Information, Communication & Society
ISSN: 1369-118X (Print) 1468-4462 (Online) Journal homepage: https://www.tandfonline.com/loi/rics20
The ‘bad women drivers’ myth: the
overrepresentation of female drivers and gender
bias in China’s media
Muyang Li & Zhifan Luo
To cite this article: Muyang Li & Zhifan Luo (2020): The ‘bad women drivers’ myth: the
overrepresentation of female drivers and gender bias in China’s media, Information,
Communication & Society, DOI: 10.1080/1369118X.2020.1713843
To link to this article: https://doi.org/10.1080/1369118X.2020.1713843
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INFORMATION, COMMUNICATION & SOCIETY
https://doi.org/10.1080/1369118X.2020.1713843
The ‘bad women drivers’ myth: the overrepresentation of
female drivers and gender bias in China’s media
Muyang Li
and Zhifan Luo
Department of Sociology, University at Albany, SUNY, Albany, NY, USA
ABSTRACT
ARTICLE HISTORY
The body of literature on underrepresentation and gender
inequality is vast. However, despite its potential to perpetuate
gender stereotypes, the overrepresentation of women in media
has received inadequate attention. This study explores how
traditional news media and social media overrepresent females as
drivers when discussing traffic accidents, and whether social
media could be the ‘new equalizer’ for gender. Focusing on
China, we collected 97,120 posts from Weibo, China’s largest
microblogging site, and 11,290 newspaper articles dated between
January 2010 and November 2018. We analyzed the data through
a mixed-methods design and found that female drivers are
overrepresented in discussions of traffic accidents, in both
newspapers and on Weibo. While the gender bias against female
drivers is more prevalent on Weibo than in newspapers, Weibo
has provided a platform for gender-aware discussion. Our study
closes by offering suggestions for cross-platform and cross-cultural
comparisons of gender representation in the digital age.
Received 2 September 2019
Accepted 23 December 2019
KEYWORDS
Big data; social media;
gender; media studies; China
1. Introduction
Female drivers’ experience mirrors women’s social position. A century ago in the United
States, ‘women at the wheel posed a serious threat to long-established ideas and practices’
of masculinity (Berger, 1986, p. 257). Such fears developed into the full-blown stereotype
of ‘bad women drivers,’ despite statistics showing that females were as good, if not better,
drivers than males (Berger, 1986). The female driver folklore failed to keep women at
home in the US. In many societies, however, women who drive are still confined by gender
bias – patriarchal norms that discriminate against women and practices that hinder them
from exercising the same rights as men in social life (Chappell & Waylen, 2013; Heilman,
2012). This bias can be built into legal systems, such as in Saudi Arabia where women did
not have the legal right to drive until 2018. It may also persist in the daily experience of
social rejection. For instance, in India, many female drivers feel unwelcome on the
road, despite their legal right to drive (Ram & Dhawan, 2018). Gender bias may even
be manifested in the form of patriarchal care. One example is the oversized womenonly parking spaces, marked by pink signs, in many cities in South Korea and China
CONTACT Muyang Li
mli2@albany.edu
Department of Sociology, University at Albany, SUNY, 351 Arts & Sciences
Bldg, 1400 Washington Avenue, Albany 12222, NY, USA
Supplemental data for this article can be accessed https://doi.org/10.1080/1369118X.2020.1713843
© 2020 Informa UK Limited, trading as Taylor & Francis Group
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M. LI AND Z. LUO
(Withnall, 2014). It has aroused heated debates on Weibo, China’s largest microblogging
site. Despite some critiques of this practice as being sexist, an online survey has shown that
the majority of the respondents regarded it as generous (Zhou & Xiao, 2018).
When women step into public domains previously dominated by men, stereotypes
based on conventional gender roles may follow, sometimes wearing the mask of patriarchal care. Such extra attention to women, sometimes presuming they are less competent
than men, may be reflected in folklore, public policy, and media coverage. This cross-cultural phenomenon challenges the existing study of gender bias, focusing on the underrepresentation of women in public life: how does overrepresentation perpetuate gender
stereotypes? In this study, we examine the role of traditional news media and social
media in the overrepresentation of female drivers, with a focus on the latter, since the
rise of social media has created both opportunities for and challenges to gender equality.
We choose the case of China, where the public discussion on driving as a gendered behavior is currently transpiring (Hu, 2016; Luo, 2015).
We address the following questions: Are female drivers overrepresented in discussions
of traffic accidents? How does it vary between social media and traditional news media?
Also, when focusing on social media, is biased content more prevalent? Does it vary across
user types? Additionally, can social media be the ‘new equalizer’ for gender? To address
these questions, we used a mixed-methods design to understand if and how the overrepresentation of female drivers has perpetuated gender bias. We started with quantitative
analysis to examine the relationship between the quantitative features of the content,
and then used computer-assisted text analysis to explore the semantic structures of the
text. This was followed by an interpretive analysis of the meaning-making process of
the discussion. Our study finds evidence that female drivers are overrepresented in discussions of traffic accidents, in both newspapers and on Weibo. While the gender bias against
female drivers is more prevalent on Weibo than in newspapers, Weibo has provided a platform for gender-aware discussion. Our study closes by offering suggestions for cross-platform and cross-cultural comparisons of gender representation in the digital age.
2. Theoretical background
2.1. The underrepresentation and stereotypes of women in traditional news
media
The institution of media can reflect and reinforce gender stereotypes (Holtzman & Sharpe,
2014). Many studies demonstrate that women are underrepresented in traditional news
media (Kahn & Goldenberg, 1991; Matthews, 2007; Vos, 2013). Among female subjects
covered, traditional news media tend to emphasize their feminine aspects associated
with traditional gender roles (Niven, 2005), trivialize or stigmatize their presence, and
depict them as fragile, subordinate, and sexually suggestive (Collins, 2011; Armstrong,
2013). For instance, despite women’s increasing visibility in the political arena, researchers
have discovered stark contrast between the coverage of male and female candidates (Heldman, Carroll, & Olson, 2005; Lawless, 2009). Similar gendered patterns have been found in
news coverage in other fields where hegemonic masculinity is entrenched, such as sports
(Bernstein, 2002; Koivula, 1999) and science (LaFollette, 1990; Steinke, 2012).
INFORMATION, COMMUNICATION & SOCIETY
3
Several scholars have attributed the underrepresentation and stereotypical depiction of
women in traditional news media to newsroom gender composition and suggested balancing gender ratios among news producers as the solution. They argue that female reporters
tend to employ more gender-sensitive perspectives than male reporters (Kim & Yoon,
2009), and that gender equality in news-making may change the culture of the newsroom
(Shor, van de Rijt, Miltsov, Kulkarni, & Skiena, 2015). Meanwhile, others think the problem transcends the newsroom; it is a problem of society at large. They argue that the gendered content in reporting reflects women’s status in the real world (Baitinger, 2015; Shor
et al., 2015).
2.2. Digital media for gender inequality: equalizer or perpetuator?
The rise of digital media constitutes new opportunities and challenges for gender equality.
Optimists believe that social media could improve women’s representation and mitigate
gender stereotypes. Some have found that social media can promote women’s self-empowerment (Yarchi & Samuel-Azran, 2018) and generate feminist imagery and counter-discourses (Bruce & Hardin, 2014). In many cases, women have relied on social media to
confront the values of ‘toxic masculinity,’ such as rape culture, and to hold stakeholders
accountable (Rentschler, 2014; Sills et al., 2016). Studies of the recent #MeToo movement
show that social media has expanded the resource repertoires women can mobilize to form
support communities and promote feminist agendas (Andalibi, Haimson, Choudhury, &
Forte, 2018; Manikonda, Beigi, Kambhampati, & Liu, 2018). In China’s movement against
sexual harassment and assault, social media constitutes a major platform for public discussion and education, and social mobilization (Yuan, 2018).
Despite the space social media platforms create for feminists to resist social oppression,
scholars note that digital media technology itself could facilitate ‘algorithmic oppression.’
Furthermore, its application could reproduce, or even amplify, pre-existing bias against
minority groups (Eubanks, 2018; Noble, 2018). Kay, Matuszek, and Munson (2015)
have found that Google’s search algorithm has been trained to exaggerate gender bias
and systematically underrepresent women because search results consistent with conventional gender roles have better ratings. The social media environment may also perpetuate
gender inequality even without the use of the algorithm. For example, similar to the gendered ‘glass ceiling’ in the real world, top-tier Twitter users perceived to be female are less
likely to be followed, listed, and retweeted than their male counterparts (Nilizadeh et al.,
2016). Also, Davis (2018) has noted that Instagram photos and videos perpetuate gender
inequality by objectifying females. Furthermore, certain social media features have made
women more vulnerable to targeting, harassment, and threats than they would be in the
offline environment (Mantilla, 2015).
2.3. Is gendered content on social media more or less biased?
Social media and traditional news media differ in terms of their content generators, generation, and distribution processes. Coverage on traditional news media is produced by
professionals who follow journalistic norms and routines, whereas social media content
is created by complex user groups lacking uniform rules and standards. Given these differences in the dynamics of content generation and distribution, how does gendered content
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M. LI AND Z. LUO
vary across media platforms? Some studies have found that social media empowers
women to defy stereotypes imposed by news media and to control their gendered representations because there is a lower threshold for being a content generator on social
media (Litchfield & Kavanagh, 2019; Shreffler, Hancock, & Schmidt, 2016). However, evidence also suggests that gender discrimination in the quantity and quality of coverage of
women on social media is as salient as that on traditional news media (Armstrong & Gao,
2011; WACC, 2015). Moreover, researchers have found that content generators on social
media are more likely to produce content that meets other users’ expectations due to the
quick response cycle (McCracken, FitzSimons, Priest, Girstmair, & Murphy, 2018). The
effects of this phenomenon on gender equality are unclear. It remains unknown whether
gendered content on social media, which is free from the restraints of journalistic ethics, is
more or less biased.
When comparing contents across platforms, social media’s content generators are not
only broader, but also more diverse than those of traditional news media. Social media
users vary in their content generation and posting behavior (De Choudhury, Diakopoulos,
& Naaman, 2012; Starbird, Palen, Hughes, & Vieweg, 2010). Certain events (e.g., traffic accidents) are more likely to be posted by users with access to a particular type of information,
such as those in law enforcement or media. Content origins can also shape how social media
users discuss and perceive information (De Silva & Riloff, 2014). Content posted by organizations, celebrities, popular individuals, or media draw public attention (Wu, Hofman,
Mason, & Watts, 2011) and influence individual users’ views (Guo, Rohde, & Wu, 2020; Tremayne & Minooie, 2013). As most research on gendered content fails to evaluate how it is
influenced by internal diversity, empirical studies are needed to compare not only media
platforms, but also user types on social media. This will help understand platforms’ and
user-types’ effects on the production and distribution of gendered content.
2.4. The more, the worse: the overrepresentation of women in media and gender
equality
Most prior studies have focused on the underrepresentation of female characters, and contend that this is an essential indicator of gender inequality. People may wonder whether
increasing the representation of women in media mitigates gender stereotypes. However,
how women are overrepresented by media has rarely been studied. Among the few existing
studies, scholars have found that women are overrepresented as slim and attractive figures
(Fouts & Burggraf, 1999; Zhang, Dixon, & Conrad, 2010), or in roles such as housewives
(Paek, Nelson, & Vilela, 2011; Sink & Mastro, 2017) or victims (Clifford, Jensen, & Petee,
2009; De Ceunynck, De Smedt, Daniels, Wouters, & Baets, 2015). This reveals that women
remain the subjects of stereotyped coverage. For instance, although women are overrepresented among criminal justice professionals on prime-time television, they are stereotypically portrayed as young and sexually attractive, and are more likely to be victimized than
their male counterparts (DeTardo-Bora, 2009; Surette, 1992).
Unlike underrepresentation, the negative effects of women’s overrepresentation have
been overlooked. Yet, the media’s overrepresentation, like the underrepresentation of certain social groups, may skew perceptions and reinforce existing stereotypes that associated
with these groups (Glassner, 2010; Mastro, 2009). Collins (2011) has noted that merely
increasing women’s media representation without removing the stereotypical depictions
INFORMATION, COMMUNICATION & SOCIETY
5
may exacerbate the problematic effects. As limited empirical research has addressed
women’s overrepresentation in media, this study explores the overrepresentation of female
characters. We will examine whether overrepresentation constitutes a form of gender bias
and inequality in the digital age. If so, how? Also, what is digital media’s role?
3. Gender representation and social media in China
3.1. Gender-marking and stigma: how the Chinese language facilitates the
overrepresentation of women in media
Gender stereotypes and stigmas are pervasive in China’s public discourse, as in many other
societies. Indeed, in the Chinese context, merely adding the word ‘female’ to a regular term
sometimes shifts its meaning from neutral, or even positive, to derogatory. One example is
the so-called ‘female Ph.D.’ (nv boshi), which refers to well-educated and ‘undesirable’
from the patricentric perspective – women. In a society that still values women’s ‘purity’
and ‘innocence,’ females with a Ph.D. are viewed by many as intimidating to men (Deng,
2016). Other examples of such gender-marking include the ‘female supervisor’ (nv
shangsi),1 ‘female soccer fan’ (nv qiumi),2 and ‘female driver’ (nv siji). This pattern of
language usage is especially notable as an increasing number of Chinese women enter previously male-dominated fields, challenging the conventional gendered division of labor.
These circumstances may produce an overrepresentation of female characters based on
gender-marking and gender-based stigmatization.
3.2. Research questions
The discussion on traffic accidents provides a lens to examine cross-media gender representation. In 2012, China became the world’s largest newspaper market (The Economist,
2013). Since 2008, it has had the world’s largest population of internet users (Macfie,
2008). During the same period, the number of vehicles per capita in China increased
four-fold (U.S. Department of Energy, 2017), and the issue of traffic accidents and drivers’
gender has spawned vibrant discussions on both news and social media (Luo, 2015; Zhou
& Xiao, 2018). As in the United States a century ago (Berger, 1986), the image of ‘women
on wheels’ in today’s China also challenges the traditional cultural association between
cars and masculinity (Christensen, 2015). Thus, it offers a lens into how society reacts
to women’s growing presence in public domains previously dominated by men.
This study explores whether there is gender bias in China’s traditional news media and
social media, via either inequitable representation or gendered coverage. It also examines
how social media features have influenced gender-based discourse, and what this means
for the future of gender equality. Specifically, we ask the following research questions:
RQ1: Are women drivers overrepresented in coverage of traffic accidents?
RQ2: Does coverage differ between social media (Weibo) and traditional news media
(newspapers)?
RQ3: On social media, is the biased coverage more prevalent? How does it vary across user types?
RQ4: Can Weibo be the ‘new equalizer’ for gender that cultivates gender-aware discussion?
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M. LI AND Z. LUO
4. Data and methods
4.1. Data
We collected data from Weibo and print Chinese newspapers. To contrast the discussion
on female and male drivers in traffic accidents, we used two pairs of keywords, ‘female driver’ and ‘accident,’ and ‘male driver’ and ‘accident,’ to search for content published
between 1 January 2010, and 30 November 2018.
4.1.1 . Weibo data
To collect posts discussing drivers’ gender in traffic accidents, we searched the designed
keywords using Weibo’s internal search engine, Weibo Search (Weibo Sousuo), and collected all returned results. In total, we collected 97,120 Weibo posts, with female drivers
marked in 82,291, male drivers in 5417, and both in 9412. For each Weibo post, we collected both its text content and metadata.
In order to detect how gendered contents vary across Weibo user types, we categorized
Weibo content generators into six groups, based on the pre-defined user categories from
the Weibo verification system and user profiles: (1) Regular individual users are individual,
anonymous users who have neither Weibo membership (see below) nor verification. (2)
Advanced individual users are individuals who are more active on Weibo. This group
includes those verified as ‘Weibo Experts’ (Weibo daren) and ‘Weibo Members’ (Weibo
huiyuan). (3) Self-media (Zimeiti) are verified celebrities, social media influencers, and
independent news accounts that produce original content, but are not officially affiliated
with the authorities (Shepherd, 2018). (4) Media are accounts associated with a mass
media enterprise/news organization. (5) Police are accounts operated by national or
local police departments. (6) Verified organizations are accounts owned by verified entities
other than media and police. The first three are operated by individuals, and the latter
three represent institutions.
4.1.2. Newspaper data
Print newspaper articles were collected from China Digital Library, a comprehensive database that has digitized over five hundred Chinese newspapers. It covers mainstream publications from all of the major Chinese Newspaper Groups. To collect news articles
containing the designated keywords, we used the database’s search function and collected
all returned results. In total, 11,290 articles from 393 Chinese newspapers were collected:
2103 had only ‘male driver,’ 7809 had only ‘female driver,’ and 1378 mentioned both.
4.2. Methods
This study explores how newspaper and Weibo content generators discuss gender issues in
traffic accidents. We used a mixed-methods approach to analyze the data, which allowed
systematic analysis of a large corpus while incorporating human-centered and questiondriven interpretation of texts (Nelson, 2020). We started with quantitative analysis to
evaluate gender overrepresentation in newspapers and on Weibo. We then used computer-assisted text analysis to explore the relationship between gendered topics and media
platforms. Based on the analysis, we used quantitative methods to estimate the prevalence
of biased content on Weibo, and how it varies across user groups. Next, we conducted an
INFORMATION, COMMUNICATION & SOCIETY
7
interpretive analysis to understand the meaning-making process behind the discussion on
female drivers and sexism.
For computer-assisted text analysis, we used self-created R software to estimate the
Structural Topic Model (STM) to identify topics in the corpora. Topic modeling is a statistical method of natural language processing that ‘analyze(s) the words of the original texts
to discover the themes that run through them’ (Blei, 2012, p. 77). The advantage of topic
modeling over alternative means of text analysis is its ability to reveal topics that human
coders may miss in analyzing large corpora (Quinn, Monroe, Colaresi, Crespin, &
Radev, 2010). And STM can measure the correlation between topics and covariates (Wesslen, 2018). In this study, we conducted two separate STM analyses: one examines the correlation between discussion of sexism and media platforms, and the other examines that
between gender-aware discussion and gender-annotation patterns on Weibo. For each
STM test, we ran multiple topic models to determine the optimal topic number. We settled
with the 35-topic STM for the first analysis and the 30-topic STM for the second one, as
each provided the most interpretable topics with the least overlap (Nelson, 2020).
In the second STM, we examined gender-aware discussion in the Weibo posts and its
correlation with gender-annotation patterns. This analysis also helped us select posts by
topic and focus on those engaging in gender-aware discussion, so that we could explore
the meaning-making process and diversity in Weibo discussion. We used representative
documents identified by the STM under the topic of sexism (N = 905, topic proportion
> .375), in which actors had deliberated over what language is sexist and what is not,
and conducted a qualitative content analysis on these posts. This involved an interpretive
and inductive coding of the posts into clusters of similar narratives. The interpretive analysis allowed a deeper look into the variation in the discussion than could have been done
with automatic text analysis. We read the contents and developed five categories to summarize the meanings embedded. The two researchers then coded the contents independently, assigning each Weibo post to one category. The coder agreement coefficient was
0.8 – an excellent measure for our study (Neuendorf, 2016).
5. Findings
5.1. The overrepresentation of women drivers in traffic accident coverage
In China, female drivers are less likely to be involved in traffic accidents than their male
counterparts. Despite the absence of nation-level data, local statistics confirm that female
drivers’ accident rates are significantly lower than those of male drivers (Chen, 2018):
females accounted for about 30% of total drivers, but were responsible for less than
10% of traffic accidents (Qiu, 2015; Su, Ning, Chen, Guo, & Yu, 2015). However, the
findings presented in Figure 1 show that among newspaper coverage of traffic accidents
that annotated a single gender, 79% mentioned female drivers. This is 3.7 times the rate
for male drivers. The Weibo contents amplified this bias, as 94% of the posts on traffic
accidents mentioned female drivers. This was 15.2 times the rate for male drivers. In
short, though female drivers were less likely to be involved in traffic accidents, they
received disproportionately more attention from media. Thus, in relation to RQ1, both
China’s newspapers and its social media overrepresented female drivers in traffic accident
discourse.
8
M. LI AND Z. LUO
Figure 1. Gender ratio of registered drivers, traffic accident, and media coverage. Note: The gender
ratio of registered drivers and traffic accidents is based on statistics of Jiangsu Province in 2014.
While the ratio varies by province and year, the pattern that male drivers are the majority and more
likely to be involved in traffic accident remains the same.
5.2. Differences between Weibo and newspapers in representing female drivers
Figure 1 shows that women are more likely to be cited for traffic accidents than men, and
this pattern is more salient on social media than in newspapers. Do the two platforms
differ in terms of their content on female drivers? To test this, we fit a 35-topic STM to
all newspaper and Weibo texts to extract topics. Then, we tested the correlation between
these topics and media platforms. The results, presented in Figure 2, show that one topic
on Weibo, marked by the ‘X,’ directly addresses sexism;3 it contains both sexist remarks
and those disputing such remarks. The other topic associated with Weibo, labeled
‘lousy driver in high heels,’ consists of sexist remarks targeting female drivers. In contrast,
topics associated with the newspaper (topics a and b) focus on news stories, rather than
direct sexism.
Figure 2. Association between topics and platforms. Note: Labels are assigned by the authors based on
the assessment of top words and top documents of each topic. Besides topics on sexism, we also label
other topics that are platform-specific. Both topics a and c discuss specific cases of car accidents. Topic b
is about regulation on turn signal.
INFORMATION, COMMUNICATION & SOCIETY
9
An examination of the representative documents identified by the STM illustrates these
differences. Although newspapers sometimes mention drivers’ gender in coverage of traffic
accidents, they rarely attribute the accident to specific gendered features. Instead, gender is
used to describe persons of interest, both men and women. For instance, in a newspaper
article reporting a traffic accident, the journalist annotates the gender of multiple persons
involved, including a male bicyclist and a female driver:
[…] The bicyclist was a male around thirty-years-old, and he said he had tried to stop the
bicycle the second he saw someone falling down in front […] The same site had an accident
last week when an electric motorcycle tipped over, and the female driver had to hastily turn
the steering wheel to avoid hitting it […].
Nevertheless, this does not mean that newspaper content is free from gender bias against
women. Indeed, gender bias and stereotypes in the minds of newspaper journalists, and
the people they cover, can shape the storytelling. In an article on car maintenance, the
journalist reports what he has learned from auto shops:
[…] One common type of accident is caused by malfunctioning reverse parking sensors.
Many inexperienced drivers and female drivers are over-reliant on the reverse parking sensor
system. Therefore, under circumstances of electronic malfunction, they don’t know how to
control the vehicle properly, which may lead to accidents […].
In this case, female drivers, despite their varying levels of driving experience, are described
as a monolithic group, equivalent to inexperienced drivers. This biased gendered perception is reported as fact, echoing the stereotype that ‘women don’t know how to drive.’
Generally speaking, newspapers avoid overtly sexist language, even though journalists’
personal perceptions can infuse storytelling with pre-existing bias. In comparison, blunt
sexist messages can be produced and disseminated on Weibo. A Weibo post on a fourcar collision caused by a female driver states:
A female driver was driving on the wrong side of the highway when she demanded in a
coquettish voice, ‘Just let me through, will ya?’ Her action caused a four-car collision4 […].
This post not only annotates the driver’s gender, but also highlights her femininity and
sexuality by describing her voice as ‘coquettish.’ By portraying the image of a woman utilizing her sexual attractiveness to break traffic regulations, which led to a severe accident,
this post reinforces the connection between female drivers and their gendered/sexual features, in a negative light.
In relation to RQ2, our findings indicate that newspapers and Weibo differ in terms of
how they address the connection between female drivers’ gendered features and car
accidents.
5.3. The prevalence of gender bias on Weibo
The analysis above compares gender-annotation patterns and gendered content between
newspapers and Weibo. As it is known that content on Weibo is more likely to mention
females in traffic accidents and address sexism, the remainder of this section explores the
nuance in gender bias on Weibo.
Information published on social media like Weibo receives a quick response from the
audience. This feature enables evaluation of the impact of gender-annotation on
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M. LI AND Z. LUO
information dissemination. We used negative binomial regression to examine whether
female drivers’ posts receive more public exposure, as measured by repost frequency.
The results show that although stories about male drivers are rarer due to underrepresentation, Weibo users still show less interest, and are less likely to repost them. In comparison, content on female drivers is expected to receive 19% (b = .176, se = .0293, p = .000)
more reposts than content on male drivers.5
On social media, only a handful of the most popular posts can receive massive public
attention, whereas the rest are rarely reposted. Does gender-annotation influence the likelihood of a post becoming eye-catching? We took the 500 most reposted posts from each
gender-annotated group and compared the number of reposts they received. The median
number of reposts of male drivers’ posts (13.00) was statistically lower than that of female
drivers’ posts (279.50), U = 7367, z = −25.764, p < .000.6 These results not only confirm
that content on female drivers is more likely to be reposted, but also shows this trend is
more salient among the eye-catching posts. Among the fifty most reposted gender-annotated contents, which account for 21% of the total reposted content ever received, only two
(the 21st and 30th) were male-annotated. Moreover, the three most popular posts on
female drivers received more reposts than all of the posts on male drivers combined.
We also examined how the prevalence of biased contents varies across Weibo user
types. In relation to RQ3, Table 1 shows an association between user groups and gender-annotation. Compared to institutional users, the individual users had a lower percentage of female-annotation, with 84% for regular users, 76.5% for advanced users, and
87.6% for self-media. In contrast, the institutional users, especially the police department
Weibo accounts, were likely to mention female drivers in their coverage. Although acting
as the authentic information source for traffic accidents, police accounts have the highest
ratio of mentioning female drivers (91.5%). They are expected to be 2.5 times more likely
to note female drivers than the advanced individual users.
5.4. Gender-aware discussion on Weibo: can new media become the ‘new
equalizer’?
The previous analysis has shown that content directly addressing sexism is associated with
Weibo. This section explores the features and dynamics of this type of content. It also
examines the possibility of Weibo acting as the ‘new equalizer’ that cultivates genderTable 1. Chi-square and binary logistic regression of female annotation by Weibo user type.
Female annotation
Weibo user type
Individual users
Regular user (N = 45,445)
Advanced user (N = 14,078)
Self-media (N = 13,210)
Media (N = 8961)
Police (N = 6831)
Organization (N = 8595)
Chi-square
Percentage
B
SE
Exp(B)
84% (−6.3)
.26***
.04
1.30
76.5% (−29.5)
87.6% (9.8)
.21***
.05
1.24
Institutional users
88.5% (10.4)
.31***
.06
1.36
91.5% (16.2)
.92***
.08
2.50
88.7% (10.8)
.50***
.06
1.65
df
p
Summary statistics
Χ2
1605.229
10
<0.001
Notes: Adjusted residuals appear in parentheses below observed percentage. Reference category in the binary logistic
regression: Advanced User.
*p < .05; **p < .01; ***p < .001.
INFORMATION, COMMUNICATION & SOCIETY
11
aware discussion, in which users address gender differences or confront gender
discrimination.
We tested the correlation between types of gender annotation and the gender-aware
discussion by fitting a 30-topic STM to all of the gender-annotated Weibo texts. The
results, presented in Figure 3, show that the topic of sexism (marked by the ‘X’) is associated with bi-gender annotation. In other words, gender-aware discussion is more likely to
occur in those posts that annotate both female and male driver, rather than in those that
annotate only one gender.
However, the gender-aware discussion did not necessarily lead to an online cyber
environment conducive to gender equality, since such discussion may provoke both sexist
discourse and its counter-discourse. To further explore the diversity in the gender-aware
discussion, and to understand whether it could lead to pro-equality ideas, we conducted a
qualitative content analysis with the representative documents under the sexism topic in
the gender-annotation STM. The resulting five categories are presented in Table 2.
Among the five categories, three addressed sexism directly. Posts in the first category,
which account for 51% of the total posts, either criticized sexism or claimed female drivers
are not inferior to male drivers. As demonstrated in the following post, the posts in this
category criticize the public discourse double standards that have gendered deviant behavior. By juxtaposing male drivers alongside female drivers, it contrasts gender labels with
gender privileges:
Like the media’s coverage of female drivers’ accidents, rapes and assaults by male drivers have
also occurred several times, and have aroused national discussion. Yet, few have thought
‘men are unsuitable for driving’ – not even that ‘men are not suitable for driving Didi7.’
Figure 3. Association between topics and type of gender-annotation. Note: Labels are assigned by the
authors based on the assessment of top words and top documents of each topic. Besides the topic on
sexism, we also label other topics associated with a specific gender-annotation. Topic a is about accidents involving drivers of both genders. Topic b is about (reversed) sexist argument targeting male
drivers.
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M. LI AND Z. LUO
Table 2. Interpretive content analysis categories of topic on sexism (N = 905)
Category
1
2
3
4
5
Description
Frequency
Percentage
Against sexist argument or bias on gender-marking, directly addressing sexism or
claiming female drivers are not inferior to male drivers
Justifying sexist argument or claiming female drivers are inferior to male drivers
Mixed message, clearing female drivers’ names while reiterating certain gender
stereotypes, or claiming male and female are equal but different due to
(stereotypical) ‘gender features’
Show or discuss facts and statistics, no opinion or attitude related to gender
Report on single case/experience that involved both man and woman drivers, no
identifiable opinion, attitude or argument related to the gender of the drivers
462
51.0%
91
133
10.0%
14.8%
57
163
6.3%
18.0%
So, malignant behaviors are treated as individual cases in the agenda – this is a manifestation
of privilege8.
The second is a counter-feminist category that justifies discrimination by rephrasing gender bias as well-grounded ‘facts.’ It accounts for ten percent of the posts. In the example
below, the user denies gender discrimination ever existed by contending that it is a fact
that female drivers are less competent than male drivers:
Sometimes, it is not gender discrimination at all. Many people have noticed the rate of stupid
accidents among female drivers. When it comes to driving, with the same degree of driving
age and intelligence, I have never seen one female driver exceed male drivers in basic skills,
response, and knowledge of vehicles. In the social division of labor, feminists hope to achieve
gender equality in all aspects; obviously, this is hard to implement.
The last category, accounting for 15% of the posts, contains mixed messages that challenge
gender discrimination against women while reiterating existing gender stereotypes. As
exemplified in the post below, it admits the higher rate of major traffic violations
among male drivers than females. However, it echoes the stereotypical message in the
post above by associating drivers’ gender with driving ability.
[…] Are females born to be lousy drivers? Indeed, the rate of major traffic violations is lower
for female drivers, but it is true that inexperienced women drivers always make trouble, and it
is also true that female drivers’ response and judgment are insufficient.
The content analysis demonstrates diversity and complexity in gender-aware discussions,
as the feminist discourse competes with the sexist discourse. Meanwhile, a third discourse
mixes the two. In relation to RQ4, social media-based discussions engender confrontations
among competing discourses. This generates uncertainty over the direction of the public
discourse of gender.
6. Discussion
This analysis of China’s traditional and social media content suggests that female drivers
have been overrepresented in the public discussion of traffic accidents. This pattern is
more salient on Weibo than in newspapers; Weibo users are more likely to attribute accidents to female drivers’ gender. Content about female drivers also receives more exposure.
Meanwhile, we found that social media provides a platform for gender-aware discussion,
although the direction of such discussion is unclear.
This study has several limitations. Firstly, the data might not include all gender-biased
and female-stigmatized content. This is because it only captures newspaper articles and
INFORMATION, COMMUNICATION & SOCIETY
13
Weibo posts that contain ‘female driver’ or ‘male driver.’ Further research could consider
additional linguistic variations. Secondly, by focusing on the visibility and overrepresentation of female drivers, this study might not have captured alternative pathways for gender
bias and gender-based stigmatization. Future research could explore the invisibility and
underrepresentation of female subjects in different settings. Thirdly, this cross-platform
comparison did not examine the specific media features that may change the contents’
overall influence. For example, the newspaper layout may give the articles different
‘weight.’ Furthermore, the comments a Weibo post receives, which may contain contradictory opinions, may redirect the discussion of the original post.
Despite the limitations, our study provides substantive evidence to the debate on social
media’s role in gender equality. It shows that social media, rather than newspapers, are a
hotbed for gender stigmatization. One explanation is that the quick response cycle and the
motivation to increase click-through rates on social media encourages content generators
to release controversial, eye-catching content. This often entails offensive and/or insulting
language. Another explanation is that newspaper journalism is restricted by China’s regulatory regime which maintains strict control over print media. Xinhua News Agency, China’s state-run press, regularly releases lists of words prohibited in newspapers. This
includes epithets targeting gender groups (People’s Daily Online, 2015). It is likely that
this blacklist reduces gender-marking in newspapers. On social media, where posts are
regulated neither to the same extent nor by the same criteria, gender-marking for
women may go unleashed. However, it is beyond the scope of this study to systematically
examine the mechanisms of content generation and distribution that shape cross-media
divergence on gender equality. One line of future research could be a cross-platform comparison to advance the understanding of these mechanisms, and go beyond the case of
China.
This study challenges the arguments that female underrepresentation reflects women’s
presence in the real world. Moreover, since China’s newspaper journalists and Weibo
users have balanced sex ratios (Sina Weibo Data Center, 2017; Zhang, Zhang, & Lin,
2014), it is difficult to attribute media gender bias to the gender composition of content
producers. Further research could consider how broader social and cultural contexts
shape gender bias in media. This study also confirms that female overrepresentation
has perpetuated pre-existing gender biases. Indeed, such biased representation has already
been embedded in the Weibo search engine, which regarded ‘female driver’ as a single
phrase, but identified ‘male driver’ as two separate words. It is unlikely that this phenomenon is unique to Chinese social media, as demonstrated by emerging literature on ‘algorithm oppression.’ However, future research is needed to compare how different algorithms
influence the representation – including underrepresentation and overrepresentation – of
women.
Notes
1. Perceived as control-freaks.
2. Perceived as ignorant of basic soccer rules and desperate for a male partner’s ‘Soccer 101’
lecture.
3. The five top-weighted documents on the sexism topic are presented in Table 1 in the Supplementary Materials.
14
M. LI AND Z. LUO
4. We were unable to fact check this accident, though we doubt the likelihood that a demand
made ‘in a coquettish voice’ could have been heard by others on the highway.
5. The result of negative binomial regression model is presented in Table 2 in the Supplementary Materials.
6. The distribution of reposted content was similar for male and female drivers, as assessed by
visual inspection. The five hundred most reposted posts from each gender-annotated group
and the number of reposts they received is presented in Figure 1 in the Supplementary
Materials.
7. Didi is a Chinese ride-sharing app similar to Uber. This post refers to a wave of news stories
in which male Didi drivers had injured, raped, and in some cases even murdered, female
passengers.
8. In the original post, the word ‘privilege’ appears in English.
Acknowledgements
We thank Dr. Kate Averett, Dr. Glenn Deane, Dr. Brandon Gorman, Dr. Ronald N. Jacobs, fellows
at the China Study Group in the Sociology Department at the University at Albany, and two anonymous reviewers for their comments. The research was supported by the Benevolent Association
Research and Creative Activity Grant, and the Karen R. Hitchcock New Frontiers Fund Award
from the University at Albany, State University of New York.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Muyang Li is a Ph.D. candidate of sociology at the University at Albany – State University of
New York. Her research interests include cultural sociology, computational social science, civil
society and democracy, and new media [twitter: twitter.com/muyangli_soc, email: mli2@albany.
edu].
Zhifan Luo is a Ph.D. candidate of sociology at the University at Albany – State University of
New York. Her research interests include political sociology, computational social science, political
discourse, social media, and misinformation campaign. Her works have been published in the Journal of World-Systems Research [twitter: twitter.com/zhifan_luo].
ORCID
Muyang Li
http://orcid.org/0000-0003-2522-4601
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