneliness and gratifications obtained among Spanish secondary school students when they use the friend-networking site type of social media. This study adopted UGT as a framework to analyze the influence of loneliness on social media gratifications obtained by teenagers using the Spanish friend-networking site Tuenti, confirming UGT as a useful approach for the study of gratifications obtained from social media of this particular type.

Findings provide further support to previous research suggesting that the media gratifications obtained depend on the individual’s psychological and social characteristics (e.g., Lou et al., 2012; Orchard & Fullwood, 2010). In particular, the present study provides an answer to our first research question on the relationship between the level of loneliness experienced by an individual and the degree of social gratifications (social inclusion and meeting new people) and entertainment gratifications obtained from SNS interaction (RQ1). Results showed that the perception of gratifications varied significantly depending on the level of loneliness of the adolescent friend-networking site user. Latent class analysis revealed three clearly defined segments of teenagers regarding their level of loneliness, and SEMM showed that loneliness influenced the perception of gratifications from SNS use, with influences depending on the level of loneliness. Whereas McKenna et al. (2002) found that lonely people have a propensity to use friend-networking sites to make friends with strangers, we did not find any relationship between loneliness and the gratification of meeting new people. The Tuenti SNS seems to be employed basically for contacting current friends to maintain a relationship, rather than for making new ones (Monge-Benito & Olabarri-Fernández, 2011). SNSs may, in general, be used less for making new acquaintances than for maintaining existing relations because of a lack of trust in online social relationships (Boyd, 2003; Vergeer & Pelzer, 2009).

For the group of individuals who reported never having felt lonely (absence of loneliness), we found inverse relationships between loneliness and both entertainment and social inclusion. Consequently, for nonlonely adolescents, friend-networking sites seem to provide a good medium for being entertained and achieving their goal of belonging to a group or gaining acceptance. This finding is in line with Sheldon (2008) who argued that Facebook benefits extraverted individuals, as they have more Facebook friends than introverts who feel more nervous in offline relationships.

As for the group of individuals who reported feeling lonely sometimes (medium level of loneliness), we identified a negative relationship between loneliness and entertainment. This group of teenagers tends to feel occasionally lonely, and the lonelier they feel, the lower their perception of being entertained by the SNS interaction. Contrary to Leung’s (2007) findings for general Internet use, in the present study, the more lonely adolescents perceived that SNSs did not help them to feel entertained. In this case, social media would not constitute a suitable medium on which to obtain entertainment gratifications but instead, may turn out to be a source of frustration if individuals were seeking entertainment through this channel.

Although Reich (2010) argued that SNSs like Facebook and MySpace do not reflect a sense of community but a networked individualism, a positive relationship between loneliness and perception of social inclusion was found regarding the group of teenagers who reported sometimes feeling alone (intermediate level of loneliness). As McKenna and Bargh (1999) argued, people with a greater need for social inclusion with others may be more likely to use an SNS with the aim of satisfying their need to belong. According to the results of this study, the expectations of such teenagers can be satisfied through the use of friendship networking sites, such as Tuenti. Consequently, social media use could be considered a coping strategy for overcoming some of the problems arising from moderate but not severe loneliness.

Nevertheless, for the individuals who composed the third class and who reported a high level of loneliness, interaction on Tuenti did not fulfill their expectation of becoming socially included. Findings for this specific group provided a negative answer to the research question asking whether social media gratifications are also experienced significantly by users with a high degree of loneliness (RQ2). Thus, Reich’s (2010) pessimistic view on the sense of community provided by Facebook and MySpace might apply in this case. A possible explanation may be provided by Sheldon (2008), who showed that respondents who felt anxiety and fear in their offline relationships used Facebook more than others to feel less lonely, but they failed to obtain Facebook friends. As Canary and Spitzberg (1993) argued, individuals with an intermediate level of loneliness might expect to receive certain gratifications from the media. In contrast, when people experience a more persistent and severe loneliness (consistent with the high level of loneliness identified in this study), the gratifications they receive via SNS interactions are limited. Teenagers with high levels of loneliness seem to perceive that social media interaction on Tuenti does not compensate for the lack of offline social relationships.

Results have practical implications for the prevention, in the last resort, mitigation, and treatment of teenager’s loneliness by showing that substituting offline relationships with social media contacts on friend-networking sites may not provide the desired relief from loneliness. In addition, because social media are also increasingly relevant for educational purposes, educators need to understand the social and cultural factors involved in teenagers’ engagement with those technologies (Ahn, 2011; Greenhow, 2011). The findings for the relationship between social gratifications and loneliness have important implications for educators. Group identity is a critical component in developing effective classroom management offline and online, when social media are utilized as educational channels. Because virtual communities also affect group identity, those network relations in social media of the friend-networking site type affect group identity and classroom group management in the online and offline classroom (Ho & Lin, 2016). Vayre and Vonthron (2017) showed that social support and social integration play an important role in student engagement with online learning environments and that loneliness is related to disengagement from such studies. When students feel lonely due to the lack of social interactivity online, they tend to finally drop their online course (El Said, 2016).

In addition, social media use within education allows a personalized learning environment approach. Personalization has been shown to determine students’ loyalty to social media learning (Arquero, Salvador del Barrio-García, & Esteban Romero-Frías, 2017). A personalized approach to educational computing should take into consideration the personal characteristics and situational aspects of students. The present study showed that the level of loneliness constitutes an important factor and should therefore be assessed by educators in an online classroom setting. In light of these findings, educators should adapt the use of social media for educational purposes to the level of loneliness of their students. Although for less lonely students, social media might constitute an appropriate tool providing entertainment and gratifications that might stimulate learning motivation, adolescents experiencing severe loneliness may benefit less from the use of these new technologies as teaching methodologies. Schools should consider that using social networks might intensify lonely students’ feelings of frustration, because their expectations of being entertained and socially included may not be met. In particular, these teenagers may still feel lonely when communicating online in a social media learning environment, and loneliness may interfere with their academic engagement and performance in online courses (El Said, 2016; Vayre & Vonthron, 2017). For those students, a traditional face-to-face setting may be a more appropriate and gratifying approach.

Conclusions

Findings of this study contribute to the understanding of the mechanism through which the degree of gratifications provided by social media interaction on friend-networking sites is regulated by the level of loneliness of the individual. In line with Brandtzæg (2010), we estimate that such knowledge has implications for the further design of SNSs, its educational uses, and for the development of effective policies aimed at mitigating potentially aversive consequences in secondary school students. Regarding the practical implications of this study, teenagers with a severe level of loneliness should avoid using friend-networking sites as a way to feel entertained or to be socially included. However, the use of the SNS Tuenti increases the feeling of social inclusion for those who feel less lonely already. Social intervention addressing teenage loneliness and educational computing methods should adapt to the level of loneliness experienced by adolescent social media users.

This study has several limitations. Any inferences about causal direction among variables must be considered tentative because of the cross-sectional character of the data. The representativeness of the findings is limited by the country-specific sample (Spanish teenagers with an online Tuenti profile). Future research should verify these results in other countries to test the existence of possible differences related to social media gratifications of lonely and nonlonely young SNSs users. This study is also not a direct test of uses and gratifications theory. UGT is indirectly confirmed by the validity of the gratifications measure. Future research should also address the relationship between motivational factors of adolescent’s social media use, loneliness, and gratifications obtained, allowing for a more complete analysis of the implications of the loneliness variable for UGT. Furthermore, this study may be limited by the lack of a differentiation of SNS user types, and therefore, we recommend that future research should apply a user typology approach based, for instance, on the unified media user typology developed by Brandtzæg (2010). The combination of data covering loneliness level, perceived gratifications, and SNS user types might allow a deeper and more precise analysis of the relationship between loneliness and differences in social media gratifications. A further line of enquiry arises from the increasing multimodal element to social media platforms (e.g., Facebook Live). Future research should reassess the relationship between gratifications and loneliness levels as the interactivity of social media increases.

Footnotes

Acknowledgments

The authors are grateful for the collaboration of the three school centers that participated in this study. And they also thank the five anonymous reviewers for their constructive comments that contributed significantly to this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors also acknowledge research grants that provided financial support for this research: GIC 12/57, FESIDE 2015, and ECO2016-76348-R.

Author Biographies

Cristina López is an assistant professor of marketing at the University of the Basque Country UPV/EHU, Bilbao, Spain. Her current research focuses on social media in education and consumer behavior, as well as statistical analysis in psychographic segmentation.

Patrick Hartmann is an associate professor (Profesor Titular Universidad) of marketing at the University of the Basque Country UPV/EHU, Bilbao, Spain. His current research interests are in consumer behavior, social media communications, advertising, and sustainability.

Vanessa Apaolaza is an associate professor (Profesor Titular Universidad) of marketing at the University of the Basque Country UPV/EHU, Bilbao, Spain. Her current research focuses on consumer behavior, social media, well-being, and sensory marketing.

References

Ahn

(2011) Digital divides and social network sites: Which students participate in social media? Journal of Educational Computing Research 45(2): 147–163.

Amichai-Hamburger

Ben-Artzi

(2003) Loneliness and Internet use. Computers in Human Behavior 19(1): 71–80.

Apaolaza

Hartmann

Medina

Barrutia

J. M.

Echebarria

(2013) The relationship between socializing on the Spanish online networking site Tuenti and teenagers’ subjective wellbeing: The roles of self-esteem and loneliness. Computers in Human Behavior 29: 1282–1289.

Arquero

J. L.

Del Barrio-García

Romero-Frías

(2017) What drives students' loyalty-formation in social media learning within a personal learning environment approach? The moderating role of need for cognition. Journal of Educational Computing Research 55: 495–525. doi:10.1177/0735633116672056.

Asher

S. R.

Paquette

J. A.

(2003) Loneliness and peer relations in childhood. Current Directions in Psychological Science 12(3): 75–78.

Beck

A. T.

Young

J. E.

(1978) College blues. Psychology Today 12(4): 80–95.

Best

Manktelow

Taylor

(2014) Online communication, social media and adolescent wellbeing: A systematic narrative review. Children and Youth Services Review 41: 27–36.

Boyd

(2003) The rhetorical construction of trust online. Communication Theory 13: 392–410.

Brandtzæg

P. B.

(2010) Towards a unified media-user typology (MUT): A meta-analysis and review of the research literature on media-user typologies. Computers in Human Behavior 26(5): 940–956.

10.

Burke

Marlow

Lento

(2010) Social network activity and social wellbeing. Proceedings of the SIGCHI conference on human factors in computing systems, Atlanta, Georgia, 1909–1912, New York, NY: ACM.

11.

Cacioppo

J. T.

Patrick

(2008) Loneliness: Human nature and the need for social connection, New York, NY: Norton.

12.

Cacioppo

J. T.

Hughes

M. E.

Waite

L. J.

Hawkley

L. C.

Thisted

R. A.

(2006) Loneliness as a specific risk factor for depressive symptoms: Cross-sectional and longitudinal analyses. Psychology and Aging 21(1): 140–151.

13.

Canary

Spitzberg

(1993) Loneliness and media gratifications. Communication Research 20(6): 800–821.

14.

Cicognani

Klimstra

Goossens

(2014) Sense of community, identity statuses, and loneliness in adolescence: A cross-national study on Italian and Belgian youth. Journal of Community Psychology 42(4): 414–432.

15.

Diddi

LaRose

(2006) Getting hooked on news: Uses and gratifications and the formation of news habits among college students in an Internet environment. Journal of Broadcasting & Electronic Media 50(2): 193–210.

16.

Dunne

Lawlor

Rowley

(2010) Young people’s use of online social networking sites—A uses and gratifications perspective. Journal of Research in Interactive Marketing 4(1): 46–58.

17.

El Said

G. R.

(2016) Understanding how learners use massive open online courses and why they drop out thematic analysis of an interview study in a developing country. Journal of Educational Computing Research 55: 724–752. doi:10.1177/0735633116681302.

18.

Ellison

Heino

Gibbs

(2006) Managing impressions online: Self-presentation processes in the online dating environment. Journal of Computer-Mediated Communication 11(2): 415–441.

19.

Erikson

(1963) Childhood and society, 2nd ed. New York, NY: Norton.

20.

Gee

J. P.

Hayes

(2012) Nurturing affinity spaces and game-based learning. Games, Learning, and Society: Learning and Meaning in the Digital Age 123: 1–40.

21.

Giordano

P. C.

(2003) Relationships in adolescence. Annual Review of Sociology 29: 257–281.

22.

Grant

I. C.

(2005) Young peoples’ relationship with online marketing practices: An intrusion too far? Journal of Marketing Management 21(5/6): 607–623.

23.

Grant

I. C.

O’Donohoe

(2007) Why young consumers are not open to mobile marketing communications. International Journal of Advertising 26(2): 223–246.

24.

Greenhow

(2011) Youth, learning, and social media. Journal of Educational Computing Research 45(2): 139–146.

25.

Gross

E. F.

Juvonen

Gable

S. L.

(2002) Internet use and well-being in adolescence. Journal of Social Issues 58: 75–90.

26.

Gudicha

D. W.

Tekle

F. B.

Vermunt

J. K.

(2016) Power and sample size computation for Wald tests in latent class models. Journal of Classification 33(1): 30–51.

27.

Harter

(1999) The construction of the self: A developmental perspective, New York, NY: Guilford Press.

28.

Heinrich

L. M.

Gullone

(2006) The clinical significance of loneliness: A literature review. Clinical Psychology Review 26(6): 695–718.

29.

T. K.

Lin

Y. T.

(2016) The effects of virtual communities on group identity in classroom management. Journal of Educational Computing Research 54(1): 3–21.

30.

Jedidi

Jagpal

H. S.

DeSarbo

W. S.

(1997) Finite mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science 16: 39–59.

31.

Jöreskog

K. G.

(1969) A general approach to confirmatory maximum likelihood factor analysis. Psychometrika 34: 183–202.

32.

Jung

Wickrama

K. A. S.

(2008) An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass 2(1): 302–317.

33.

Katz

Haas

Gurevitch

(1973) On the use of the mass media for important things. American Sociological Review 38: 164–181.

34.

Kim

LaRose

Peng

(2009) Loneliness as the cause and the effect of problematic Internet use: The relationship between Internet use and psychological well-being. CyberPsychology & Behavior 12(4): 451–455.

35.

Kim

K. S.

Yoo-Lee

Sin

S. C. J.

(2011) Social media as information source: Undergraduates' use and evaluation behavior. Proceedings of the American Society for Information Science and Technology 48(1): 1–3.

36.

Kippax

Murray

J. P.

(1980) Using the mass media: Need gratification and perceived utility. Communication Research 7: 333–360.

37.

Cho

C. H.

Roberts

M. S.

(2005) Internet uses and gratifications: A structural equation model of interactive advertising. Journal of Advertising 34(2): 57–70.

38.

Koh

Kim

Y. G.

(2004) Knowledge sharing in virtual communities: An e-business perspective. Expert Systems with Applications 26(2): 155–166.

39.

Kraut

Kiesler

Boneva

Cummings

Helgeson

Crawford

(2002) Internet paradox revisited. Journal of Social Issues 58(1): 49–74.

40.

Kraut

Patterson

Lundmark

Kiesler

Mukophadhyay

Scherlis

(1998) Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist 53(9): 1017–1031.

41.

LaRose

Mastro

Eastin

M. S.

(2001) Understanding Internet usage a social-cognitive approach to uses and gratifications. Social Science Computer Review 19(4): 395–413.

42.

Larson

R. W.

(1999) The uses of loneliness in adolescence. In: Rotenberg

K. J.

Hymel

(eds) Loneliness in childhood and adolescence, Cambridge, England: Cambridge University Press, pp. 244–262.

43.

Lazarsfeld

Henry

(1968) Latent structure analysis, New York, NY: Houghton Mifflin.

44.

Leary

M. R.

Cox

C. B.

(2008) Belongingness motivation: A mainspring of social action. In: Shah

J. Y.

Gardner

W. L.

(eds) Handbook of motivation science, New York, NY: Guilford Press, pp. 27–40.

45.

Lee

C. S.

Goh

D. H.

Chua

A. Y. K.

Ang

R. P.

(2010) Indagator: Investigating perceived gratifications of an application that blends mobile content sharing with gameplay. Journal of the American Society for Information Science and Technology 61(6): 1244–1257.

46.

Lee

C. S.

(2012) News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior 28: 331–339.

47.

Leung

(2002) Loneliness, self-disclosure, and ICQ (“I seek you”) use. Cyberpsychology & Behavior 5: 241–251.

48.

Leung

(2007) Stressful life events, motives for Internet use, and social support among digital kids. Cyberpsychology & Behavior 10: 204–214.

49.

Leung

(2009) Users-generated content on the Internet: An examination of gratifications, civic engagement, and psychological empowerment. New Media & Society 11(8): 1327–1347.

50.

Lou

L. L.

Nickerson

McMorris

(2012) An examination of the reciprocal relationship of loneliness and Facebook use among first-year college students. Journal of Educational Computing Research 46(1): 105–117.

51.

Magidson

Vermunt

(2004) Latent class models. In: Kaplan

(ed.) Handbook of quantitative methodology for the social sciences, Newbury Park, CA: Sage, pp. 175–198.

52.

Masyn

Henderson

C. E.

Greenbaum

P. E.

(2010) Exploring the latent structures of psychological constructs in social development using the dimensional—categorical spectrum. Review of Social Development 19(3): 470–493.

53.

McKenna

K. Y. A.

Bargh

J. A.

(1999) Causes and consequences of social interaction on the Internet: A conceptual framework. Media Psychology 1: 249–269.

54.

McKenna

K. Y. A.

Green

A. S.

Gleason

M. E. J.

(2002) Relationship formation on the Internet: What’s the big attraction? Journal of Social Issues 58(1): 9–31.

55.

Monge-Benito

Olabarri-Fernández

M. E.

(2011) Los alumnos de la UPV/EHU frente a Tuenti y Facebook: Usos y percepciones [Students of the UPV/EHU: Uses and perceptions of Tuenti and Facebook]. Revista Latina de Comunicación Social 66: 79–100.

56.

Morahan-Martin

(1999) The relationship between loneliness and Internet use and abuse. Cyberpsychology & Behavior 2(5): 431–439.

57.

Morahan-Martin

Schumacher

(2000) Incidence and correlates of pathological Internet use among college students. Computers in Human Behavior 16(1): 13–29.

58.

Muthén

B. O.

(2003) Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003). Psychological Methods 8: 369–377.

59.

Muthén

L. K.

Muthén

B. O.

(2009) Mplus user’s guide, 5th ed. Los Angeles, CA: Author.

60.

Nylund

K. L.

(2007) Latent transition analysis: Modeling extensions and an application to peer victimization (Unpublished doctoral dissertation), Los Angeles: University of California.

61.

Nylund

K. L.

Asparouhov

Muthén

B. O.

(2007) Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling 14(4): 535–569.

62.

Orchard

L. J.

Fullwood

(2010) Current perspectives on personality and Internet use. Social Science Computer Review 28(2): 155–169.

63.

Palmgreen

Rayburn

J. D.

(1985) A comparison of gratification models of media satisfaction. Communication Monographs 52: 334–346.

64.

Papacharissi

Mendelson

(2011) Toward a new(er) sociability: Uses, gratifications, and social capital on Facebook. In: Papathanassopoulos

(ed.) Media perspectives for the 21st century, New York, NY: Routledge, pp. 199–2011.

65.

Park

Kee

K. F.

Valenzuela

(2009) Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior 12(6): 729–733.

66.

Peplau

L. A.

Perlman

(1982) Perspectives on loneliness. In: Peplau

L. A.

Perlman

(eds) Loneliness: A sourcebook of current theory, research and therapy, New York, NY: Wiley, pp. 1–20.

67.

Perlman

(1988) Loneliness: A life span, developmental perspective. In: Milardo

R. M.

(ed.) Families and social networks, Newbury Park, CA: Sage, pp. 190–220.

68.

Perse

E. M.

Rubin

A. M.

(1990) Chronic loneliness and television use. Journal of Broadcasting and Electronic Media 34: 37–53.

69.

Peter

Valkenburg

Schouten

(2006) Characteristics and motives of adolescents talking with strangers on the Internet. CyberPsychology & Behavior 9: 526–530.

70.

Pittman

Reich

(2016) Social media and loneliness: Why an Instagram picture may be worth more than a thousand Twitter words. Computers in Human Behavior 62: 155–167.

71.

Pretty

G. M. H.

Andrewes

Collett

(1994) Exploring adolescents’ sense of community and its relationship to loneliness. Journal of Community Psychology 22(4): 346–358.

72.

Qualter

Vanhalst

Harris

Van Roekel

Lodder

Bangee

Verhagen

(2015) Loneliness across the life span. Perspectives on Psychological Science 10(2): 250–264.

73.

Raacke

Bonds-Raacke

(2008) MySpace and Facebook: Applying the uses and gratifications theory to exploring friend-networking sites. Cyberpsychology & Behavior 11(2): 169–174.

74.

Ramaswamy

DeSarbo

W. S.

Reibstein

D. J.

Robinson

W. T.

(1993) An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science 12: 103−124.

75.

Raza

S. A.

Qazi

Umer

(2016) Facebook is a source of social capital building among university students evidence from a developing country. Journal of Educational Computing Research 55: 295–322. doi:10.1177/0735633116667357.

76.

Reich

S. M.

(2010) Adolescents’ sense of community on MySpace and Facebook: A mixed-methods approach. Journal of Community Psychology 38(6): 688–705.

77.

Reich

S. M.

Black

R. W.

Korobkova

(2014) Connections and communities in virtual worlds designed for children. Journal of Community Psychology 42(3): 255–267.

78.

Rokach

Neto

(2000) Coping with loneliness in adolescence: A cross-cultural study. Social Behavior and Personality 28: 329–342.

79.

Rubin

A. M.

(1983) Television uses and gratifications: The interactions of viewing patterns and motivations. Journal of Broadcasting 27: 37–51.

80.

Rubin

A. M.

(2002) The uses and gratifications perspective of media effects. In: Bryant

Zillmann

(eds) Media effects: Advances in theory and research, 2nd ed. Mahwah, NJ: Erlbaum, pp. 525–548.

81.

Rubin

A. M.

Perse

E. M.

Powell

R. A.

(1985) Loneliness, parasocial interaction, and local television news viewing. Communication Research 12(2): 155–180.

82.

Rubin

R. B.

Perse

E. M.

Barbato

C. A.

(1988) Conceptualization and measurement of interpersonal communication motives. Human Communication Research 14: 602–628.

83.

Ruggiero

T. E.

(2000) Uses and gratifications theory in the 21st century. Mass Communication & Society 3(1): 3–37.

84.

Russell

(1996) UCLA loneliness scale (version 3): Reliability, validity, and factor structure. Journal of Personality Assessment 66: 20–40.

85.

Sádaba

Bringué

(2011) Redes sociales. Manual de Supervivencia para padres [Social media. A parent’s survival handbook], Barcelona, Spain: Ed. Viceversa.

86.

Sánchez-Burón, A., & Fernández-Martín, M. P. (2010). Informe Generación 2.0 2010: Hábitos de los adolescentes en el uso de las redes sociales. Estudio comparativo entre Comunidades Autónomas [Generation 2.0 2010 report: Adolescent’s uses of social media]. Retrieved from http://www.slideshare.net/ucjc/generacin-20-hbitos-de-los-ad (accessed January 17, 2015).

87.

Sánchez-Franco

M. J.

Roldán

J. L.

(2010) Expressive aesthetics to ease perceived community support: Exploring personal innovativeness and routinized behavior as moderators in Tuenti. Computers in Human Behavior 26(6): 1445–1457.

88.

Sánchez-Franco

M. J.

Buitrago-Esquinas

E. M.

Hernández-Mogollón

J. M.

(2012) Antecedentes sociales y psicológicos del compromiso comunitario. Un análisis del comportamiento del usuario de una red social de relaciones [Social and psicological antecedents of user behavior in social media]. Cuadernos de Economía y Dirección de la Empresa 15(4): 205–220.

89.

Schinka

K. C.

Van Dulmen

M. H.

Mata

A. D.

Bossarte

Swahn

(2013) Psychosocial predictors and outcomes of loneliness trajectories from childhood to early adolescence. Journal of Adolescence 36: 1251–1260.

90.

Schwarz

(1978) Estimating the dimensions of a model. Annals of Statistics 6: 461–464.

91.

Shaw

L. H.

Gant

L. M.

(2002) In defense of the Internet: The relationship between Internet communication and depression, loneliness, self-esteem, and perceived social support. Cyberpsychology & Behavior 5(2): 157–171.

92.

Sheldon

(2008) The relationship between unwillingness-to-communicate and students’ Facebook use. Journal of Media Psychology 20(2): 67–75.

93.

Van Dulmen

M. H. M.

Goossens

(2013) Loneliness trajectories. Journal of Adolescence 36: 1247–1250.

94.

Vanhalst

Luyckx

Raes

Goossens

(2012) Loneliness and depressive symptoms: The mediating and moderating role of uncontrollable ruminative thoughts. The Journal of Psychology 146(1–2): 259–276.

95.

Vayre

Vonthron

A.-M.

(2017) Psychological engagement of students in distance and online learning: Effects of self-efficacy and psychosocial processes. Journal of Educational Computing Research 55(2): 197–218.

96.

Vergeer

Pelzer

(2009) Consequences of media and Internet use of offline and online network capital and well-being. A causal model approach. Journal of Computer-Mediated Communication 15: 189–210.

97.

Vermunt

J. K.

Magidson

(2000) Latent gold 2.0 user’s guide, Belmont, MA: Statistical Innovations.

98.

Vermunt

J. K.

Magidson

(2002) Latent class cluster analysis. In: Hagenaars

J. A.

McCutcheon

A. L.

(eds) Applied latent class analysis, Cambridge, England: Cambridge University Press, pp. 89–106.

99.

Wedel

Kamakura

W. A.

(2000) Market segmentation: Conceptual and methodological foundations, 2nd ed. Northwell, MA: Kluwer Academic.

100.

Weiqin

E. L.

Campbell

Kimpton

Wozencroft

Orel

(2016) Social capital on Facebook: The impact of personality and online communication behaviors. Journal of Educational Computing Research 54(6): 747–786.

101.

Wolfradt

Doll

(2001) Motives of adolescents to use the Internet as a function of personality traits, personal and social factors. Journal of Educational Computing Research 24(1): 13–27.