Full Terms & Conditions of access and use can be found at
The Social Science Journal
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/ussj20
Demographic inequalities or personality
differences? Exploring six types of social media
usage divides in Mainland China
Yiyan Zhang , Lei Guo , Homero Gil de Zúñiga , Tian Xie & Robert Jiqi Zhang
To cite this article: Yiyan Zhang , Lei Guo , Homero Gil de Zúñiga , Tian Xie & Robert Jiqi Zhang
(2021): Demographic inequalities or personality differences? Exploring six types of social media
usage divides in Mainland China, The Social Science Journal
To link to this article: https://doi.org/10.1080/03623319.2020.1851952
Published online: 14 Jan 2021.
Submit your article to this journal
View related articles
View Crossmark data
Demographic inequalities or personality differences? Exploring six
types of social media usage divides in Mainland China
, Lei Guo
, Homero Gil de Zúñiga
, Tian Xie
, and Robert Jiqi Zhang
Division of Emerging Media Studies, Boston University, Boston, USA;
Political Science, University of Salamanca,
Film Production & Media Studies, Pennsylvania State University, State College, USA;
Comunicación y Letras, Universidad Diego Portales, Santiago, Chile;
Department of Psychology, Wuhan University,
School of Psychology, Nanjing Normal University, Nanjing, China;
School of Psychology, Massey
University, Auckland, New Zealand
With the wider penetration of information and communication technolo-
gies (ICTs), digital divide scholars have turned attention from physical
access to the difference in usage. Based on a national representative survey
conducted in mainland China (N = l,004), this exploratory study contributes
to the literature by explicating a new typology of social media usage
divides predicted by demographic factors and personality traits (the Big
Five) and by adding the context of an authoritarian country. The results
show that even controlling for personality traits, age shows strong negative
effects on most usages. Males and educated populations are also savvier in
some of the usages. Interestingly, “reverse divides” were found in main-
stream informational use, indicating that the older generations and the
lower-income groups use social media for getting information from main-
stream media relatively more frequently. This paper also reveals significant
predicting and interaction effects of individuals’ personality traits on some
Received 25 April 2020
Revised 20 September 2020
Accepted 11 November 2020
Usage divide; social media;
personality traits; China
Social media have become increasingly popular around the world. The number of social media users
worldwide surged from 970 million in 2010 to 2.46 billion in 2017 (Statista, 2018). Arguably, this
trend has important implications for the digital divide, which can be loosely defined as uneven access
to information and communication technologies (ICTs) as related to socioeconomic inequalities
(Van Dijk & Hacker, 2003). On the one hand, scholars have contended that, by allowing individuals
to gather information, receive education, connect with people, and participate in public affairs with
low cost, social media have the potential to narrow the global digital divide, providing an opportu-
nity for developing countries to “catch up” with the global North (Ali, 2011; Chen & Wellman, 2004;
Pimmer et al., 2012). On the other hand, the increasing reliance on social media in people’s daily life
could lead to new forms of digital divide, such as usage divide. Both effects brought by the
emergence of social media are calling for an updated understanding of the digital divide in the
social media era, especially in countries where socioeconomic resources are unevenly distributed.
Specifically, the digital divide within non-western, developing countries with a large population of
“netizens” yet uneven access to ICTs remains largely understudied (Chen & Wellman, 2004; Fong,
2009; Harwit, 2004). Consider the example of China. As of 2015, the country’s popular social
networking services Weibo and WeChat, which have similar affordances with Twitter and
CONTACT Yiyan Zhang email@example.com Division of Emerging Media Studies, Boston University, Boston, MA 02215.
© 2021 Western Social Science Association
THE SOCIAL SCIENCE JOURNAL
WhatsApp respectively, reached 212 million and 697 million monthly active users respectively (Fan,
2015; Tencent, 2016). However, little is known about how Chinese users with different demographic
and personality traits employ these ICTs differently, given the existing high level of social and
economic inequalities in China (Institute of Social Science Survey, Peking University, 2015). In
addition, China’s controlled information environment and the wide spread of low-cost mobile
devices among the disadvantaged groups in recent years (Wang, 2015) may complicate behavioral
patterns of social media use among different groups of people and thus challenge the western
observation of the digital divide, which indicates privileged social groups – males, the younger
generations, and people with higher income and higher education level use more ICTs than their
counterparts. Hence, this study seeks to contribute to the digital divide literature by adding a new
perspective based on the context of social media use in China.
With the wider penetration of ICTs, digital divide scholars have turned their attention from the
access to ICTs to the types of usage (e.g., Kim & Kim, 2001; Van Dijk, 2005; Wei & Hindman, 2011).
Following this line of research, this study focuses on the potential usage divide on social media.
While different uses of ICTs among different social and economic groups have been widely discussed
in the literature, the field lacks a coherent analytical framework for understanding the usage divide in
terms of ICTs in general and social media in particular (Gil de Zúñiga & Diehl, 2017). As such, based
on a synthesis of the existing literature (Kim & Kim, 2001; Van Deursen & van Dijk, 2013, 2015; Van
Dijk & Hacker, 2003), this study proposes six types of usages of social media, including social use for
existing ties, social use for new ties, mainstream informational use, alternative informational use,
active political use, and passive political use.
Given the worldwide saturation of Internet penetration, people might now have more personal
choices over whether and how to use the ICTs and are less bounded by their demographic traits. The
overarching questions here are: Since usage divide goes beyond access to preference, is it driven by
factors other than the demographic ones, such as personality traits? If so, would these new factors
interact with the traditional demographic-led gaps and help to transfer the more class-based digital
divide to a more choice-based version? This paper bridges the traditional digital divide literature that
focuses on demographic factors (e.g., Norris, 2001; Van Dijk, 2005; Van Dijk & Hacker, 2003) and
psychological literature that connect personality traits and social media use (e.g., Correa et al., 2010;
Gil de Zúñiga et al., 2017; Hughes et al., 2012; Ryan & Xenos, 2011) by investigating how age,
gender, education, and income, as well as the Big Five personality traits, predict usage gaps. The
interaction between the Big Five and demographic variables were also examined to see if the usage
divide is less related to the more inherent factors like age and gender.
Based on a national representative survey conducted in China, this exploratory paper reveals how
a technology (social media) is used by different demographic and personality groups in various ways
at its early stage. The interaction effects of the two types of factors are analyzed here. This study also
discusses the implications and potential of expanding from the particular situation in China to other
Levels of the digital divide and social media usages
The knowledge gap hypothesis states that the gaps in knowledge between social segments with
higher and lower socioeconomic status tend to increase in accordance with the growth of informa-
tion in a social system (Tichenor et al., 1970). Extending the idea to the digital media environment in
the 1990s, the digital divide refers to social and economic inequalities as related to the access to ICTs
(Gunkel, 2003; Van Dijk, 2005). Given the various benefits that ICTs bring to their users (Bikson &
Panis, 1997; Hacker & Steiner, 2001), the uneven access to ICTs may then in turn increase the social
and economic disparities between the “haves” and “have-nots” (Van Dijk, 2005).
With the wider penetration of ICTs in many societies, the above dichotomy is not sufficient to
capture the development of ICTs. As Van Dijk and Hacker (2003) contended, a “differentiation
position” (p. 509) should be taken to examine different levels of the digital divide separately within
2Y. ZHANG ET AL.
the “haves.” As such, scholars have turned their attention to the opportunity and reception divide
(Kim & Kim, 2001), motivational divide, material divide, skill divide (Van Dijk, 2005), and usage
divide (Hargittai, 2001; Willis & Tranter, 2006). Among various types of divides, the usage divide is
worth special attention as “the key issue is not unequal access to computers but rather the unequal
ways that computers are used” (Warschauer, 2003, p. 47). For instance, Van Dijk and Hacker (2003)
showed that people in different socioeconomic segments used computer applications for different
purposes: information, personal development, social interaction, leisure, commercial transaction,
and gaming. More recently, Harris et al. (2017) found that children from different socioeconomic
neighborhoods, while having similar degrees of computer access, showed distinct usage patterns.
They argued that different uses of computers for academic and non-academic purposes would have
a significant impact on individuals’ future economic, academic, and health situations.
The existing research on the uses of ICTs has greatly enriched our understanding of the digital
divide. However, different studies have focused on uniquely different aspects of ICT uses, without
offering a coherent analytical framework for theorizing the phenomenon. Different usages within
a particular type of ICT application, such as social media, still need more nuanced explications. Van
Deursen and van Dijk (2015) grouped ICTs usages into personal development, leisure, social
interaction, information, news, commercial transaction, and online games based on a factorial,
which can hardly be applied to social media without revisions (e.g., commercial transaction and
online games are not common in the use of social media). More recent scholars specified different
typologies for social media usage, which are yet neither comprehensive enough nor unified. For
instance, Gil de Zúñiga et al. (2017) and Hughes et al. (2012) roughly divide social media use into
news/information use and social interaction use. Seidman (2013) took a more motivational approach
and proposed a division between social media use for belongingness (i.e., information-seeking and
communication) and for self-presentation (general self-disclosure and emotional disclosure). Chen
et al. (2016) further added social media use for receiving political information and for sharing
political information in addition to the commonly-seen news use. Based on a synthesis of the
relevant literature, this study proposes six types of social media usages, which are based on three
aspects: social, informational, and political – arguably the key functions of social media.
ICTs allow for increased social interactions (Hacker & Steiner, 2001; Van Deursen & van Dijk,
2013, 2015). In particular, social media by definition help facilitate the development of social
networks among their users (boyd & Ellison, 2007). While digital divide research focused on social
use is rare, studies about ICTs in general suggest that because of the use of ICTs, well-networked
individuals may accumulate more social capital, or “resources that can be accessed or mobilized
through ties in the network” (Lin, 2008, p. 51), than those less connected (e.g., Van Dijk & Hacker,
2003). Specifically, scholars have proposed two dimensions of social use: maintaining existing strong
ties and establishing new weak ties (Bikson & Panis, 1997; Van Deursen & van Dijk, 2013). However,
it remains unknown how people in different socioeconomic groups use social media for these two
networking purposes differently, which is examined here.
Gathering informational is another vital use of ICTs. Based on a nationally representative survey
in the United States, Goldfarb and Prince (2008) found that women and less-educated people spent
less time reading news online than others. Wei and Hindman (2011) indicated that education was
even more strongly related to the informational use of the Internet than to general Internet access
and some traditional media. Van Deursen and van Dijk (2013) also found that elder citizens, women,
and people with less household income read significantly less news online compared with their more
privileged counterparts. More recently, a great number of online users have turned to social media
for news (Gil de Zúñiga et al., 2012). Whether the gender-, age-, education- or income-generated
digital divide for informational purposes also exist on social media is worth further analysis. Besides,
social media provide a platform for individuals to access to information alternative to mainstream
discourse (Newman, 2011; Poell & Borra, 2012). How people in different social and economic groups
use social media for alternative content may shed light on another dimension of the digital divide in
terms of informational usage.
THE SOCIAL SCIENCE JOURNAL 3
The last two types of social media use to be examined are about political use. It is widely believed
that ICT use affords better political engagement (Brundidge et al., 2014; Norris, 2001; Van Dijk,
2006). Specifically, social media not only have the theoretical affordances to catalyze political
engagement and deliberation (Halpern & Gibbs, 2013), but also provide de facto platforms for
political expression and activism (e.g., Tufekci, 2017). Two forms of political use online have been
examined in the previous literature: active use, defined as two-way political communication (e.g.,
participating in online discussions about politics), and passive use, defined as one-way political
communication (e.g., following politicians on social media) (Bakker & De Vreese, 2011; Lutz &
Hoffmann, 2017). Scholars have revealed that privileged groups (e.g., well-educated, high-income,
young population) are more likely to be active content creators on social media (Brake, 2014;
Hargittai & Walejko, 2008). Following the existing literature, this study also considers a usage divide
in terms of active and passive political use.
Taken together, the study seeks to examine how people in different demographic and personality
groups use social media from three aspects: social (for maintaining existing ties or developing new
ties), informational (mainstream or alternative), and political (active or passive).
Social media usage divides in China
As of 2016, the number of China’s Internet users reached 731 million (53.2% of the population),
among which a large majority (84.3%) used social media (CNNIC, 2017a). Despite the country’s
rapid growth in the ICT industry, limited digital divide studies have been conducted, especially
regarding different usages. Based on previous literature sampling both China and other locations
(CNNIC, 2017a; Fong, 2009; Harwit, 2004; Van Deursen & van Dijk, 2013), we found that age,
gender, income, and education level, among other demographic variables, emerge as the common
factors that influence different types of ICT usages across borders. Therefore, we focus on these four
potential predictors of the six types of usage divides in this paper.
Previous studies have revealed potential divides in the two types of social usages in China.
Although Wang and Chen (2017) indicated that Chinese college students use WeChat for both
maintaining existing social ties and developing new ties, empirical evidence on how the usage
patterns vary across different social and economic groups in China is scarce. In fact, social
networking is of special importance to Chinese society, of which “guanxi,” or “personal connection,
relationship, or network” (Ruan, 2017, p. 1), is a defining characteristic. China is often considered an
“acquaintance society,” where social interaction is more restricted to acquaintances than its western
counterpart (Fei, 1992; He, 2011). Therefore, maintaining existing social ties, even when using social
media, is of great importance in China. Referring to Gil de Zúñiga et al. (2017)’s findings based a 20-
country survey that younger generations, women, and less-educated people use social media more
frequently for social interaction and considering Van Dijk and Hacker (2003)’s argument that higher
income groups enjoy better computer access, resources, and skills, we hypothesize:
H1a-d: Younger generations (a), women (b), higher-income population (c), and less educated people
(d) will have a relatively higher frequency of social use for maintaining existing ties on social media.
In an “acquaintance society,” we know that connecting with existing friends is a preferable usage of
social media. Nevertheless, to what extent the use of social media will break traditional norms and
facilitate networking beyond acquaintances is less clear among Chinese people. He et al. (2020)
found that digital access, especially access to tablet PCs and smartphones, as well as social media
usage, had positive connections older adults’ informal social participation, defined as the frequency
of participation in social activities mainly with strangers. The former relationship was also moder-
ated by age, indicating the existence of an age divide in the social use of ICTs in China. However, the
authors did not find a significant age divide in the relationship between social media usage and social
participation. Thus, we ask:
4Y. ZHANG ET AL.
RQ1a-d: How will age (a), gender (b), education (c), and income (d) be related to the frequency
divide of social use for developing new ties on social media?
Informational use of social media is also a major usage in China. 70.6% of Chinese Internet users
consume news on social media (CNNIC, 2017b). Nevertheless, there is a clear distinction between
getting information from mainstream and alternative sources. Although having experienced com-
mercialization in the 1990s, mainstream media in China is under strict governmental control
whether online or offline, and hence less likely to cover or provide independent views on heated
but politically-sensitive social topics (Fu & Lee, 2016; Stockmann, 2013). On the other hand, social
media provide channels for Chinese citizens to access news alternative to or critical of the official
discourse (Guo, 2017). Research has shown that alternative sources on Weibo and WeChat may have
the potential to bypass government censorship, but require more skills and effort to access (Fu et al.,
2013; King et al., 2013). Therefore, it is reasonable to assume that the more privileged social groups –
those who are more capable or more motivated to seek alternative information – will use social
media more for alternative news. We then raise the following hypotheses:
H2a-d: Younger generations (a), men (b), higher-income population (c), and more educated people
(d) will have a relatively higher frequency of alternative informational use on social media.
As for mainstream informational use, Guo (2017) suggested that when using social media for news,
older adults in China tended to avoid information and opinions that challenged the status quo.
However, the existing literature is limited to specific demographic-caused effects. Therefore, we ask:
RQ2a-d: How will age (a), gender (b), education (c), and income (d) be related to the frequency
divide of mainstream informational use on social media?
Similarly, a usage divide may also exist in terms of political use on social media in China. For
instance, Chen et al. (2016) indicated that women were significantly less likely to actively express
opinions or participate in activism online in mainland China. Beyond this demographic, it appeared
that older adults in China lagged behind not only in terms of accessing ICTs, but also adjusting their
political participation to the more active forms online (Xie & Jaeger, 2008). Guo (2017) also
suggested that some older adults, growing up through the Cultural Revolution, feared that discussing
politics on social media would lead to ramifications and therefore were more likely to lurk passively
online. Therefore, the following hypotheses are proposed:
H3a-b: Younger generations (a) and men (b) will have a relatively higher frequency of active political
use on social media.
H4a-b: Younger generations (a) and men (b) will have a relatively lower frequency of passive
political use on social media.
Aside from the gender- and age-generated divides, how income and education lead to gaps in active
and passive political use on social media in China remains unclear in the previous literature. We
RQ3a-b: How will education (a) and income (b) be related to the frequency divide of active political
use on social media?
RQ4a-b: How will education (a) and income (b) be related to the frequency divide of passive political
use on social media?
THE SOCIAL SCIENCE JOURNAL 5
The usage divide and personality traits
On top of the demographic variables, personality traits may have an additional impact on different uses
of social media. The measure of the Big Five personality traits, including extraversion, neuroticism,
openness to experiences, agreeableness, and conscientiousness, have been widely used to predict
behaviors related to social media use (Correa et al., 2010; Gil de Zúñiga et al., 2017; Hughes et al.,
2012; Ryan & Xenos, 2011). As defined in Seidman (2013), extraversion refers to one’s sociability, energy,
and talkativeness, which is related to more frequent and higher quality friend-making behaviors
(Asendorpf & Wilpers, 1998). Neuroticism, as the opposite of emotional stability, is related to anxiety.
Openness, defined as acceptance to creativity and novelty, is linked to more self-disclosure. Individuals
with a high level of agreeableness tend to be more helpful and show a more consistent, authentic
personality. Conscientiousness is often related to competence, order, dutifulness, achievement striving,
self-discipline, and deliberation (Matthews, Deary, & Whiteman, 2003).
Previous research has demonstrated the connection between the Big Five personality traits and
social media usage divides. Overall, all of the five personality traits were found to be positively
related to the general use of social media worldwide (Correa et al., 2010; Gil de Zúñiga et al., 2017;
Ryan & Xenos, 2011). Hawi and Samaha (2019) also found that among the Big Five, agreeableness,
conscientiousness, and emotional stability (the reverse of neuroticism) were negative predictors of
social media addiction. More specifically, each personality trait demonstrated distinct effects on
different social media usages. First, although extraversion and neuroticism showed positive effects on
the social use of social media across studies (Correa et al., 2010; Gil de Zúñiga et al., 2017; Hughes
et al., 2012), Gil de Zúñiga et al. (2017) and Hughes et al. (2012) observed contradictory effects of
conscientiousness and openness. As for informational use, agreeableness and neuroticism were
found to be positive predictors, while results were also mixed on the effects of conscientiousness
and openness. Lastly, Ryan and Xenos (2011) found that people who are more extraverted or more
neurotic tend to use Facebook more for active social contributions such as posting status and
commenting, but less for passive engagement like the following pages. Quintelier and Theocharis
(2013) also showed with a Belgian sample that extraversion, openness, and neuroticism were all
positive contributors to the political use of social media. The above literature provides some hints as
to the direction of the potential relationships between the Big Five and social media usage divides.
However, the studies did not address the six types of usages systematically and provide a horizontal
comparison. Also, the mixed results have not been tested in a non-western context. Therefore,
RQ5a-e: How will (a) extraversion, (b) neuroticism, (c) agreeableness, (d) conscientious, and (e)
openness be related to the six types of usages on social media in China?
Finally, previous studies indicated that extraversion, neuroticism, and openness significantly
interacted with age and gender when influencing different Internet usages. Based on an Israeli
sample, Hamburger and Ben-Artzi (2000) revealed that information services use was only
negatively related to neuroticism among men, whereas social services use was only negatively
connected to extraversion and positively predicted by neuroticism among women. Correa
et al. (2010) also found that extraversion, neuroticism, and openness predicted social media
usage differently by age and gender. Therefore, it is logical to assume that the three person-
ality traits will interact with the two demographic variables. Similar to the main effects, the
interaction effects of personality traits have never been examined in China’s context. We ask
RQ6a-c: How will (a) extraversion, (b) neuroticism, and (c) openness interact with demographic
factors (age and gender) in influencing the six types of usages on social media in China?
6Y. ZHANG ET AL.
This study is based on an online survey draws from the Word Digital Influence Project,
a collaboration between a research group based in New Zealand and Europe. The project collected
data in 22 countries from the Americas, Asia, Europe, and South Africa. The cooperation rate was
relatively high, averaging 77% across the 22 countries. The dataset was also used in Gil de Zúñiga &
Liu (2017). The data collection was conducted in September 2015 with 1,004 valid responses
recorded. For this study, we focused on data related to mainland China as our parameter of interest.
The data collection was administered by Nielsen, a U.S.-based international polling company.
Nielsen selected a stratified random sample from its pool of Chinese citizens with demographics (age
and gender) matching the 2010 Chinese Census made by China’s Office for National Statistics. Due
to the limitations of the web-only survey design, the collected sample is slightly younger and more
educated than the census data (See Table 1). However, as this study focuses on social media users,
these sampling biases will not compromise the validity of our results.
Informational, social, and political usages of social media
Three types of social media use, each with two dimensions, were measured on a series of multi-
item 7-point Likert scales (1 = Rarely and 7 = All the time). Respondents were asked to rate their
frequency of using social media for various activities during the past three months. The scales were
adapted from Lee and Ma (2012) and Valenzuela et al. (2009). The items for mainstream
informational use included, “To get news about current events from mainstream media (e.g.,
professional news services),” whereas the measure of alternative informational use was, “To get
news from alternative journalism sources (non-professional journalism, e.g., WeChat official
accounts).” Examples of social media use for existing and new social ties were, “To stay in touch
with friends and family” and “To meet new people who share interests,” respectively. Active and
passive political use on social media were measured by asking questions such as “Posting or sharing
thoughts about current events or politics” and “Friending, liking, or following a politician or political
figure.” The Cronbach’s α of informational, social, and political use on social media were 0.79,
0.91, and 0.96.
Overall social media use
Previous studies (e.g., Van Dijk & Hacker, 2003) directly compared the frequency of varied Internet
uses, ignoring that the variation could be brought about by the distinction of the total amount of
time spent online among social groups. In order to capture the relative frequencies of different types
Table 1. Demographic breakdown by age, gender and education versus census data.
Sample Census data
Age Group 18–24 10.5 12.7
25–34 31.5 14.9
35–44 27.9 18.2
45–64 27.2 24.3
65+ 2.9 8.9
Gender Female 44.4 48.8
Male 55.6 51.2
Education High School 9.3 15
Some College 23 5.5
College Degree+ 58.7 3.7
Graduate Degree+ 7.6 0.3
THE SOCIAL SCIENCE JOURNAL 7
of social media use among people of different demographics, individuals’ overall use time of social
media was controlled. Respondents were asked to rate their overall frequency of using social media
on a 7-point Likert scale (1 = Rarely and 7 = All the time; mean = 5.23, SD = 1.23).
The respondents’ age (median = 37, SD = 11.98), gender (men = 55.6%), household income before
taxes (mean = ￥187,068), and education level (mode = Bachelor degree) collected were used as
The big five personality traits
The study also included the Big Five personality traits as independent variables. The five traits are
openness (Cronbach’s α = 0.69), conscientiousness (Cronbach’s α = 0.71), extraversion (Cronbach’s
α = 0.81), agreeableness (Cronbach’s α = 0.72), and neuroticism (Cronbach’s α = 0.73) using the
scales constructed by Gil de Zúñiga et al. (2017). Each trait is measured with six different items on
a 7-point Likert scale (1 = Disagree Completely and 7 = Agree Completely).
Categorical variables, including ender and education, were dummy coded before the analysis.
Hierarchical linear regressions were used to examine the usage divide on social media. For each
usage, seven models (Models a-g) were tested. With overall social media use, measured by self-
reported overall frequency of using social media, as a control variable in Block 1, Model a also
measured the impact that demographic variables (Block 2) and the Big Five personality traits
(Block 3) have on the six types of social media usages respectively. In the following models
(Models b-g), the interaction terms addressed in RQ6a-c were added one by one.
Overall, the results show that age was the strongest demographic predictor across all types of social
media usages, whereas gender, income, and education were significantly associated with only some
usages. Among the Big Five personality traits, while extraversion and conscientiousness positively
predicted all six usages, the other three show distinct effects on different usages. The age- and
gender-induced relationships were also found to interact with extraversion, neuroticism, and open-
ness. Table 2 summarizes the statistical results.
The usage divides by demographic factors
The effects of demographic factors on social use of social media were tested in Models 1a and 2a.
Age was found to be negatively associated with social use for existing ties (β = −.102, p <.001).
Therefore, H1a was supported. Significant age-induced divide was also found in social use for new
ties (β = −.176, p < .001) (RQ1a). Gender, income, and education do not show significant impacts on
either social usage divide, which rejected H1b-d and addressed RQ1b-d.
As for informational usages, the two types show distinct relationships with demographic factors.
In terms of alternative information use, only age appears to be a significant negative predictor
(β = −.106, p < .01). Therefore, H2a was supported. H2b-d were rejected as the other relationships
were not significant. Contrarily, a “reverse divide” by age and income was found in terms of
mainstream informational use, but not alternative informational use. Specifically, older generations
(β = .083, p < .05) or lower-income populations (β = −.060, p < .05) used social media relatively more
for mainstream information, which is contradictory to the traditional assumption of the digital
8Y. ZHANG ET AL.
Table 2. Hierarchical linear regressions on the six types of social media usages.
Social use Informational use Political use
For existing ties For new ties Mainstream Alternative Active Passive
Block 1 – Overall social media use
Overall social media use .452*** .300*** .329*** .393*** .189*** .167***
Block 2 – Demographics
Age −.102*** −.176*** .083* −.106** −.148*** −.124***
Gender (women) .047 −.032 −.082** −.034 −.026 .018
Income −.004 .023 −.060* −.007 .025 .019
Education (comparing to less than college)
Some college .032 .084 .094* −.008 .110* .138*
Bachelor’s degree .078 .089 .097 −.012 .136* .159*
Graduate school or higher .066 .065 .027 −.001 .027 .047
Block 3 – Personality traits
Extraversion .140*** .308*** .187 .186*** .323*** .284***
Agreeableness .087* −.153*** .025 −.001 −.169*** −.120*
Conscientiousness .216*** .238*** .180 .150*** .162*** .134***
Neuroticism .025 −.042 −.080 .081* −.012 −.029
Openness −.037 −.155*** −.076 −.118** −.276*** −.268***
46.1% 30.2% 30.7% 25.7% 18.7% 15.6%
Block 4 – Interaction terms
Age × Extraversion
.269 −.032 .064 .194 −.190 −.228
0.2% 0.01% 0.01% 0.2% 0.1% 0.1%
Gender × Extraversion (model c) .109 .392** .055 .103 .575*** .543***
0.1% 0.7% 0.02% 0.05% 1.50% 1.30%
Age × Neuroticism
−.126 .261† −.016 −.023 .224† .313*
0.1% 0.3% 0.001% 0.002% 0.3% 0.4%
Gender × Neuroticism
−.130 −.380** .053 .073 −.485*** −.407**
0.1% 0.7% 0.02% 0.03% 1.2% 0.8%
Age × Openness
.122 −.444* .266 −.122 −.306 −.321
0.03% 0.3% 0.1% 0.02% 0.2% 0.2%
Gender × Openness
.315* .203 −.043 .090 .327† .303
0.3% 0.1% 0.005% 0.02% 0.3% 0.2%
Note. Entries are final-entry ordinary least squares (OLS) standardized coefficients (β). p-values are two-tailed. †p <.10, *p <.05; **p <.01; ***p <.001.
THE SOCIAL SCIENCE JOURNAL 9
divide. These results addressed RQ2a and RQ2c. However, men and people with the education level
of some college, who were socioeconomically more privileged, still spent significantly more time on
mainstream information use of social media than women (β = −.082, p < .01) and those with lower
education levels (β = .094, p < .05), which answered RQ2b and RQ2c.
Finally, age and education were the strongest predictors of active and passive political usage
divides on social media. Younger generations devoted relatively more time to active (β = −.148,
p < .001) and passive (β = −.124, p < .001) political use on social media than the older generations,
with the effect being greater in active political use. H3a was supported and H4a was rejected. H3b
and H4b were not supported as no significant gender-based differences were found in either type of
political usage. Compared to a less-educated population, people having some college education
(active: β = .110, p < .05; passive: β = .138, p < .05) or a bachelor degree (active: β = .136, p < .05;
passive: β = .159, p < .05) were significantly more active for both active and passive political use
(RQ3a and RQ4a). The coefficients were also larger for passive political usage, indicating a stronger
education-induced gap than active political use. Income was nevertheless not a significant predictor
of political usage divides on social media, which answered RQ3b and RQ4b.
The impact of personality traits
Block 3 in Model 1–6a included the Big Five in the regressions. Overall, except for mainstream
informational use, the other five types of usage divides were largely influenced by personality traits,
although the specific effects varied across usages.
First, extraversion predicted five of the six usages of social media positively (RQ5a). Among the
two types of social usages, making news ties (β = .308, p < .001) experienced greater influence from
extraversion than maintaining existing ties (β = .140, p < .001). As for information use, extraverted
people only used social media significantly more for alternative news (β = .186, p < .001), but not for
mainstream news. Also, extraversion tended to have a slightly stronger impact on social media use
for active political participation (β = .323, p < .001) than for the passive forms (β = .284, p < .001).
Second, the effects of agreeableness were only significant for the social and political use of social
media (RQ5b). People who were more agreeable, while they use social media relatively more
frequently for maintaining existing ties (β = .087, p < .05), appear to be less devoted to social use
for new ties (β = −.153, p < .001). The directions of the relationships were consistent between the two
types of political use, with higher impact on active political use (β = −.169, p < .001) than passive
political use (β = −.120, p < .001).
Similar to extraversion, conscientiousness also shows positive connections with all usages except
for mainstream information use (RQ5c). The connection conscientiousness had with social use for
new ties (β = .238, p < .001) was slightly stronger than with social use for existing ties (β = .216,
p < .001). People who were more conscientious also used social media significantly more for
alternative informational use (β = .150, p < .001) but not for mainstream informational use.
Similar to extraversion, active political use (β = .162, p < .001) was more influenced by conscien-
tiousness than passive use (β = .134, p < .001).
Neuroticism, contrary to the previous literature, was the weakest personality predictor (RQ5d). It
was only positively related to alternative informational use (β = .081, p < .05) and showed no
significant relationships with other usages.
Lastly, the effects of openness on usage divides of social media were mostly negative (RQ5e).
While not significantly associated with maintaining existing ties, openness negatively predicted
people’s relative time spent on making new friends on social media (β = −.155, p < .001). This
finding may speak to the mixed results found in previous studies. As for the two types of
informational use, openness was found to be negatively related to only alternative informational
use (β = −.118, p < .01). People who were more open to new experiences also spent relatively less
time on active (β = −.276, p < .001) and passive (β = −.268, p < .001) political use of social media.
10 Y. ZHANG ET AL.
Interaction effects between demographic and personality traits
Based on Models b-g, RQ6a-c considered the extent to which extraversion, neuroticism, and
openness interact with the demographic variables (age and gender) and how they collectively
influence varied usages of social media. The results show that extraversion only significantly
interact with gender in influencing social use for new ties (β = .392, p < .01) as well as active
(β = .575, p < .001) and passive (β = .547, p < .001) political use on social media (RQ6a). As
shown in Figure 1, among extraverts, women spent relatively more time on the social use of
social media for existing ties and on active political use than men, while the relationship was
reversed for a less extraverted population. On the contrary, women were less likely to devote
their time to social use for new ties among extraverts but more likely to do so among non-
Neuroticism intervened in the effects of both age and gender (RQ6b). As indicated in Figure 2, for
social use for active ties (β = −.380, p < .01) as well as active (β = −.485, p < .001) and passive (β = −.407,
p < .001) political use, women showed relatively less usage than men, whereas the divides were reversed
among the low neurotic group. As for age, although older generations spent relatively less time on passive
political use than the younger ones in both low and high neuroticism groups, the age-generated
difference was larger for the former group.
Openness interacted with both age and gender in creating usage divides (RQ6c, see Figure 3). For
people who were more open to new experiences, women used social media more frequently than
men for maintaining existing ties; conversely, men devoted slightly more time than women among
the less open population (β = .315, p < .05). Openness also strengthened the negative impact of age
on social use for new ties, leading to a wider gap between the old and the young among highly open
people as compared to less open (β = −.444, p < .05).
Figure 1. Interaction effects between extraversion and gender.
THE SOCIAL SCIENCE JOURNAL 11
This study contributes to the digital divide literature by explicating the six types of usage divides and
by adding the effects of both demographic and personality traits into the discussion. The results
show that both demographic and personality traits significantly predicted different aspects of the
usage divide, with a “reverse divide” found in mainstream informational use. This paper also
suggests that extraversion, neuroticism, and openness, among all the Big Five personality traits,
significantly interact with age and gender in influencing the varied social media usages. Detailed
implications are discussed below.
Demographic factors: divide and “Reverse divide”
According to the results, usage gaps on social media are still prevalent in China for age, gender, and
education, but not for income. Compared to the younger generations, elderly citizens lagged behind
in using social media for building existing and new social ties, for consuming alternative
Figure 2. Interaction effects of neuroticism and age/gender.
Figure 3. Interaction effects of openness and age/gender.
12 Y. ZHANG ET AL.
information, and for active and political use, regardless of their personality traits. women and the
lower-income population were also found to consume relatively less mainstream information from
social media. These findings suggest that while certain ICTs enable disadvantaged groups to connect
to the digital world, these individuals may not necessarily partake in the opportunity by fully
utilizing social media to expand their social capital, keep informed, and participate in politics. As
indicated in previous literature (Gil de Zúñiga, 2006; Gunkel, 2003; Hacker & Steiner, 2001; Van
Dijk, 2005), failing to use certain functions will prevent social groups from enjoying certain benefits
brought by ICTs, which may exacerbate social and economic inequalities in a long term.
Nevertheless, we found a “reverse divide” in mainstream informational use of social media.
Contrary to the situation described in previous literature, the underprivileged groups – the elderly
and the lower-income population – used social media more for mainstream informational use than
their more privileged counterparts. This, on one side, speaks to the saturation of social media access
in China. According to CNNIC (2016), 90.7% of Chinese use instant message applications and 77%
use other forms of social media. Specifically, the spread of budget mobile devices contributed to the
proliferation of social media use and enabled low-income populations to get connected (Wang,
2015). Guo (2017) also found that, while many older Chinese adults rarely use computers to access
the Internet, they use the mobile-based WeChat on a daily basis to follow news updates. Thus,
mainstream informational use of social media, especially through mobile devices, may serve as
a starting point for bridging digital inequalities.
On the other hand, it seems that the older adults and the poor are still stuck at the most basic
level of social media use. In an information environment that is highly controlled by the govern-
ment, mere exposure to the “mainstream life” on social media cannot guarantee well-informed
citizens. While disseminating alternative information is one of the most celebrated democratizing
potentials of China’s social media (Fu et al., 2013; King et al., 2013), it is not equally enjoyed by the
whole population. This divide could prevent the formation of a healthy civic society. The reversed
informational use pattern on social media between the young and the old may also indicate an
ideological gap between the two generations because of the unique history in China, which was
carried over to the utilization of ICTs (Guo, 2017).
Regarding the gender differences, a potential explanation on why H3b and H4b were not
supported lies on the fact that once penetration of social media use reaches a high level (saturation
mode), structural divides continue to play important roles (e.g., education) but gender distinction do
not make such a stark difference. Only when the participatory behaviors include various degrees of
nuances, these differences can be observed. In other words, there may not be a gender divide in
general active or passive participation levels as revealed in this study. Yet, the difference may remain
if examining more specialized ways of participation, which calls for future exploration.
Personality traits: direct and indirect impact
By including the Big Five personality traits into the analysis, the study found that individuals’
personality traits did influence social media usages directly and indirectly. As indicated in the
results, the impact of each personality trait varied across different usages, which expands previous
literature that mostly focused on the overall use of social media. Also, the Big Five personality traits
showed stronger predictive power than demographics in several occasions, indicating that digital
divide research should not merely be dominated by using demographic variables as predictors.
Specifically, along the same line of the previous studies (Correa et al., 2010; Hughes et al., 2012;
Ryan & Xenos, 2011), extraverted people, who are normally more social, and conscientious people,
who are believed to be more aware of their political responsibilities, had a higher frequency of use
across most usages. People who were more agreeable, although more devoted to maintaining existing
ties relatively on social media, were less likely to use the platforms for meeting new people or
participating in politics; these tendencies may be explained by the “acquaintance society” and the
highly sensitive political environment in China. In addition, with the mainstream informational
THE SOCIAL SCIENCE JOURNAL 13
sources strictly censored, it is not surprising that neurotic individuals were more eager to seek
alternative information on social media.
More interestingly, openness showed an impact that is contradictory to what was found in
western cases (Gallego & Oberski, 2012; Quintelier & Theocharis, 2013; Ryan & Xenos, 2011), as
people who were more open to new things were found to use social media less for building new ties,
getting alternative news, and participating in politics. The explanation could be twofold. First, as
shown above, the widely-used social media in China are not as novel as in its nascence. People may
regard social media as a routine part of life and have turned to other channels for new experiences.
Second, being aware of the rules of “acquaintance society,” the information censorship, and the
political sensitivity in China, people who are more open may be more willing to seek for ways to
make friends, read news, and be involved in politics elsewhere, such as offline.
Lastly, the study revealed significant interaction effects between demographic and personality
traits on social media usage divides. Notably, multiple relationships, especially between gender and
social media usages, were reversed with the moderation of extraversion, neuroticism, and openness.
Earlier scholars suggested that personality traits could change during the period of young adulthood
(i.e., 20–40 years old; Roberts & Mroczek, 2008; Srivastava et al., 2003) and even at an older age (i.e.
43–91 years old; Mroczek & Spiro, 2003). Thus, the results of this study may suggest the potential of
an alternative solution, other than promoting ICTs literacy, to address digital inequalities.
Overall, this exploratory study investigated demographic- and personality-induced usage divides
in mainland China, with different patterns found across the six social media uses. Nevertheless, the
study is not free from limitations. The data was collected in 2015, which is at the nascence of social
media in China when Internet penetration was only 50.3% (CNNIC, 2017b) and when the earlier
adaptors of social media are younger and much more educated compared to the general population.
Thus, the results of this study may not reflect the current situation of social media usage divides in
China, but the goal is to present how ICTs are used by different people in various ways before it
reaches a saturated usage. The 2015 data can also provide valuable contrast with the more recent
observations. Future research could obtain more recent data to verify the model found in the current
study and observe overtime effects changes.
Also, the current study is cross-sectional and thus cannot reveal whether the divides are widening
or narrowing. As Van Dijk and Hacker (2003) noted, future studies should provide more empirical
longitudinal evidence to capture the change over time of the six types of usage divides. Lastly, this
study was conducted in a particular context in China. As mentioned before, the uniqueness of
China’s media environment and users’ characteristics may limit the generalizability of the findings.
Nevertheless, the impact pattern of demographic factors and personality traits on social media usages
have the potential to hold in contexts outside of China under the discussed circumstances. Future
scholars should continue to examine social media usage divide in other contexts to provide a more
complete picture of how users across borders understand and utilize the emerging ICTs.
This research was supported by Grant FA2386-15-1-0003 from the Asian Office of Aerospace Research and
Development. Responsibility for the information and views set out in this study lies entirely with the authors.
Yiyan Zhang http://orcid.org/0000-0003-4299-0236
Lei Guo http://orcid.org/0000-0001-9971-8634
Homero Gil de Zúñiga http://orcid.org/0000-0002-4187-3604
14 Y. ZHANG ET AL.
Ali, A. H. (2011). The power of social media in developing nations: New tools for closing the global digital divide and
beyond. Harvard Human Rights Journal, 24, 185. https://harvardhrj.com/wp-content/uploads/sites/14/2009/09/185-
Asendorpf, J. B., & Wilpers, S. (1998). Personality effects on social relationships. Journal of Personality and Social
Psychology, 74(6), 1531–1544. https://doi.org/10.1037/0022-3518.104.22.1681
Bakker, T. P., & De Vreese, C. H. (2011). Good news for the future? Young people, Internet use, and political
participation. Communication Research, 38(4), 451–470. https://doi.org/10.1177/0093650210381738
Bikson, T. K., & Panis, C. W. (1997). Computers and connectivity: Current trends. In Kiesler, S. (Eds.), Culture of the
Internet. Lawrence Erlbaum Associates Publishers.
boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of computer-
mediated communication,13(1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.x
Brake, D. R. (2014). Are we all online content creators now? Web 2.0 and digital divides. Journal of Computer-
Mediated Communication, 19(3), 591–609. https://doi.org/10.1111/jcc4.12042
Brundidge, J., Garrett, R. K., Rojas, H., & Gil de Zúñiga, H. (2014). Political participation and ideological news
online:“Differential gains” and “differential losses” in a presidential election cycle. Mass Communication and
Society, 17(4), 464–486. https://doi.org/10.1080/15205436.2013.821492
Chen, H. T., Chan, M., & Lee, F. L. (2016). Social media use and democratic engagement: A comparative study of
Hong Kong, Taiwan, and China. Chinese Journal of Communication, 9(4), 348–366. https://doi.org/10.1080/
Chen, W., & Wellman, B. (2004). The global digital divide–within and between countries. IT & Society, 1(7), 39–45.
CNNIC. (2016). 2015 Research report on user behaviors on social applications in China. http://www.cac.gov.cn/files/
CNNIC. (2017a). 2016 China Internet news market research report.
CNNIC. (2017b). 38th statistical survey report on the Internet development.
Correa, T., Hinsley, A. W., & De Zuniga, H. G. (2010). Who interacts on the Web?: The intersection of users’
personality and social media use. Computers in Human Behavior, 26(2), 247–253. https://doi.org/10.1016/j.chb.
Fan, B. (2015). 2015 Weibo users development report. Sina Weibo. http://data.weibo.com/report/reportDetail?id=297
Fei, X. (1992). From the soil: The foundations of Chinese society: A translation of: Fei Xiatong’s Xiangtu Zhongguo.
University of California Press.
Fong, M. W. (2009). Digital divide between urban and rural regions in China. The Electronic Journal of Information
Systems in Developing Countries, 36(1), 1–12. https://doi.org/10.1002/j.1681-4835.2009.tb00253.x
Fu, J. S., & Lee, A. Y. L. (2016). Chinese journalists’ discursive weibo practices in an extended journalistic sphere.
Journalism Studies, 17(1), 80–99. https://doi.org/10.1080/1461670X.2014.962927
Fu, K. W., Chan, C. H., & Chau, M. (2013). Assessing censorship on microblogs in China: Discriminatory keyword
analysis and the real-name registration policy. IEEE Internet Computing, 17(3), 42–50. https://doi.org/10.1109/MIC.
Gallego, A., & Oberski, D. (2012). Personality and political participation: The mediation hypothesis. Political Behavior,
34(3), 425–451. https://doi.org/10.1007/s11109-011-9168-7
Gil de Zúñiga, H. (2006). Reshaping digital inequality in the European Union: How psychological barriers affect
internet adoption rates. Webology, 3(4), 32. https://www.webology.org/2006/v3n4/a32.html
Gil de Zúñiga, H., & Diehl, T. (2017). Citizenship, social media, and big data: Current and future research in the social
sciences. Social Science Computer Review, 35(1), 3–9. https://doi.org/10.1177/0894439315619589
Gil de Zúñiga, H., Diehl, T., Huber, B., & Liu, J. (2017). Personality traits and social media use in 20 countries: How
personality relates to frequency of social media use, social media news use, and social media use for social
interaction. Cyberpsychology, Behavior and Social Networking, 20(9), 540–552. https://doi.org/10.1089/cyber.2017.
Gil de Zúñiga, H., Jung, N., & Valenzuela, S. (2012). Social media use for news and individuals’ social capital, civic
engagement and political participation. Journal of Computer-Mediated Communication, 17(3), 319–336. https://doi.
Gil de Zúñiga, H., & Liu, J. H. (2017). Second screening politics in the social media sphere: Advancing research on
dual screen use in political communication with evidence from 20 countries. Journal of Broadcasting & Electronic
Media, 61(2), 193–219. https://doi.org/10.1080/08838151.2017.1309420
Gil de Zúñiga, H., & Liu, J. H. (2017). Second screening politics in the social media sphere: Advancing research on
dual screen use in political communication with evidence from 20 countries. Journal of Broadcasting & Electronic
Goldfarb, A., & Prince, J. (2008). Internet adoption and usage patterns are different: Implications for the digital divide.
Information Economics and Policy, 20(1), 2–15. https://doi.org/10.1016/j.infoecopol.2007.05.001
THE SOCIAL SCIENCE JOURNAL 15
Gunkel, D. J. (2003). Second thoughts: Toward a critique of the digital divide. New Media & Society, 5(4), 499–522.
Guo, L. (2017). WeChat as a semipublic alternative sphere: Exploring the use of WeChat among Chinese older adults.
International Journal of Communication, 11, 21. https://ijoc.org/index.php/ijoc/article/view/5537
Hacker, K. L., & Steiner, R. (2001). Hurdles of access and benefits of usage for Internet communication.
Communication Research Reports, 18(4), 399–407. https://doi.org/10.1080/08824090109384821
Halpern, D., & Gibbs, J. (2013). Social media as a catalyst for online deliberation? Exploring the affordances of
Facebook and YouTube for political expression. Computers in Human Behavior, 29(3), 1159–1168. https://doi.org/
Hamburger, Y. A., & Ben-Artzi, E. (2000). The relationship between extraversion and neuroticism and the different
uses of the internet. Computers in Human Behavior, 16(4), 441–449. https://doi.org/10.1016/S0747-5632(00)00017-0
Hargittai, E. (2001). Second-level digital divide: Differences in people’s online skills. First Monday, 7(4). https://doi.
Hargittai, E., & Walejko, G. (2008). The participation divide: Content creation and sharing in the digital age.
Information, Community and Society, 11(2), 239–256. https://doi.org/10.1080/13691180801946150
Harris, C., Straker, L., & Pollock, C. (2017). A socioeconomic related ‘digital divide’ exists in how, not if, young people
use computers. PloS One, 12(3), e0175011. https://doi.org/10.1371/journal.pone.0175011
Harwit, E. (2004). Spreading telecommunications to developing areas in China: Telephones, the internet and the
digital divide. The China Quarterly, 180, 1010–1030. https://doi.org/10.1017/S0305741004000724
Hawi, N., & Samaha, M. (2019). Identifying commonalities and differences in personality characteristics of Internet
and social media addiction profiles: Traits, self-esteem, and self-construal. Behaviour & Information Technology, 38
(2), 110–119. https://doi.org/10.1080/0144929X.2018.1515984
He, T., Huang, C., Li, M., Zhou, Y., & Li, S. (2020). Social participation of the elderly in China: The roles of
conventional media, digital access and social media engagement. Telematics and Informatics, 48, 101347. https://
He, X. (2011). Renqing in an Acquaintance Society. Journal of Nanjing Normal University (Social Science Edition), 4,
Hughes, D. J., Rowe, M., Batey, M., & Lee, A. (2012). A tale of two sites: Twitter vs. Facebook and the personality
predictors of social media usage. Computers in Human Behavior, 28(2), 561–569. https://doi.org/10.1016/j.chb.2011.
Institute of Social Science Survey, Peking University. (2015) . China Family Panel Studies (CFPS) [Data set]. Peking
University Open Research Data Platform. https://doi.org/10.18170/DVN/45LCSO
Kim, M. C., & Kim, J. K. (2001, July). Digital divide: Conceptual discussions and prospect. In International Conference
Human Society@ Internet (pp. 78–91). Springer, Berlin, Heidelberg.
King, G., Pan, J., & Roberts, M. E. (2013). How censorship in China allows government criticism but silences collective
expression. American Political Science Review, 107(2), 326–343. https://doi.org/10.1017/S0003055413000014
Lee, C. S., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience. Computers
in Human Behavior, 28(2), 331–339. https://doi.org/10.1016/j.chb.2011.10.002
Lin, N. (2008). A network theory of social capital. In D. Castiglione, J. W. van Deth & G. Wolleb (Eds.), The handbook
of social capital (pp. 50–69). London: Oxford University Press.
Lutz, C., & Hoffmann, C. P. (2017). The dark side of online participation: Exploring non-, passive and negative
participation. Information, Communication & Society, 20(6), 876–897. https://doi.org/10.1080/1369118X.2017.
Matthews, G., Deary, I. J., & Whiteman, M. C. (2003). Personality traits. Cambridge University Press.
Mroczek, D. K., & Spiro, A., III. (2003). Modeling intraindividual change in personality traits: Findings from the
normative aging study. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 58(3), P153–
Newman, N. (2011). Mainstream media and the distribution of news in the age of social discovery. Reuters Institute for
the Study of Journalism. http://reutersinstitute.politics.ox.ac.uk/fileadmin/documents/Publications/
Norris, P. (2001). Digital divide: Civic engagement, information poverty, and the Internet worldwide. Cambridge
Pimmer, C., Linxen, S., & Gröhbiel, U. (2012). Facebook as a learning tool? A case study on the appropriation of social
network sites from mobile phones in developing countries. British Journal of Educational Technology, 43(5),
Poell, T., & Borra, E. (2012). Twitter, YouTube, and Flickr as platforms of alternative journalism: The social media
account of the 2010 Toronto G20 protests. Journalism, 13(6), 695–713. https://doi.org/10.1177/1464884911431533
Quintelier, E., & Theocharis, Y. (2013). Online political engagement, Facebook, and personality traits. Social Science
Computer Review, 31(3), 280–290. https://doi.org/10.1177/0894439312462802
Roberts, B. W., & Mroczek, D. (2008). Personality trait change in adulthood. Current Directions in Psychological
Science, 17(1), 31–35. https://doi.org/10.1111/j.1467-8721.2008.00543.x
16 Y. ZHANG ET AL.
Ruan, J. (2017). Guanxi, social capital and school choice in China the rise of ritual capital (Palgrave studies on Chinese
education in a global perspective). Springer International Publishing : Imprint: Palgrave Macmillan.
Ryan, T., & Xenos, S. (2011). Who uses Facebook? An investigation into the relationship between the Big Five, shyness,
narcissism, loneliness, and Facebook usage. Computers in Human Behavior, 27(5), 1658–1664. https://doi.org/10.
Seidman, G. (2013). Self-presentation and belonging on Facebook: How personality influences social media use and
motivations. Personality and Individual Differences, 54(3), 402–407. https://doi.org/10.1016/j.paid.2012.10.009
Srivastava, S., John, O. P., Gosling, S. D., & Potter, J. (2003). Development of personality in early and middle
adulthood: Set like plaster or persistent change? Journal of Personality and Social Psychology, 84(5), 1041. https://
Statista. (2018). Number of social network users worldwide from 2010 to 2021 (in billions). Statista https://www.statista.
Stockmann, D. (2013). Media commercialization and authoritarian rule in China. Cambridge University Press.
Tencent. (2016). Tencent publicized company performance in 2015 Q4 and in the year. Tencent. https://www.tencent.
Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth in knowledge. Public
Opinion Quarterly, 34(2), 159–170. https://doi.org/10.1086/267786
Tufekci, Z. (2017). Twitter and tear gas: The power and fragility of networked protest. Yale University Press.
Valenzuela, S., Park, N., & Kee, K. F. (2009). Is there social capital in a social network site?: Facebook use and college
students’ life satisfaction, trust, and participation. Journal of Computer-mediated Communication, 14(4), 875–901.
van Deursen, A. J., & van Dijk, J. A. (2013). The digital divide shifts to differences in usage. New Media & Society, 16
(3), 507–526. https://doi.org/10.1177/1461444813487959
van Deursen, A. J., & van Dijk, J. A. (2015). Toward a multifaceted model of Internet access for understanding digital
divides: An empirical investigation. The Information Society, 31(5), 379–391. https://doi.org/10.1080/01972243.
van Dijk, J., & Hacker, K. (2003). The digital divide as a complex and dynamic phenomenon. The Information Society,
19(4), 315–326. https://doi.org/10.1080/01972240309487
van Dijk, J. A. (2005). The deepening divide: Inequality in the information society. Sage Publications.
van Dijk, J. A. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4–5), 221–235. https://doi.
Wang, X. (2015, May 6). Chinese low-end smartphone market: The era of ‘shanzhai’ has passed, budget smartphones
dominate | UCL global social media impact study. Global Social Media Impact Study. https://blogs.ucl.ac.uk/global-
Wang, Y., & Chen, Z. (2017). Relationship resources investment and return in the social networks on WeChat [in
Chinese]. Modern Communication (Journal of Communication University of China), 11, 010.
Warschauer, M. (2003). Demystifying the digital divide. Scientific American, 289(2), 42–47. https://doi.org/10.1038/
Wei, L., & Hindman, D. B. (2011). Does the digital divide matter more? Comparing the effects of new media and old
media use on the education-based knowledge gap. Mass Communication and Society, 14(2), 216–235. https://doi.
Willis, S., & Tranter, B. (2006). Beyond the ‘digital divide’ Internet diffusion and inequality in Australia. Journal of
Sociology, 42(1), 43–59. https://doi.org/10.1177/1440783306061352
Xie, B., & Jaeger, P. T. (2008). Older adults and political participation on the internet: A cross-cultural comparison of
the USA and China. Journal of Cross-cultural Gerontology, 23(1), 1–15. https://doi.org/10.1007/s10823-007-9050-6
THE SOCIAL SCIENCE JOURNAL 17