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Tweeting to Feel Connected: A Model for Social Connectedness in Online

Social Networks

Christoph Riedl, Felix Köbler, Suparna Goswami, and Helmut Krcmar

Christoph Riedl, Institute for Quantitative Social Science, Harvard University,

[email protected]

Felix Köbler, Information Systems, Technische Universität München, [email protected]

Suparna Goswami, Information Systems, Technische Universität München,

[email protected]

Helmut Krcmar, Information Systems, Technische Universität München, [email protected]

Accepted for Publication at

International Journal of Human-Computer Interaction

(Original Research Article, regular issue)

Please address all correspondence on this paper to Christoph Riedl

Phone: (1) 857 204 9655; Email: [email protected]

TWEETING TO FEEL CONNECTED: A MODEL FOR SOCIAL CONNECTEDNESS IN ONLINE SOCIAL NETWORKS

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Tweeting to Feel Connected: A Model for Social Connectedness in Online

Social Networks1

Abstract. Social connectedness is an indicator of the extent to which people can realize various

network benefits and is therefore a source of social capital. Using the case of Twitter, we develop and test

a theoretical model of social connectedness based on the functional and structural characteristics of

peoples’ communication behavior within a online social network. We investigate how social presence,

social awareness, and social connectedness influence each other, and when and for whom the effects of

social presence and social awareness are most strongly related to positive outcomes in social

connectedness. Specifically, we study the concurrent direct and moderating effect of two structural

constructs characterizing peoples’ online social network: network size and frequency of usage. We test

our research model using data (n = 121) collected from two sources: (1) an online survey of Twitter users

and (2) their usage data collected directly from Twitter. Our results indicate that social awareness, social

presence, and usage frequency have a direct effect on social connectedness, while network size has a

moderating effect. We find social presence partially mediating the relationship between social awareness

and social connectedness. We use the findings of our analysis to outline design implications for online

social networks from a human computer interaction perspective.

Keywords: Online social network, social capital, social presence, social connectedness, social awareness,

Twitter.

1 Acknowledgements. We wish to thank Dominikus Baur and four anonymous reviewers for their

helpful comments. Christoph Riedl acknowledges support from the German Research Foundation (DFG)

under grant code RI 2185/1-1. All mistakes remain the authors’ own.


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Introduction

Benefits accrue to individuals and groups through social interactions (Granovetter, 1973; Bandiera

et al., 2008). Such benefits are termed social capital and can serve as a strong motivation for establishing

and maintaining social connections (Bourdieu, 1986; Adler and Kwon, 2002a). In addition to real life or

offline social interactions for maintaining social connections, various modern means of communication

such as online social network sites (SNS) have evolved into significant means for developing,

maintaining, and strengthening social connections (e.g., Brandtzæg et al., 2010; Vasalou et al., 2010;

Humphreys, 2007; Ellison et al., 2006; Aral and Walker, 2011). Therefore, these online social networking

sites can serve as important sources of social capital and, consequently, the various benefits that can be

realized through enhanced social capital (Ellison et al., 2007; Zhao and Rosson, 2009; Ji et al., 2010).

While prior research has started establishing causal relationships between the use of SNS and social

capital, there is still a limited understanding when and for whom these effects are strongest.

The phenomenal growth of SNS over the last decade has resulted in increased research and

practical interest in them. Accordingly, they serve as a fertile ground for information systems (IS), human

computer interaction (HCI), and social science research (Gu, 2010). There has been research focusing on

the various motivations that people have in joining online social networks (Ellison et al., 2007), on

sharing information in them (Wasko and Faraj, 2005; Brandtzæg et al., 2010), or on the relationship

between online and offline social networks and how these influence each other (Ellison et al., 2007;

Ledbetter et al., 2011; Oxendine et al., 2003). Much of this research is guided by the inherent realization

that SNS can serve to complement and modify peoples’ social interactions and behavioral patterns, and

can result in beneficial outcomes (Zhao and Rosson, 2009). However, there is little understanding of the

actual process through which the benefits associated with participating in such online social networks can

be realized. Assuming that feeling socially connected, or social connectedness, facilitates the accrual of

social capital related benefits, we would like to better understand the factors that can enhance social

connectedness, and the process through which social connectedness is created in online social networks.

Furthermore, in this study we address the question when and for which groups of SNS users the effects


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are strongest. To address this question, we focus our attention specifically on potential moderating effects

of usage frequency and the size of the online social network.

Using the concept of social capital, which encompasses the norms and networks facilitating

collective actions for mutual benefits (Woolcock, 1998) as the overarching framework, we identify two

key factors that engender feelings of connectedness among the users of a SNS. We then empirically

examine our theoretical model using data collected from Twitter users using a combination of survey data

and usage data collected directly from Twitter. Survey research is a highly accepted research method in

many disciplines such as management, information systems, and various social science disciplines, as

well as HCI (e.g., Ren et al., 2011; .Ji et al., 2010; Morris et al., 2010). Twitter is currently one of three

dominant SNS platforms alongside Facebook and Google+ that allows its users to manage social

relationships among each other and provides functionalities to establish an online social network

(Thompson, 2007; Twitter, 2011). Further, Twitter can be considered as a technology-mediated

communication medium that substitutes face-to-face communication to some extent. There is an ongoing

debate on the effectiveness of such media as communication tools and different contexts in which they

can be used as suitable replacements. Our research contributes to this debate by investigating the extent to

which SNS usage results in feelings of connectedness among its users.

This research focuses on understanding the interplay between users’ perceptions regarding SNS

usage and their actual use of it in enhancing social connectedness (Carlson and Zmud, 1999). We address

this question in the context of Twitter by examining both structural and functional characteristics of an

individual’s online social network and the individual’s communication behavior within the network. In

particular, we investigate the interplay between three fundamental constructs of social interactions: social

presence, social awareness, and social connectedness. Furthermore, we study the concurrent direct and

moderating effect of two structural constructs characterizing peoples’ social network (Scott, 2000):

network size and frequency of usage. While prior research has examined and highlighted the importance

of social presence, social awareness, and social connectedness, there is little empirical evidence on how

they influence each other (Rettie, 2003). This lack of knowledge impedes academic and practical


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understanding on SNS and their implications, and limits the ability to systematically design information

systems to address specific goals. Testing our theoretical model thus allows us to address the research

questions (a) how do social presence, social awareness, and social connectedness influence each other,

and (b) when and for whom the effects of social presence and social awareness are most strongly related

to positive outcomes in social connectedness. We also present additional descriptive analyses of users’

usage behavior. Based on the findings of our analyses, we propose design implications that incorporate

our study findings and analyses, and can be used by human computer interaction (HCI) designers for

informed design of online SNS.

The remaining sections of the paper are organized as follows: Section 2 provides an overview of

previous research on online social networking, focusing on social connectedness and social capital.

Section 3 outlines our research model and depicts the theoretical development of our research hypotheses.

Section 4 presents the data collection. Section 5 presents analyses and results of the theoretical model and

Section 6 provides additional descriptive analyses of Twitter usage behavior. Implications of our research

findings are discussed in Section 7.

Literature Review

Online social networking has gained significant popularity over the last decade. It is a form of

computer-mediated communication, facilitated by advances in information and communication

technologies, and the advent of Internet-based applications that allow its users to individually present

themselves through profiles, display their social networks, and establish or maintain connections with

others (Ellison et al., 2007). This novel form of communication and social interaction is increasingly

consuming a significant and growing portion of an individual’s time and attention (Bapna et al., 2011).

Social networking sites that enable individuals to follow the lives of friends, acquaintances and families,

to find experts, and to engage in commercial transactions have grown exponentially in their user numbers

since the turn of this century (Huberman et al., 2008). Examples of prominent SNS are Twitter, Facebook,

and most recently Google+, the three of them represent the most dominant SNS by 2011 in terms of

number of active users.


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Previous Research on Online Social Networks

Early research on computer-mediated communication focused on virtual communities has been

based on the assumption that people participating in them used these platforms to connect with

individuals outside their pre-existing social group or location (Wellman and Gulia, 1999), primarily based

on shared interests or shared life scenarios. For example, the role of virtual communities in providing

informational, emotional, and appraisal support to elderly and patient groups have been investigated

(Leimeister et al., 2008). Current SNS can be distinguished from these virtual communities by the fact

that they are primarily used for the maintenance of existing (offline) social ties, although these platforms

also allow for the formation of purely online ties. For instance, it was found that Facebook was used for

strengthening and intensifying offline relationships that people already have (Ellison et al., 2007; Lampe

et al., 2006).

Research has investigated different aspects of online social networking, such as privacy and

security concerns that users of such networks have and how they affect their usage behavior (Acquisti and

Gross, 2006), different motivations of using SNS, such as for finding and connecting to strangers, how

these sites can be used for keeping up with acquaintances (Köbler et al., 2011; Lampe et al., 2006), how

various functionalities provided by SNS impact social interactions (Boyd and Ellison, 2007), and the use

of social networking sites in a work context (Zhao and Rosson, 2009).

Studies have focused on the formation and dynamics of online social networks and how online

profiles can be created and managed to generate potential offline benefits for individuals, e.g., increasing

the feeling of social connectedness (Köbler et al., 2010), building social capital among its users (Lampe et

al., 2006; Koroleva et al., 2011; Wellman and Gulia, 1999), and creating social support (Leimeister et al.,

2008). Research indicates that social connectedness within SNS is a qualitative source of social capital,

since social connectedness activates and maintains social relationships and in turn results in beneficial

outcomes for individuals, such as a higher social capital (Koroleva et al., 2011). Further, frequent usage

and interaction with other SNS members can result in higher social trust and civic participation among

SNS users (Valenzuela et al., 2009). Accordingly individuals’ offline and online lives overlap and


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influence each other through the use of SNS (Subrahmanyam et al., 2008; Ellison et al., 2007). SNS are

recognized as a complement network of relationships to the offline world by “providing a platform for

active communication between friends and more passive observation through aggregated streams of social

news” (Burke et al., 2010, p. 1909). Participation in SNS is associated with greater feelings of bonding

social capital, emotional support and lower levels of loneliness (Burke et al., 2010; Ellison et al., 2007).

However, none of the studies investigated in detail how this is achieved exactly.

In addition to the several beneficial aspects of SNS use, potential negative consequences from

participation in SNS have also been identified. It has been suggested that information overload caused by

active participation can result in reduced levels of user activity, a negative attitude towards shared

information that potentially lowers the possibility of beneficial aspects such as social capital (Koroleva et

al., 2010). In some cases, information overload might even cause confusions and dysfunctional effects in

the form of stress and anxiety among users of SNS (Eppler and Mengis, 2004). Other studies have

documented that active participation on SNS may result in depression or envy and jealousy of other

individuals (Muise et al., 2009).

The above discussion indicates the possible social benefits of SNS use, however, not every type of

SNS use (Burke et al., 2010) and not every social network (Granovetter, 1973) hold the same potential to

create social value for an individual. Drawing from the concept of social capital, we examine the factors

that enhance and/or explain social capital creation in SNS, which we then empirically investigate using

data collected from users of the Twitter as an example.

Social Capital and Social Connectedness

Social capital is an outcome of network membership and refers to the value of social relations

within and between social networks and the ability to produce collective or individual (economic) benefits

from network members. Networks are the causal agents or moderators of social capital (Williams, 2006).

A social network represents the social ties within which an individual lives and can be characterized by its

structural properties and functional properties (Scott, 2000). The structural properties describe the social

network at large, and are generally measured by the objective characteristics of social networks (Cohen


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and Syme, 1985). For example, the number of ties that a person has in a social network, and frequency of

interactions with other members of the network are examples of structural properties of the social

network. The functional properties are subjective, and can be measured by capturing an individual’s

perceptions regarding the availability and adequacy of resources that can be obtained from the social

network (Cohen and Syme 1985). Therefore, functional properties can influence the general well-being of

network members by providing them with social goods, such as social support and connectedness, and

several forms of social capital (Huysman and Wulf, 2004). Previous research has identified several

beneficial outcomes of social support and social connectedness and has shown that these factors help

people in overcoming social disadvantages like isolation, and strengthen their integration into society

(Cornwell et al., 2008; Pfeil et al., 2011).

Multiple definitions of the concept of social capital can be found in sociology (Woolcock, 1998). It

has been defined as the source of positive externalities for members of a social network that are generated

through shared trust, norms, values, and their consequent effects on expectations and behavior and

broadly refers to the benefits received from social relationships (Adler and Kwon, 2002b). Similar to

physical, financial, and human capital, social capital may facilitate productive activity to individuals or

groups (Coleman, 1988; White, 2002). The beneficial effects of social capital could be of psychological,

emotional, and economical nature (Lin, 1999; 2000).

Social capital that emerges from the use of modern information technology has been referred to as

socio-technical capital (Resnick, 2001), and can be thought of as an artifact of users’ technology usage

behavior. Online social network use can thus enhance users’ social capital (Koroleva et al., 2011).

Therefore, we are interested in understanding the determinants of social capital among the users online

social network sites. In particular, our research identifies the role played by users’ communication

behavior and structural aspects of the network in determining social connectedness.

Social connectedness is a well-established concept in psychology that offers affective benefits like

intimacy, sense of sharing, and stronger group attraction (Ijsselsteijn et al., 2003). A number of studies on

social capital investigated connectedness within communities as an indicator of social capital and


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identified “sense of community” as an indicator and catalyst for behavioral dimensions of social capital

(Stone, 2001; Long and Perkins, 2007). Social connectedness can be viewed as an estimator of the quality

of an individual’s social network and a precursor to social capital (Putnam, 2000), or directly linked to

social capital itself (Bandiera et al., 2008). Therefore, the extent to which network members can

appropriate social capital from the social network will be determined by their connectedness within the

network.

Human beings have the fundamental need to belong and feel connected (Rettie, 2003).

Accordingly, social connectedness is often viewed as a precursor to success in life, and refers to an

individual's attitude and relationship to society. Social connectedness has been identified as one of the

fundamental underlying motivating principles behind social behavior (Smith and Mackie, 2000).

Individuals can assess their social relationships based on the extent to which they feel socially connected.

Connectedness is characterized by a feeling of staying in touch with ongoing social relationships (Romero

et al., 2007), and has been associated with many social and health related benefits and is therefore a

desirable property for individuals to achieve.

SNS as Means of Enhancing Social Connectedness

Social connectedness can be described as the feeling of belonging to a social group and implies the

creation of bonding relationships. Communication is often seen as a means of feeling connected and being

in touch, and computer-mediated communication is increasingly becoming an important way of achieving

social connectedness. Accordingly, social connectedness is considered a potential key concept not only in

the analysis of communication but the development of communication technologies and applications,

such as SNS (Rettie, 2003). Communication can be classified as connectedness-oriented communication

where the focus is mainly on maintaining and enhancing social relationships, and content-oriented

communication, where the goal is exchange of information (Kuwabara et al., 2002). Connectedness-

oriented communication allows people to be aware of each other and contribute to maintain social

relationships (Rettie, 2003).


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It is less clear, however, how social connectedness is achieved exactly. Two candidate constructs

have been suggested in various studies, social presence and social awareness, which we aim to

consolidate in our theoretical model. Social presence is considered an important factor in creating a sense

of community among online communities (Aragon, 2003), while the role of social awareness is

particularly critical in mediated environments given that there is scarcity of real life visual cues regarding

what others are doing in such environments (Bardram and Hansen, 2004). However, there is a lack of

clarity regarding the relationship between these theoretical constructs, the process through which feelings

of social connectedness are created, and further, there is no empirical validation of these relationships in

the context of online SNS. A theoretically grounded understanding of the relationship between these

variables will help in furthering the role of online SNS as significant sources of social capital and in

formulating guidelines for designing SNS and other communication environments to better support the

creation of social connectedness.

Research Model

Our research model outlines the relationship between the structural and functional characteristics of

a SNS using social capital and social connectedness as the theoretical lens. Based on the preceding

review, we propose that social awareness and social presence, as well as the size of an online social

network and usage of SNS influence social connectedness (see Figure 1).

----- Figure 1 -----

Social Awareness and Social Presence

Awareness is defined as an understanding of the activities of others which defines one’s own

activities (Dourish and Bellotti, 1992). Social awareness relies on knowing the (social) context of an

individual; as a result, individual A perceives social awareness of individual B, if A has access to the

(social) context of individual B (Bardram and Hansen, 2004).

Social awareness on a SNS refers to users being conscious about the activities of other users of the

SNS. This consciousness or awareness can be facilitated by certain functionalities provided by the SNS,


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or also by the way people choose to use the platform or communicate over it. When people are not co-

located, they can create a sense of awareness by mediating information regarding their (social) contexts

through the use of technology, such as email, instant messaging, or SNS. It has been shown, for example,

that online and status information in instant messaging applications raised awareness regarding

availability of individuals in the network (Nardi et al., 2000; Bardram and Hansen, 2004).

In contrast to awareness, social presence is defined as “degree of salience of the other person in a

mediated communication and the consequent salience of their interpersonal interactions” (Short et al.,

1976, p. 65). Social presence is usually considered as a prerequisite for effective communication. Social

presence within a computer-mediated environment is defined as user’s subjective sensation of “being

there,” which is also referred to as “telepresence” or “being together” (Biocca and Levy, 1995; Biocca,

1997; de Greef and Ijsselsteijn, 2001). Others have linked the notion of social presence to concepts of

“immediacy” (Weiner and Mehrabian, 1968) and “intimacy” (Argyle and Dean, 1965), where actions

within a medium that facilitate immediacy are used to create and maintain intimacy, and these actions

also enhance the feeling of social presence within a medium (Gunawardena, 1995).

Literature reflects the interconnection between social presence and awareness by introducing

definitions and terms such as “presence awareness” (Tollmar et al., 1996), which can be regarded as the

awareness of the presence of social ties, and “affective awareness” which can be defined as a general

sense of being connected (Liechti and Ichikawa, 1999). While social presence implies awareness of the

other individual, it has been proposed that “awareness can occur without either social presence or

connectedness” (Rettie, 2003, p. 4). Social presence has also been described as the state in which two

individuals are aware of each other in a virtual environment, and consequently, their “mutual awareness is

the essence of social presence” (Biocca et al., 2003, p. 463).

In computer-mediated environments, it is the medium that allows a sense of social presence (Short

at al., 1976). In particular, media might differ in the extent to which they allow users to experience others

as being present (Cyr et al., 2009). This might play a particularly important role in Twitter as message

length is severely restricted (140 characters). Therefore, social presence can be facilitated or limited by


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certain functionalities of Twitter. Further, people’s perception regarding awareness will also increase the

perception of social presence among Twitter users. Therefore, we hypothesize:

H1: Perception of social presence is positively associated with social awareness.

Social Awareness and Social Connectedness

In the context of computer-mediated communication, awareness can be thought of both as a

perception of other users in the system, and as an aspect of the system that facilitates that perception

(Rettie, 2003). Accordingly, social awareness in SNS refers to people being conscious about other users

in the network. It has been proposed that raised awareness regarding availability of other individuals in

the network, and features that help in monitoring this information, creates and maintains a sense of social

connectedness (Nardi et al., 2000). Therefore, a SNS as a medium will facilitate social connectedness by

allowing for enhanced awareness among its users. Accordingly, we hypothesize:

H2: Social awareness will lead to social connectedness.

Social Presence and Social Connectedness

Social presence has been referred to as a psychological connection that users feel among each other

after an interaction, and can be described as a temporary judgment of an interaction as augmented or

limited by the medium (Biocca et al., 2003). Therefore, increased perceptions regarding social presence

among SNS users will enhance the connection that users will feel with each other. This will result in

increased feelings of social connectedness. Moreover, social connectedness is also described as the

emotional experience, which is generated by the perceived social presence of other individuals in a

medium (Rettie, 2003). Therefore, we hypothesize:

H3: Social presence will lead to social connectedness.

Effects of Network Size and Frequency of Use

Social connectedness has been characterized by the size of the social network (2008; Cornwell et

al., 2008; Goswami et al., 2010). Accordingly, network size will play a role in the extent to which users

feel socially connected. A user who has many connections will be more ensconced within the network,


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and therefore, feel more socially connected. Further, having more members in one’s social network means

that at any point in time the user will have a heightened awareness regarding other members of the

network, and this in turn will result in increased feelings of being connected. Therefore, network size will

also moderate the relationship between social awareness and social connectedness. Accordingly, we

hypothesize:

H4a: Network size is positively associated with social connectedness.

H4b: Network size positively moderates the relationship between social awareness and social

connectedness.

Frequency and amount of communication play an important role in maintaining social relationships

(Rettie, 2003). Previous research has suggested that social connectedness is enhanced by active

information sharing among members (Köbler et al., 2010). Therefore, more frequent usage would result in

users experiencing higher levels of social connectedness. Further, the more people interact with each

other, the more it is likely that they will experience psychological and emotional connection with other

members of the network. Since social presence also refers to the psychological connection that users feel

after an interaction, a higher frequency of use will result in a stronger relationship between social

presence and social connectedness. Accordingly we hypothesize:

H5a: Frequency of use is positively associated with social connectedness.

H5b: Frequency of use positively moderates the relationship between social presence and social

connectedness.

Method and Data

The research model was tested using survey data as well as actual usage data collected from

Twitter.

Background on Twitter


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Twitter, founded in 2006, describes itself as a real-time information network (Twitter, 2012).

Twitter had around 175 million registered users at the end of 2010 with 500.000 new accounts created

every day in 2011 (Twitter, 2011). Twitter allows its users to manage relationships among each other and

thus establish an online social network (Thompson, 2007; Twitter, 2012).

At the heart of the Twitter network are short messages termed tweets of a maximum of 140

characters in length. In prior research on Twitter, the messages exchanged on the SNS have been

classified as mostly consisting of status updates of what users are doing, where they are, how they are

feeling, or hyperlinks to other sites (Kietzmann et al., 2011). Therefore, Twitter is more suited for

connectedness-oriented communication rather than content-oriented communication. Guided by the

theories on social capital, social presence, and social awareness, we look for evidence of mechanisms

within Twitter that can strengthen social connectedness.

Tweets can contain any type of textual information, including hyperlinks (e.g., Uniform Resource

Locator information) to other resources. By default, all messages posted are publicly available in an

unrestricted fashion in the global stream of Twitter messages. To filter this global stream, users can

choose to follow other Twitter users, i.e., users subscribe to the message stream of other users to get these

messages displayed on their Twitter clients (e.g., browser-based and mobile clients). These users are

termed followers. Followers and following are means of forming a network resulting in a directed graph

of social ties within Twitter. In this social graph, Twitter users represent nodes and following-follower

relationships represent directed edges between the nodes with the follower relationship resulting in an

incoming connection (i.e., in-degree) and the following relationship in an outgoing connection (i.e., out-

degree).

In addition to a standard message type, two special types of messaging are possible: re-tweets and

replies. Re-tweets are citations of existing tweets that are sent without editing to a user’s followers. This

type of message is a means of spreading information from others through a user’s own network of

followers. Replies are messages directed at a specific user while still being publicly available. In addition

to the browser-based client, Twitter also maintains an application programming interface (API) which


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allows third-party developers to offer software clients to access Twitter and to integrate the service into

other application offerings, e.g., mobile clients.

Online social networks, and particularly Twitter, exemplify the mode of communication that

primarily caters to connectedness-oriented communication, since a very small message size typically pre-

empts the conveyance of content. In order to support this connectedness-oriented communication a high

degree of social awareness and social presence is necessary. Consequently, SNS have also been classified

as awareness or presence systems (Rettie, 2003). In this research, we examine the relationship between

social awareness, social presence, and social connectedness in the context of Twitter.

Data Collection

An online survey was developed to measure social awareness, social presence, and social

connectedness. The link to the survey was distributed through student mailing lists of under graduate and

graduate students enrolled in a large German university, and by posting the survey link in various SNS

(i.e., Facebook), and sending out tweets on various lists of Twitter users. Similar sampling approach is

often used for online surveys (e.g., Ledbetter et al., 2011). All respondents to our survey were Twitter

users. The survey was carried out over a two months period in mid 2011. The survey research

methodology is particularly suitable for testing relationships between multiple constructs simultaneously

and enhances the generalizability of results (Dooley, 2001). In the survey, respondents were asked to

provide their Twitter user names. This information was then used to gather network size and usage

information directly from respondents’ Twitter accounts by employing the Twitter API. The survey

consent form clearly explained this data collection procedure to respondents. After combining the survey

data with the information collected directly from respondents’ Twitter profiles, we deleted the Twitter

user names from our records in order to anonymize the dataset. The details of the data collected directly

from Twitter are provided below in the discussion on operationalization of variables.

Operationalization of Variables


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Most of our research variables were operationalized using multi-item survey scales, while others

(such as tenure, usage, and network size) were usage data obtained from Twitter, retrieved by Twitter API

calls. In order to enhance validity of the survey instrument, we used tested and validated scales from

previous studies to measure our research variables (Stone, 1978).

The initial face and content validity of the measurement items was assessed by researchers and

faculty members of the computer science department of a large German university. Their feedback was

used to refine the items. Following this, two rounds of questionnaire sorting exercise (labeled and

unlabeled) were carried out based on Moore and Benbasat (1991). Four graduate students participated in

each sorting exercise. For the unlabeled sorting exercise, the labels or descriptions that the sorters came

up with closely corresponded with the actual construct names and on average more than 70% of the items

were correctly sorted into their intended constructs. The results of the unlabeled sorting exercise were

then used to drop some of the measurement items (which were wrongly sorted or found to be ambiguous)

and to refine the scale. The labeled sorting exercise – in which the sorters were provided with the name

and definition of each construct – resulted in an even higher percentage of the items getting correctly

sorted into their intended constructs, thus indicating a high level of face and content validity. All

constructs were measured on a seven-point Likert scale ranging from “Strongly Disagree” to “Strongly

Agree.” The following paragraphs provide a brief definition of the constructs and Table 1 reports the

items that were used to measure the various constructs, with the various sources from which they were

derived.

Social Connectedness. Connectedness refers to being connected by having social, professional, or

commercial relationships. People having high levels of social connectedness tend to feel close to others,

identify with others and participate in social groups and activities (Lee et al., 2001). Social connectedness

in the current context is defined as the connectedness that is achieved by using Twitter as a

communication medium. We drew from previous research measuring online social connectedness

(Ledbetter, 2009; Ledbetter et al., 2011) in order to measure social connectedness using nine items.


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Social Presence. Social presence is usually defined as users’ subjective sensation of “being there”

in a scene, i.e., the illusion of having a non-mediated communication when in reality the communication

is mediated. Based on previous research on social presence (e.g., Biocca et al., 2003; Shen and Khalifa,

2009) we conceptualize and operationalize social presence as a user experience, i.e., a phenomenal nature

of the medium as perceived by the user. The items used to measure social presence were derived from

Shen and Khalifa (2009).

Social Awareness. We define social awareness as the extent to which users are aware of the

presence of others, and react to their presence. Therefore, social awareness can be thought of as

perception of other users and is facilitated by certain aspects of the systems (Rettie, 2003). In computer-

mediated communication such as in online communities, microblogging platforms, and SNS, social

awareness is dependent on factors such as the use of status messages and symbols.

Table 1 shows the items that were used to measure social connectedness, social presence, and

social awareness.

----- Table 1 -----

Network Size (measured directly from Twitter). Following established practices in graph theory,

we measured network size through a node’s in-degree and out-degree. In the context of Twitter, our

measure was operationalized as a sum of the number of followers and following relationships. The

number of followers and the number of following was obtained directly from Twitter through calls to the

Twitter API. Since this variable ranged from 0 to 152,813, and significantly deviated from a normal

distribution, we performed a natural log transformation to obtain a relatively normal distribution of

network size.

• Following: The number of other Twitter users that the focal user is following (i.e., has subscribed to

their tweets). The variable Following represents the number of other Twitter users in whom the focal

user is interested, and represents the node’s out-degree. The variable was measured as a count

obtained directly from respondents’ Twitter profiles and ranged from 0 to 73,959 in our sample.


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• Followers: The number of users who follow a focal user. This is a direct measure of the focal user’s

online social network size regarding incoming connections and represents the node’s in-degree. The

number of followers cannot be controlled by the focal user but are unsolicited expressions of interest

of those who follow. In our sample, the variable followers ranged from 0 to 78,854.

Frequency of Use (measured directly from Twitter). Different measures have been employed in

the field of IS research to measure the usage of an IS application. In the context of the SNS Twitter, we

operationalize our usage measure as the frequency with which the application Twitter has been used.

Frequency of usage is calculated by dividing the total number of tweets a user has posted by the user’s

tenure with Twitter. Both measures have been obtained directly from Twitter through calls to the Twitter

API. Like network size, frequency is transformed using a natural log transformation.

• Tenure (Twitter API): The Twitter API allows reading the sign-up date when a Twitter user account

has been created. We have operationalized this as “(June 26, 2011 – sign-up Date) in days.” In our

sample, the earliest sign-up date was Nov 03 15:01:14 EST 2006; and the most recent sign-up date

was Apr 12 01:23:13 EDT 2011.

• Tweets (Twitter API): The number of all tweets ever posted by the respondent as a natural number

(min 0; max 62,173).

As the number of consecutive calls to the Twitter API is limited to 500 per hour, we had to perform the

data extraction over multiple days.

Data Analysis and Results

We received 141 completed survey responses out of which 121 provided a valid Twitter name

linking to a public profile. These 121 were included in the final analysis. Table 2 presents the

demographic variables of our sample. As mentioned previously, all our respondents were Twitter users,

and the sample was equally distributed between male and females Twitter users. The demographic

distribution of our sample corresponds closely to the overall demographic distribution found on Twitter


18

(Twitter Demographics, 2012). While we cannot rule out potential selection bias, the demographic

characteristics of our sample with regards to gender, age, and education are representative of the overall

population of Twitter users.

----- Table 2 -----

Partial least squares (PLS) analysis was used to perform the data analysis using the software

SmartPLS Version 2.0. (Ringle et al., 2005). PLS has enjoyed increasing popularity in recent years

because its ability to model latent constructs under conditions of non-normality and in small to medium-

sized samples (Chin, 1998). It allows researchers to specify the relationships among the conceptual

factors of interest and the measures underlying each construct, resulting in a simultaneous analysis of the

measurement model (i.e., how well the measures relate to each construct) and the structural model (i.e.,

whether the hypothesized relationships at the theoretical level are empirically true). Using the two-stage

approach, we first examined the measurement model, and then the structural model.

Measurement Model

Internal consistency, convergent validity, and discriminant validity were assessed to validate the

instruments (Gefen and Straub, 2005). Internal consistency was examined using composite reliability,

which in PLS relies on the actual loading to calculate the factor scores and is a better indicator of internal

consistency than Cronbach’s alpha (Ranganathan et al., 2004). As shown in Table 3, the composite

reliability for the constructs in the model were all above the suggested threshold of 0.7 (Chin, 1998;

Straub, 1989), thus supporting the reliability of the measures.

Convergent validity indicates the extent to which the items of a scale that are theoretically related

are also related in reality. Convergent validity measures the correlation among item measures of a given

construct using different methods of measurement. Table 3 presents information about the factor loadings

of the measures of our research model. All items have significant path loadings at the 0.001 level. The

average variance extracted (AVE) values are all above the recommended value of 0.5 (Fornell and

Larcker, 1981). Therefore, convergent validity of the scales is acceptable.

----- Table 3 -----


19

Table 4 shows the correlation between constructs, with the diagonal elements being the square

roots of the average variance extracted (AVEs). This indicates that the variance shared by the items of a

particular construct is higher than the variance that is shared by the construct with another construct,

which is used as an indicator of discriminant validity in PLS.

----- Table 4 -----

Another method of assessing discriminant validity is by checking the factor loadings and cross

loadings of the measurement items (see Table 5). Scanning down the columns indicates that the item

loadings in their corresponding columns are all higher than the loadings of items used to measure the

other constructs. Scanning across rows indicate that item loadings are higher for their corresponding

constructs than for other constructs. Thus, the measurement items of this model satisfy the two criteria for

discriminant validity suggested by Chin (1998). Overall, the data provides empirical support for

reliability, as well as convergent and discriminant validity of the scales of our measurement model.

----- Table 5 -----

Structural Model

Table 6 presents the result of our model testing. We first tested a model incorporating only the

main effects and then introduced the moderating effects in a second model. Overall, our models explain

59% and 63% of the variance in social connectedness in Twitter. In addition to testing the main effects of

social awareness and social presence on social connectedness, we also test for a mediating effect of social

presence on the relationship between social awareness and social connectedness. Our results indicate that

both social awareness and social presence have significant direct effects on social connectedness and that

the effect of social awareness is partially mediated by social presence.

----- Table 6 -----

While network size does not have a significant direct effect on social connectedness, it positively

moderates the relationship between social awareness and social connectedness. Frequency of use has a


20

significant direct effect. However, the moderating effect on the relationship between social presence and

social connectedness was not significant.

Understanding Usage Behavior

The above analysis relies on partial least squares analysis to test a theoretic model linking Twitter

usage to perceived benefits such as social connectedness. To better understand our sample population’s

usage behavior of Twitter, we performed several additional analyses both on actual usage data collected

from Twitter as well as responses elicited through the online survey. First, we investigate what other

communication media are used and in which frequency. Figure 2 shows communication media usage.

Face-to-face, social media, and cell phone text messages are the dominant communication media with

around 50% of the respondent reporting “very frequently.” Given the sample population of Twitter users,

the usage frequency of other electronic communication media such as email, cellular phone text messages

is, not surprisingly, very high (around 45% responding “very frequently”).

----- Figure 2 -----

To better understand whether the respondents to our survey were exclusive users of Twitter, we

collected data on their usage of other social networking sites (Figure 3). Almost all respondents were also

users of Facebook (93%). The only other widely-used SNS was LinkedIn (54%). Other SNS had only

limited reported usage of below 25%.

----- Figure 3 -----

In order to get a better understanding of users’ feeling of connectedness, we further analyzed self-

reported details regarding the nature of connections users have on Twitter. We investigate different

connection types in the panels of Figure 4. Panel 1 shows the composition of connections. Most

connections are with celebrities and prolific people (22%), friends (21%), and acquaintances and casual

contacts (21%). Very few connections are with family members or romantic relationships (2% and 0.3%

respectively). Work-based relationships account for 13%. Panel 2 shows the classification of connections

regarding their physical distance. Twitter connections are mostly between local friends (34% reported

“almost all” or “all”). The classification of long-distance friends is largely consistent (42% reporting


21

“none” or “very few” long-distance connections) which increases the reliability of this finding. This

indicates that most Twitter users rely on Twitter as a medium for remaining connected to friends, casual

acquaintances, and other prominent Twitter users rather than using it for keeping in touch with family.

Therefore, Twitter can be considered as a tool for establishing and maintaining weak network ties, rather

than for strong network ties.

----- Figure 4 -----

Finally, we investigate individuals’ usage behavior by analyzing each respondent’s last 200 tweets

(Figure 5 shows a random sample of 50 respondents from our overall sample of 121). To illustrate

frequency of use, we plot the tweets as a time series: the shorter the time series, the higher an individual’s

usage frequency (the elapsed time between the last 200 tweets is shorter). For a high-frequency user such

as “User 1,” the last 200 tweets span only a period of 3.6 days resulting in an average of 55 tweets per

day. For a low-frequency user, such as “User 50,” the last 200 tweets span almost one and a half years

resulting in an average of 0.3 tweets per day. The majority of tweets are connectedness-oriented mentions

of other users accounting for 45% of all tweets. Another category of connectedness-oriented tweets is re-

tweets with additional mention of another Twitter user (mostly the source of the re-tweet), which account

for another 20% of tweets. Re-tweets without mentioning of another user are very rare accounting for

below 1% of tweets. Finally, plain tweets account for 35% of the tweets. This supports our argument that

Twitter is a connectedness-oriented social network and provides additional credibility to our findings of

the ability to build social relationships through the network.

----- Figure 5 -----

Discussion and Implications

Discussion

In this paper, we developed a model theorizing relationships between social presence, social

awareness, and social connectedness. Additionally, we investigated direct and moderating influences of


22

users’ network size and frequency of use. We tested the model using survey data collected from users of

the online social network Twitter. The effects of network size and frequency of use were measured

directly from Twitter. While prior research has examined and highlighted the importance of social

presence, social awareness, and social connectedness, there is little empirical evidence on how they

influence each other (Rettie, 2003). Testing our theoretical model, we address the research questions (a)

how do social presence, social awareness, and social connectedness influence each other, and (b) when

and for whom the effects of social presence and social awareness are most strongly related to positive

outcomes in social connectedness.

Our data show a positive and significant effect of social awareness on social presence, thus

supporting H1. Furthermore, our empirical results find both social awareness and social presence have a

significant positive effect on social connectedness, thus supporting H2 and H3. Furthermore, we find that

social presence partially mediates the relationship between social awareness and social connectedness.

Thus, social awareness functions as a fundamental building block for the higher-level constructs of social

presence and social connectedness, while social presence functions as a mechanism through which the

positive effect of social awareness on social connectedness is realized. This finding thus explains the

process through which social connectedness is created and has important implications on the designs of

SNS. We argue that social connectedness is the higher-level construct, which is influenced by both the

functional and structural characteristics of the social network, and the communication among individuals

within the network.

In the context of computer-mediated communication, social presence theory advocates that the

medium should be as proximate to face-to-face communication as possible. However, previous research

in social presence in computer-mediated communication has often been criticized for taking a too media-

centric view on the occurrence of social presence (Shen and Khalifa, 2009), with social presence often

being considered an inherent property of the medium itself (the richer the medium, the more social

presence it is believed to have). Our research overcomes this criticism by considering the mechanism

through which social presence can be enhanced in a not so rich medium (Twitter). By facilitating the


23

exchange of very short connectedness-oriented messages and status updates, Twitter enhances feelings of

awareness regarding other users in the medium, and this in turn enhances social presence. Further, both

social presence and social awareness significantly influence social connectedness, an excellent indicator

of the potential to appropriate social capital from the network.

To provide a more complete picture addressing the question when and for which user groups

feelings of social connectedness are most strongly affected by social presence and social awareness, we

also study direct and moderating effects of network size and usage frequency through data collected

directly from user profiles. Regarding network size we find no significant direct effect but a significant

moderating effect on the relationship between social awareness and social connectedness. Therefore, H4a

is not supported, while H4b is supported. This indicates, that having a large network is not necessarily a

pre-condition to feeling socially connected, however, the larger the network size, the stronger the

relationship between social awareness and social connectedness. The relationship between social

awareness and social connectedness can be summarized as a feeling of being connected in the network

because of a better understanding regarding the activities of others. A larger network results in increased

understanding regarding the activities of others and therefore increased feeling of connectedness.

Frequency of use has a significant direct effect but the moderating effect on the relationship

between social presence and social connectedness is not significant, thus hypothesis H5a is supported

while H5b is not supported. We speculate that this could be due to the fact that not every type of usage

and not every type of social network results in having higher levels of psychological connection as a

result of interactions within the network (Granovetter, 1973; Burke et al., 2010). In Twitter, for instance,

not just the amount or frequency of usage, but rather the fundamental motivation behind usage of the

medium, may have a better explanatory power of the relationship between social presence and social

connectedness.

Further analysis of Twitter usage behavior provides a better indication of the structural

characteristics of the Twitter network, and shows that users rely on connectedness-enhancing Tweets in

order to establish and maintain weak ties with other Twitter users. Previous research has suggested that


24

social capital is associated with both strong and weak ties that an individual has within a system

(Granovetter, 1973; Nahapiet and Ghoshal, 1998). This indicates that Twitter can indeed serve as means

of enhancing social capital for its users.

Overall, our use of two distinct but complementary data sources provides a richer picture of the

studied phenomenon and follows suggested practice in IS research (Sharma et al., 2009). This study is

one of the few examples of research that combines measures of psychological perceptions with actual

measures of usage and network size from the SNS to investigate the link between subjective and objective

variables. Our results show that usage of an online social network, such as Twitter, can indeed result in

tangible outcomes by increasing users’ social capital. Hence, we provide empirical evidence to the

discussion on whether the use of online social networks translates into real-world benefits.

Limitations

In this study of social capital formation in SNS, we relied on two very basic measures of usage

behavior and network size. We did not, in particular, investigate additional structural properties of the

online social network such as the networks density or the centrality of the focal user within the

relationship graph. As relationships on Twitter are directed rather than undirected (as it would be the case,

for example, in Facebook) this might provide additional insights which could be explored in future

research. Furthermore, our usage measure did not take the nature or the content of the communicated

information into consideration. Consequently, as the motivation to join Twitter and the information

shared on Twitter is likely not identical to that of other SNS such as Facebook or Google+, future

research could be extended in this direction to investigate more closely in how far our results can are

generalizable to other SNS. Also, we acknowledge that users may be simultaneously active on multiple

SNS, and their overall social connectedness is a cumulative effect. However, in this research, we were

primarily interested in focusing on social connectedness and its determinants that can be attributed to one

particular SNS, and therefore we only asked respondents about their Twitter usage behavior. We believe

that this parallels real life social network membership and its consequences as people simultaneously

belong to multiple networks (e.g., school friends, work colleagues, local community) and they are likely


25

to feel different levels of connectedness in each of these networks. Researchers have already started

analyzing the content of communication in online social networks, and these could provide a richer

understanding of different types of usage behaviors. However, only few of them have started to

investigate the link between content and perceptual responses.

Most studies of Twitter are population-level studies (e.g., Golder and Macy, 2011; Perreault and

Ruths 2011). Our paper provides a valuable contribution as it attempts to establish user-level inferences

between functional and structural features of the social networking site on users’ feeling of

connectedness. This is a difficult task due to sample selection and can potentially introduce biases, which

can limit the degree to which inferences can be drawn. However, our analysis of demographic

information from Twitter shows that the distribution of the various demographic characteristics in Twitter

(Twitter Demographics 2012) is very similar to their distribution in our research sample. We therefore

believe that this user-level study can lead to more meaningful insights and consequently design

implications.

We study an actual, global online social network with several million users and their established

social networks, rather than an experimental system in a laboratory setting. While we acknowledge that

this type of research using observational data, and self-reported responses can have certain limitations, the

fact that we study an actual system, with actual users, and their natural usage behavior should reflect

positively on the concerns of external validity.

Given the observational nature of our study, we acknowledge potential limitations to causal

inference. However, we objectively measure actual Twitter usage and social network size, and are thus

improving on earlier research on user behavior in online social networks that often relies only on survey-

based measures. For example, in their study of communication behavior Ledbetter et al. (2011) rely on

users’ self-reported usage metrics instead of measurements taken directly from the system. Consequently,

we expect very little measurement error in determining users’ network size and usage frequency.

Furthermore, using multiple data sources and measurement methods, we address potential concerns of

common method variance.


26

Research Implications

Our work contributes to the stream of literature on the formation of social capital in online social

networks (Ledbetter et al., 2011; Koroleva et al., 2011) by specifically investigating moderating effects of

usage frequency and online social network size on the relationship between social awareness, social

presence, and social connectedness.

Extending early research on social capital formation process, we go beyond the study of direct

effects but specifically study both mediating and moderating effects in order to deepen our understanding

how key constructs social presence, social awareness, and social connectedness influence each other and

the conditions that affect the direction and/or strength of the underlying relationship. As current research

on the formation of social capital via SNS usage lacks validated measurement instruments that are

specifically developed to capture social capital outcomes (Koroleva et al., 2011), we built our research

model on existing and established psychological concepts such as social presence, social awareness and

social connectedness as potential predictors of social capital among SNS users. Our literature review

indicated that prior empirical research missed to generate a clear understanding of potential relationships

between these constructs. Therefore, our research contributes to a better understanding of the

relationships between the three constructs by disentangling the empirical effect of social presence and

social awareness on social connectedness. We thus contribute to and extend social psychology literature,

which has investigated these constructs (Rettie, 2003; Biocca et al., 2003; Shen and Khalifa, 2009;

Koroleva et al., 2011).

Design Implications

From a practical perspective, our finding could have major implications in the field of HCI where

researchers consistently attempt to develop systems to improve various aspects of social benefits of

applications. Building on the concept of evidence-driven social design, we aim at translating the findings

resulting from the test of our theoretical model into concrete design implications (Kraut and Resnick,

2011a; Riedl, 2011). The goal of this being to help designers incorporate these social science research


27

findings into informed design choices for online communities and online social networks. Table 7

provides a summary of design implications that can be drawn from this research.

----- Table 7 -----

Conclusion

In this study we presented results that extend our understanding of the theoretical and empirical

relationship between social presence, social awareness, and social connectedness in the use of online

social network sites. Drawing from literature on social psychology, we developed a model that relates the

key constructs influencing social connectedness in the offline world to the realm of computer-mediated

communication in online social networks. Furthermore, we contribute to our understanding of which user

groups are most strongly affected by the interaction of social presence, social awareness, and social

connectedness, and the underlying process through which social connectedness gets created. The

empirical results using data collected from Twitter users largely support the proposed research model. We

also investigated the direct and moderating effects of network size and usage frequency, finding a direct

effect of usage frequency on social connectedness and a moderating effect of network size on the

relationship between social awareness and social connectedness. We argued that by increasing users’

feeling of social connectedness online social networks such as Twitter can make valuable contribution to

users’ well being. Thus, we provide insights about how specific aspects of computer-mediated

communication in the form of online social networks affect and moderate users’ perception of social

connectedness and provide design implications that can inform the design of online social networking

sites.

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Tables

Table 1. Operationalization of Constructs. Variable Items Source Social Connectedness

I feel distant from people in my Twitter network I do not feel related to most people in my Twitter network I feel like an outsider in my Twitter network I feel disconnected from the Twitter world around me I do not feel I participate with anyone or any group in Twitter I feel close to people on Twitter I am able to relate to my peers on Twitter I am able to connect with other people on Twitter I find myself actively involved in my Twitter friends’ lives

Adapted from Lee et al., 2001.

Social Presence

People on Twitter understand each other I understand the other Twitter users’ opinions The other Twitter users understand what I mean My thoughts are clear to the others on Twitter The other individuals’ thoughts are clear to me

Adapted from Shen and Khalifa, 2009.

Social Awareness

I hardly notice other users on Twitter I feel that other Twitter users are aware of my presence The other individuals do not notice me on Twitter I am often aware of other users in Twitter Others of often aware of me on Twitter

Shen and Khalifa, 2009; Biocca et al., 2001.

Note. Items in italics are reverse coded.

Table 2. Demographic Details (N = 121). Variable Categories Frequencies Gender Male

Female Not specified

60 58

3 Age Under 17

18 - 24 25 - 34 35 - 44 45 - 54 55 or older Not specified

2 46 41 21

8 1 2

Education High School Bachelor Master / Diploma PhD Not specified

20 44 42

7 8


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Table 3. Psychometric Properties of Measurement Model. Construct Item Factor

Loadings Composite Reliability

AVE

Social Connectedness

SC1 SC2 SC3 SC4 SC5 SC6 SC7 SC8 SC9

0.730 0.796 0.713 0.861 0.867 0.871 0.755 0.801 0.715

0.938 0.628

Social Presence SP1 SP2 SP3 SP4 SP5

0.804 0.811 0.828 0.780 0.764

0.897 0.637

Social Awareness SA1 SA2 SA3 SA4 SA5

0.725 0.845 0.770 0.629 0.867

0.879 0.596

Table 4. Correlation Between Constructs. Frequen

cy of Use

Network Size

Social Awareness

Social Presence


Frequency of Use Network Size 0.679 Social Awareness 0.488 0.582 0.772 Social Presence 0.323 0.301 0.483 0.798 Social Connectedness

0.539 0.460 0.686 0.555 0.792

Note. Diagonal elements are the square roots of AVE (for latent constructs).


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Table 5. Factor Loadings and Cross Loadings. Social

Presence Social Awareness


SP1 0.804 0.367 0.493 SP2 0.811 0.409 0.451 SP3 0.828 0.432 0.456 SP4 0.780 0.353 0.352 SP5 0.764 0.359 0.456 SA1 0.404 0.725 0.546 SA2 0.441 0.845 0.601 SA3 0.262 0.770 0.483 SA4 0.336 0.629 0.401 SA5 0.394 0.867 0.582 SC1 0.275 0.501 0.730 SC2 0.396 0.593 0.796 SC3 0.375 0.514 0.713 SC4 0.494 0.599 0.861 SC5 0.427 0.600 0.867 SC6 0.544 0.548 0.871 SC7 0.490 0.507 0.755 SC8 0.572 0.537 0.801 SC9 0.347 0.480 0.715


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Table 6. Hypotheses Testing (Dependent Variable = Social Connectedness). Variable Path Coefficients Model 1 Model 2 Implications Independent Variables Social Awareness Social Presence 0.483*** 0.483*** H1 Supported Social Awareness 0.462*** 0.476*** H2 Supported Social Presence 0.267*** 0.335*** H3 Supported Network Size -0.100 -0.115 H4a Not Supported Frequency of Use 0.284*** 0.250** H5a Supported Moderating effect of Network Size Network Size * Social Awareness 0.199** H4b Supported Moderating effect of Frequency of Use Frequency of Use * Social Presence 0.075 H5b Not Supported Control Variables Age 0.037 0.059 Gender -0.049 -0.029 R2 0.585 0.630 Note. Significance levels * p < 0.05; ** p < 0.01; *** p < 0.001.


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Table 7. Summary of design implications that can be drawn from this study. Implication Description Social benefits of SNS

Our results indicate that online social networking sites have indeed social benefits by enabling users to build social capital. Users can successfully build social capital and feelings of social connectedness through online systems thus indicating that socially beneficial effects of personal interaction can be transferred from an offline to an online context. Designers of HCI should therefore consider adding social networking features to their website to support the creation of social capital. This design implication is particularly important for designers of online systems that are targeted at users’ social benefits such as those providing companionship and social support.

Social benefits despite limited media richness

Our results indicate that the social benefits of SNS can be achieved even with very limited media such as Twitter, which allows only the exchange of very short broadcast messages. Therefore, designers should exploit even very simple means of communication, given that even tweets are able to successfully convey social awareness, social presence, and social connectedness. System features should especially focus on the exchange of connectedness-oriented messages and status updates. Systems should specifically allow to direct messages at others and provide personal and location information to put messages into a social and geographic context.

Social awareness as fundamental system feature

Social awareness functions as a fundamental building block for higher-level constructs of social presence and social connectedness. Designs should be directed towards incorporating and enhancing awareness creating features within the SNS, as awareness not only has a direct effect on social connectedness but also enhances social connectedness indirectly through increased feelings of social presence. Possible features to consider are status indicators, status messages, and other forms of awareness displays to provide contextual information about the activities of group members (Dabbish and Kraut, 2008).

Social presence as building block for social connectedness

Evidence from our study suggests that social presence functions as an important building block for social connectedness. Consequently, designers should aim at integrating features to enhance social presence in order to enhance users’ feeling of social connectedness. Possible system features to convey the presence of other people are images of members (Resnick et al., 2011). To support social presence, Twitter includes profile pictures next to each tweet and a display of a relative time indicator such as “posted 8 minutes ago” to evoke immediacy.

Direct effect of usage frequency

Our results show that frequency of use has a strong direct effect on users’ feeling of social connectedness. To enhance social connectedness, designers of HCI should integrate system features that motivate users to use the system more. A wide variety of system features can be employed such as low threshold interfaces for easily making small contributions, identifying user contributions by name, leader boards, and conveying to users that they are unique and others in the group cannot make the contributions they are making (Preece and Shneiderman, 2009; Resnick and Kraut, 2011b). Twitter encourages usage frequency for example, through their “Trends” feature that


42

shows major trends in current Twitter content and thus invites the user to explore contributions made by other Twitter users who are not in the ego’s immediate network.

Moderating effect of network size

While network size has no direct effect on social connectedness, it is an important moderating factor for the effect of social awareness on social connectedness. To increase the positive effect of users’ network size, designers should implement system features that help users to grow their online social network. Possible system features are to automatically suggest other members as possible friends or import functions that allow importing an address book to grow the online social network (Preece and Schneiderman, 2009). Twitter supports users in building a larger network through the “Who to follow” panel that suggests other Twitter users. In addition to the immediate suggested list of users, the panel also offers a “refresh” button, which will generate a new set of suggested users.


43

Figures

Social''Presence'

Social'Connectedness'

Social'Awareness'

Usage'Frequency'

Online'Social'Network'Size'

H1+

H3+

H2+

H5b

H4b

H5a+

H4a+

Figure 1. Research Model.

Other online

Cellular phone text messages

Social networking websites

Instant messaging

E−mail

Mobile / cellular phone

Telephone (land line)

Face−to−face 4.42 (0.83)2.49 (1.28)4.03 (1.03)3.98 (1.06)3.58 (1.24)4.24 (0.91)4.09 (1.12)2.44 (1.24)

4.42 (0.83)2.49 (1.28)4.03 (1.03)3.98 (1.06)3.58 (1.24)4.24 (0.91)4.09 (1.12)2.44 (1.24)

4.42 (0.83)2.49 (1.28)4.03 (1.03)3.98 (1.06)3.58 (1.24)4.24 (0.91)4.09 (1.12)2.44 (1.24)

4.42 (0.83)2.49 (1.28)4.03 (1.03)3.98 (1.06)3.58 (1.24)4.24 (0.91)4.09 (1.12)2.44 (1.24)

4.42 (0.83)2.49 (1.28)4.03 (1.03)3.98 (1.06)3.58 (1.24)4.24 (0.91)4.09 (1.12)2.44 (1.24)

2%30%4%2%9%3%5%

31%

2%24%3%9%10%2%6%22%

4%18%16%14%21%7%11%26%

36%22%38%37%33%44%32%16%

56%6%38%38%27%44%47%6%

Mean (SD) Never Very rarely Rarely Frequently Very frequently

Percent020406080100

How often do you use the following media to communicate with friends?

Figure 2. Communication media usage.


44

BadooHabbo

StudiVZOrkut

QzoneFriendster

XingMyspace

FoursquareLinkedIn

FacebookTwitter

0 20 40 60 80 100Percentage

Netw

ork

What other social networking sites do you use?

Figure 3. Reported usage of other social networking sites.

Romantic relationshipsFamily

Work Accaintances & casula contacts

FriendsOthers

Celebrities & prolific people

0 5 10 15 20Percentage

Type

Mean composition of Twitter connection by type

Mostly long−distance friendsMostly local friends

42%

29%

19%

34%

0 20 40 60 80 100Percentage

ResponseNoneVery fewSomeAlmost allAll

What is the distance of your connections on Twitter

Figure 4. Composition of Twitter connections by type and physical distance between ego and Twitter connections. Percentages denote distribution excluding neutral rating.


45

Figure 5. Usage frequency of random subset of 50 Twitter users of our survey sample of 121. Each users’ last 200 tweets are shown, classified by tweet type (plain tweet, mention, re-tweet, or mention within a re-tweet). The distribution of tweet types is shown in the bar plot next to it. Numbers to the far right show tweets per day (T/D). For “User 1,” a high-frequency user, the last 200 tweets span only a period of 3.6 days while for “User 50,” a low-frequency user, the last 200 tweets span almost one and a half years.


46

Biographical Notes

Christoph Riedl is a post-doctoral fellow at the Institute for Quantitative Social Science and

Harvard Business School at Harvard University. He has been awarded a post-doctoral fellowship of the

German Research Foundation. He holds a PhD in Information Systems from Technische Universität

München, Germany. His research interests include crowd sourcing and collective intelligence.

Felix Köbler is a PhD candidate and research assistant at the Chair for Information Systems of

Technische Universität München. His teaching and research areas include Online Social Networking,

Human-Computer Interaction as well as Ubiquitous and Mobile Computing. He received a B.Sc. and

M.Sc. in Information Systems from Technische Universität München.

Suparna Goswami is a Research Fellow at the Technische Universität München, Germany. She

holds a PhD in Information Systems from the National University of Singapore. Her research interests lie

in the areas of Web 2.0 technologies and their implications, topics in human-computer interaction and

digitally enabled inter-organizational networks.

Helmut Krcmar is a full professor of Information Systems and holds the Chair for Information

Systems at the Department of Informatics, Technische Universität München, Germany. His research

interests include computer supported cooperative work, and information systems in healthcare and e-

government.

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