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3 The Consumer: Psychological Mechanisms Underlying Potential Misattributions

3 The Consumer: Psychological Mechanisms Underlying Potential Misattributions

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der. These forms of discrimination have received significant attention from lawmakers, researchers, human resource managers, civil rights activists, and the public. This

continued attention is justifiable, as researchers have supported that these forms of

discrimination are evident in organizations (Budig, 2002; Schneider et al., 1997;

Wittenbrink et al., 1997). Researchers are beginning to explore biases as antecedents

to intolerance and discrimination against other less commonly studied target groups.

Evidence also supports the notion that discriminatory practices exist in the present

day based on age (e.g., Finkelstein & Burke, 1998; Perry & Finkelstein, 1999),

sexual orientation (e.g., Clain & Leppel, 2001; Hebl, Foster, Mannix, & Dovidio,

2002), level of physical attractiveness (and/or unattractiveness) (e.g., Hosoda, StoneRomero, & Coats, 2003; Stone, Stone, & Dipboye, 1992), and disability status

(e.g., Ravaud, Madiot, & Ville, 1992; Vaughn, Thomas, & Doyle, 2011).

Social and cognitive psychology research has extensively focused on attitudes,

stereotypes, and prejudices, all psychological constructs believed to be vulnerable

to serving as antecedents to discriminatory practices. An attitude can be defined as

“a psychological tendency that is expressed by evaluating a particular entity with

some degree of favor or disfavor” (Eagly & Chaiken, 1993, p. 1). As defined by

psychological research, attitudes typically involve some affective component. It is

well established that an individual’s attitude toward a target object or concept is a

possible determinant of behavior toward that object or concept (Brehm, Kassin, &

Fein, 2005; Kraus, 1995). Stereotypes are defined as generalized psychological representations of the characteristics shared by members of a particular group

(McGarty, Yzerbyt, & Spears, 2002). Stereotypes pertain to inherent or learned

beliefs about a certain group, while attitudes refer to the affective components or

reactions to a group. Lastly, prejudice is most commonly defined within the academic literature as pertaining to hostile or negative attitude toward, or a negative

evaluation of, a particular group (Greenwald & Pettigrew, 2014). Collectively, we

refer to attitudes, stereotypes, and prejudices as biases moving forward.



15.3.2



Potential Gap in the Existing Workplace Discrimination

Literature



Where we see a potential gap in the existing research is how discrimination relates

to workplace decisions based on the use of SNSs in hiring and recruitment contexts.

Social media and SNS use by organizations may be impacted by the biases previously discussed, resulting in intentional or unintentional discrimination.

Researchers have noted that under certain conditions, biases might strongly

influence subsequent behavior (Antonak & Livneh, 1988). For example, in cases in

which the holder of a given bias is presented with the opportunity to act with fewer

normative constraints, the actor is more inclined toward behaving in a manor

congruent with the attitude held (Fazio & Williams, 1986). In these weaker environmental conditions, more subjectivity and discretion is left to the individual. As an

example within the area of human resources, highly structured interviews can



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mitigate the manifestation of discriminatory behaviors as little room is left for

subjectivity in interpretation or discretion in judgment on the part of the interviewer

(Heilman & Haynes, 2008). In contrast, when a consumer is presented with varying

levels of information without strong environmental cues to direct appropriate use of

said information, this unstructured environment may foster outcomes that are more

heavily influenced by the consumer’s personal attitudes and stereotypes (Greenwald

& Pettigrew, 2014).

In thinking about a typical context in which a recruiter or hiring manager

performs a search of candidates’ SNS presence, this online environment presents a

prime opportunity for a more subjective context, as these searches are often done

informally, in private, and without any formal documentation, and often with few,

if any, organizational policies in place to guide or direct practice.

Further, empirical research is just beginning to form around how SNSs are

associated with potentially work-related constructs, such as the Big 5 personality

characteristics (Măgurean, Vỵrgă, & Sava, 2014). With such limited available

research, it is perhaps even less likely that practitioners have data backed resources

or guidelines available which they can use to make informed decisions based on

best practices. In addition, although attitude, stereotype, and prejudice research has

a long-standing history, measurement techniques have become increasingly complex

theoretically (Antonak & Livneh, 2000), further challenging the forward progress

of empirical research on the effect of using SNSs for hiring purposes.

Individuals’ attitudes and the composite organizational and societal climate that

these attitudes produce are barriers linked to discriminatory behavior (Antonak &

Livneh, 1988; Colella & Stone, 2005). When considering these barriers, it is important to evaluate both explicit and implicit biases which may differentially impact

consumer interpretations of SNS information.



15.3.3



Explicit Bias



Since the Civil Rights Act (CRA) of 1964, a societal revolution has taken place in

the United States, with overt and explicit forms of discrimination steadily declining.

We have also seen a significant decline in overt discrimination by employers due to

the CRA and its Title VII mandate against selection procedures that result in disparate impact on, or disproportionally impacting members of, a protected class (race,

color, religion, sex, or national origin). This important mandate means that it is

imperative employers or organizations treat all people fairly, not providing any

advantages to one group over another, protected group. However, it is well known

that explicit biases still exist in today’s society and in our workplaces.

The potential for explicit biases to negatively impact certain demographic groups

was the initial reason for the implementation of the Uniform Guidelines on

Employee Selection Procedures (Equal Employment Opportunity Commission,

1978), a set of guidelines focused on ensuring the fair treatment of certain protected

demographic classes during the employment application process. As research has



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shown, both implicit and explicit biases can impact the hiring process, but the courts

have been much harsher when discrimination is due to more blatantly explicit

discrimination, originating from more overt stereotypes (Swim, Scott, Sechrist,

Campbell, & Stangor, 2003).



15.3.4



Types of Explicit Bias and Their Implications

for Selection



Explicit, or overt, biases against members of certain demographic groups can take

many different forms. While not all explicit biases are hostile in nature, all do hold

the potential to unfairly impact a job candidate.

In order for intergroup conflict, the source of explicit and implicit biases, to

occur, people must perceive themselves and others as members of distinct groups

(Fiske & Taylor, 2013). This occurs at both a conscious and subconscious level,

leading to the development of numerous group identity theories in the social and

cognitive psychology literatures, including: Social Identity Theory (Tajfel, 1981),

Self-categorization Theory (Turner, 1985), and Optimal Distinctiveness Theory

(Brewer, 1991), among others.

Once people have created their perceptions of group memberships, there are

various ways in which group membership can lead to explicit biases against members of other groups. These explicit biases can be either more cognitively loaded

stereotypes, or more emotionally loaded prejudice (Fiske & Taylor, 2013). While

both types of explicit biases can drive discriminatory practices, the emotionally

loaded prejudices have been found to drive discrimination more than cognitively

loaded stereotypes (Dovidio, Brigham, Johnson, & Gaertner, 1996; Talaska, Fiske,

& Chaiken, 2008, Tropp & Pettigrew, 2005).

Explicit stereotypes are cognitively loaded and can be extremely impactful in the

hiring process. It is currently estimated that about 10 % of people hold extreme,

blatant stereotypes (Fiske & Taylor, 2013). While 10 % of the population seems like

a relatively low percentage, as Fiske and Taylor (2007) mention, this is an extremely

impactful 10 %. These biases can become increasingly dangerous when they result

from competition for scarce resources, such as jobs. Moreover, people typically

believe others have stronger biases against out-groups than they themselves do,

which can be especially troublesome for those who do hold blatant stereotypes

against specific demographic groups. Affective prejudices, on the other hand, can

potentially drive discrimination in hiring practices more because they are more

immediate, often stronger, and can be less easily noticed by the person holding the

affective bias.

Confirmation bias is one form of bias that can impact the ways a decision maker

leverages SNSs during the selection process (Snyder & Swann, 1978). Confirmation

bias is the tendency for people to search for information that confirms their preexisting beliefs. This type of bias becomes potentially detrimental if hiring managers

hold negative preexisting beliefs regarding members of certain protected classes.



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The kind of SNSs leveraged during the hiring process may have an impact on the

extent to which confirmation bias plays a role. With the sheer amount of information available to consume on certain SNSs, such as Facebook, explicit biases have

the potential to be much more impactful than they do on other SNSs, such as

LinkedIn, that are strictly for professional networking. If an ill-meaning decision

maker is using an SNS with a plethora of information presented, then it is a simple

task to find information that would confirm negative stereotypes about a job

applicant.

Confirmation bias can also benefit members of a decision maker’s in-group. If a

decision maker positively views a candidate based on demographic group membership, then it stands to reason the decision maker will seek out information to confirm

positive biases he or she may hold regarding that job applicant. This shows the

double-edged danger of explicit biases acting through confirmation bias to negatively

impact certain demographic groups.



15.3.5



Implications of Explicit Biases on Cyber-Vetting



When leveraging SNSs in the hiring process, we must consider the more overt

beliefs regarding races that SNS consumers may hold. Although explicit biases are

likely to be less pervasive than implicit biases, they hold the power to be much more

impactful. If even one decision maker within an organization holds explicit

discriminatory beliefs regarding a specific group, then the potential ramifications

could very easily lead to litigation against the organization for discriminatory hiring

practices.

Although laws have been passed, procedures are in place, and court cases have

been won in the effort to eliminate acted-upon bias in organizations, members of

protected classes, including race, gender, and age, continue to be targets of explicit

biases. When thinking of stereotypes and prejudice, race is generally the first defining characteristic that comes to mind. Indeed, racial prejudice has been studied by

social psychologists since the 1920s (e.g., Allport, 1935; Thurstone, 1928).

Numerous findings have stemmed from this research, and typically center around

in-group versus out-group thinking that influences behaviors and actions. Racial

biases have been a continual issue in employee selection and while holding strong,

negative explicit biases based on race may be declining in modern times, the use of

SNSs may offer a low-structure environment through which explicit racial biases

can be engaged and acted upon to manifest in the form of intentional discrimination.

However, it is important to note that many of the issues arising with race biases are

due to less overt or intentional mechanisms as detailed later in this chapter, or as a

result of disparate impact resulting from employment testing and procedures, rather

than manifestations of strong, negative explicit biases.

In addition to racial biases, gender biases have a significant impact on hiring

practices. Gender bias is thought to be an important antecedent in explaining males

holding more positions of power in both business and government (Chen, Vanek,



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Lund, & Heintz, 2005). Research has found that both cognitive and emotional

explicit biases can result in gender discrimination when utilizing SNSs during the

hiring process.

One discriminatory and enduring stereotype that continues to impact females in

the workplace is the notion that they will have difficulty balancing work and family.

It is still all too common for interviewers to ask the inappropriate, and illegal, question of whether or not a female candidate will be able to handle long hours if she has

children. This is an example of something that could plague female job candidates

if an employer uses SNSs, such as Facebook, to cyber-vet a female applicant and

finds pictures indicating she has young children.

Other examples of the negative impact of explicit biases on gender discrimination during the hiring process stem from common stereotypes. For example, leadership is more congruous with stereotypically masculine behaviors and the male

gender, than with stereotypically feminine behaviors and females. Moreover,

leadership behaviors exhibited by males are viewed more favorably than those

exhibited by females (Eagly & Karau, 2002). This could, for example, result in

hiring managers, depending on the explicit gender biases they hold toward one

gender, whom make gender-based assumptions about a candidate’s skills and

experiences outlined on SNSs like LinkedIn.

Age-related biases and discrimination in employment contexts led to the development of the Age Discrimination in Employment Act (ADEA; 1967). Age is an

interesting demographic categorization in that the utilization of SNSs in the hiring

process could potentially have a negative impact on both younger and older adults,

depending on the context. Take, for example, a job opening for a leadership role.

If two candidates are similarly qualified in terms of their resumes, but one looks a

bit older in their SNS profile picture, and more mature in the eyes of the decision

maker, then this could be a deciding factor that is non-job relevant in nature.

Conversely, meta-analyses have shown that older adults are often categorized

through the lenses of a variety of negative stereotypes (Kite & Johnson, 1988).

Interestingly, Cuddy and Fiske (2002) found that people often have feelings of

condescension and compassion for older adults. Indeed, other researchers have

found older adults to be perceived as socially, cognitively, and physically incompetent (Nelson, 2002).

All of these potential biases against different age groups become significantly

confounded when considered in light of research showing that different age groups

use SNSs differently. Information posted by older and younger job applicants is likely

to be different, leading to more freedom of interpretation by the SNS consumer.

In addition to classes protected under federal employment laws, explicit biases

present in selection procedures also affect unprotected classes such as sexual orientation. Currently, there is no national equal employment law regarding the treatment of

job applicants based on sexual orientation, though individual states have begun implementing equal employment acts pertaining specifically to sexual orientation. These

state laws regarding discrimination based on sexual orientation may impact selection

practices which rely on SNS screenings in several ways. Numerous SNSs allow users

to join virtual groups which identify its members based on sexual orientation.



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Additionally, some SNSs allow users to publically post their sexual orientation in the

form of declaring the gender to which they are attracted. An SNS user’s sexual

orientation can be discovered in numerous other ways as well, such as in personal

posts and pictures. The selection decisions of a hiring manager biased against

individuals with a particular sexual orientation may easily be influenced should that

hiring manager see a candidate’s group membership or profile information identifying

that candidate’s sexual orientation. While it is conceivable that a biased hiring manager could learn the sexual orientation of a candidate screened using more traditional

methods, we believe that the use of SNSs to screen applicants makes this event much

more likely to occur. Although in many states this is not technically an illegal criterion

to use in removing a candidate from consideration in the hiring process, it is an important area of consideration when discussing potential explicit biases that can unfairly

disadvantage job candidates with different sexual orientations.

While the Uniform Guidelines long predate the development of SNSs and their

subsequent usage in making hiring decisions, they can still lend guidance to practitioners regarding potentially troublesome issues in utilizing SNSs in selection

decisions. Explicit biases regarding specific demographic groups still exist today,

and the proliferation of potential information regarding a job candidate’s membership in a variety of demographic groups should give pause to practitioners considering leveraging SNSs to cyber-vet candidates for employment.

While common explicit biases pertaining to specific demographic classes are

discussed in this chapter, an exhaustive discussion of the legal ramifications of

utilizing SNSs in selection is beyond the scope of our discussion. For a more

in-depth understanding of the potential legal issues surrounding the utilization of

SNSs in selection decisions, please see Chap. 14.



15.3.6



Implicit Bias



Title VII under the CRA of 1964 helped drastically decrease overt and hostile

discriminatory practices toward members of protected classes. Although explicit

biases are much less common today, individuals still make harmful implicit

associations. Implicit biases and implicit associations refer to associations or evaluations that one makes without having knowledge of making them. Research in this

area suggests that many people may engage in some form of discrimination due to

held implicit associations without necessarily having the intent or awareness of

doing so (Greenwald, Banaji, & Nosek, 2015; Greenwald, Poehlman, Uhlmann,

& Banaji, 2009). Over the course of the past two decades, the study of implicit bias

measurement has increased substantially (Antonak & Livneh, 2000; Greenwald

et al., 2015; Greenwald, McGhee, & Schwartz, 1998; Greenwald, Nosek, & Banaji,

2003; Nosek & Banaji, 2001).

Implicit bias research draws heavily from literature on social and cognitive

memory, and many of the most commonly used measures of implicit bias involve

individuals making rapid classifications of stimuli representing two contrasted



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categories (Greenwald & Pettigrew, 2014). Greenwald and his colleagues

(e.g., Greenwald et al., 1998, 2003; Greenwald & Banaji, 1995) provided much of

the framework for the most commonly adopted measure of implicit associations

(Fazio & Olson, 2003), the implicit association test (IAT) (Greenwald et al., 1998).

Greenwald and Banaji (1995) described implicit measures as concerned with

measuring the more automatic, and sometimes unconscious, associations that

individuals hold. In their review of implicit measures, Fazio and Olson (2003)

suggest that implicit measures “provide estimates of individuals’ attitudes without

our having to directly ask them for such information (p. 303).”

The IAT is a latency-based measure which operates on the premise that it will be

cognitively less difficult for an individual to match two congruent ideas compared

to two incongruent ideas. Introspective access to the associations is believed to be

prevented by having participants respond to the task in as timely a fashion as

possible while still eliciting the correct responses. Thus, the techniques “automatic

evaluation” component is derived from the prevention of introspective access to the

association strength being measured. Since the measure’s introduction (Greenwald

et al., 1998), the IAT has been used to classify various forms of implicit cognitions

beyond implicit attitudes such as self-esteem, stereotypes, political views, and

consumer preferences (Greenwald et al., 2009; Hofmann, Gawronski, Gschwendner,

Le, & Schmitt, 2005). The ability to adapt the IAT for a variety of target groups is

due to the flexibility of its methodology.

Researchers refer to implicit bias scores from measures such as the IAT to be

relative scores of favorability toward one group in comparison to the other (e.g.,

Greenwald & Pettigrew, 2014). Further, it is believed that these more automatic and

unconscious implicit cognitions feed into the higher levels of consciously controlled

and mediated explicit cognitions.

More recently, researchers have turned attention to discriminatory behaviors as a

result of favoritism toward the in-group, rather than necessitating hostility toward

an out-group (Greenwald & Pettigrew, 2014). While not a completely new idea,

attitude and stereotype researchers have increased attention to the impact of ingroup favoritism as an important predecessor of discriminatory practices, much like

out-group hostility (Greenwald & Pettigrew, 2014). Thus, discrimination may not

only result from intentional hostility or malice toward a member of an out-group,

but may also result from differential favoring and support for members of one’s own

group.



15.3.7



How Implicit Bias Can Affect SNS Consumers



The process of receiving and reacting to visual context in making judgements and

evaluations about a prospective candidate based on demographic features is more

easily enabled and happens much sooner via information presented on many SNSs

compared to more traditional staffing procedures (e.g., determining who to invite

for a first round interview on the basis of a traditional resume). Implicit processes



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based on demographic context (disability status, religious affiliation, age, sexual

orientation, community groups, etc.) have been shown to impact conscious

judgments about a person and can be powerful as the basis for more deliberate or

intentional judgment and action.

As an example, two separately conducted meta-analyses on Black-White racial

attitudes and stereotypes have found evidence of predictive validity correlations and

racially discriminatory behavior (Greenwald et al., 2009; Oswald, Mitchell, Blanton,

Jaccard, & Tetlock, 2013). While the effects found in these studies by Greenwald

et al. (2009) and Oswald et al. (2013) were modest (r = .236 and r = .117, respectively), Greenwald et al. (2015) have argued that the magnitude of the relationships

found in these sets of studies are societally impactful due to both the pure volume

of individuals affected at one time, and the fact that these biases can repeatedly

affect one individual over time.

Making matters more difficult, individuals with implicit biases that result in

intergroup favoritism often do not believe their actions are discriminatory, as

in-group favoritism is often perceived to be legitimate, normative, and procedurally

fair (Greenwald & Pettigrew, 2014). These shifts in focus toward examining the

impact of in-group favoritism are not only being acknowledged within the halls of

the ivory towers, but many popular press outlets have also highlighted this phenomenon in organizational settings. For example, Forbes featured an article highlighting

empirical research which suggested hiring managers at professional firms (lawyers,

investment bankers, and consultants) would weight factors such as a candidate’s

perceived similarity to them in relation to experiences, leisure pursuits, and

self-presentation styles over other qualifications such as evidence of productivity

potential (Adams, 2012). This finding is in line with much of the historical research

conducted in the area of similarity increasing or enhancing liking (Byrne, 1961;

Byrne, Ervin, & Lamberth, 1970). Byrne and colleagues established and demonstrated that the similarity-attraction principle is robust, and can apply to various

types of similarities including attitudes, personality traits, behaviors, beliefs, and

values (Byrne, 1961; Byrne et al., 1970). As this applies to an SNS context, the

similarity-attraction principle has been presented as an explanation for findings in

workplace contexts where evaluations were found to be more favorable when the

evaluator (e.g., hiring manager) and the evaluatee (e.g., candidate) are of the same

race and gender (e.g., Riordan, 2000).

Researchers suggest that it is difficult to prevent unintended discrimination due

to implicit biases or associations, but there are some things that one can do to lessen

the impact of implicit bias in the context of SNS usage (Devine, Forscher, Austin,

& Cox, 2012). As an example, Devine et al. (2012) demonstrated in a longitudinal

study of Black-White implicit race bias, that biases could be dramatically reduced

in a sample of non-Black participants via the use of a habit-breaking intervention

which included both education and training components. Within a 45-min training

session, participants in Devine et al.’s (2012) intervention group were first informed

about the pervasiveness and impact of biases, and then viewed five common

strategies, culled from the literature, that could be used in everyday situations to

counteract the implicit biases formed. These authors found that those participating



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in the training intervention group reduced their levels of implicit bias in the BlackWhite race IAT in comparison to the control group. In addition, those participants

receiving the intervention showed a sustained change in implicit bias over a period

of 2 months.

Further, as the development and use of the structured interview process has

enhanced the validity of the traditional interview process, and as rater training has

been shown to reduce bias in the performance appraisal process, one recommendation posited herein for those using SNSs in selection and staffing functions would

be to study, identify, and train end users on the elements of SNS content that have

been found to be of most value in predicting future job success (i.e., focus attention

toward the job-relevant information). This training would be based on applied

research which links certain types of SNS content to subsequent job success in various roles. This recommendation is predicated on continued advancement of applied

research in this area, expanding our knowledge of the most effective information

gleaned from SNS in the prediction of job success. As an example, Back et al.

(2010) evidenced convergence between actual personality and ratings of personality provided by consumers of users’ SNS profiles on four of the Big 5 dimensions

of personality (no relationship was found with emotional stability). The magnitude

of the aggregated independent ratings were moderate, with extraversion and

openness being most strongly predicted (Back et al., 2010).

In addition to identifying and sharing training and implementing policies that

coach hiring managers and recruiters on the types of information that should be

attended to when reviewing SNS profiles, awareness, and diversity training covering implicit associations, such as that conducted in the intervention used in Devine

et al.’s (2012) research described earlier, may be beneficial. The manifestation of

commonly held automatic associations into conscious discriminatory behaviors or

actions may be decreased for hiring managers and recruiters with increased awareness of their presence, and conscious efforts and strategies to deploy to reduce their

impact on decision-making. Various demonstrations related to the impact of implicit

associations are available online, including the ability to take different versions of

IATs for free, and many other valuable resources available at the Project Implicit

website (https://implicit.harvard.edu/implicit/).



15.4



The Producer and Consumer: How Biases Can

Simultaneously Affect Both and Potential Solutions

for Decreasing Bias



As with more traditional application process materials, certain applicant information available through SNSs may allude to applicants’ membership to specific

groups (Smith & Kitter, 2010). For example, a hiring manager may look at the name

Thomas on a resume and can safely assume the candidate is male. However, if the

name on the resume was something more gender neutral like Taylor, the hiring

manager would likely not be able to determine the gender of the candidate. If that



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same hiring manager were to look Taylor up on social media or an SNS, there

would likely be several more obvious clues pointing to the gender of Taylor, such

as pictures, family posts, or even the gender label itself.

The following lists provide a visual for the additional information employers or

organizations may be able to glean from a candidate’s SNS profiles during the

screening process, above that provided in application materials prior to face-to-face

contact.

• Information Available in Traditional Screening Methods:

















Name

Pronouns used in resumes, cover letters, and letters of recommendation

Activities list

Scholarships

Year of graduation/degree earned

Years of experience

Work history



• Additional Available Information on Social Media/SNSs:

















Pictures

Birthdate

Family/children

Membership to specific groups

Relationship status

Sexual Orientation

Group-related posts



We believe there is greater potential for individual biases to impact the selection

process when hiring managers have access to candidates’ more personal information on SNSs. Among the information readily available to hiring managers on SNSs

are group-related posts, pictures, and comments that may lead to assumptions about

group membership, support of a particular group, or provoke personal biases. SNS

users often demonstrate their support for social, economic, political, or environmental causes by adopting common publically displayed symbols on their SNS

profiles. For example, in June of 2015, following the US Supreme Court’s ruling in

Obergefell v. Hodges, that state-level bans on same-sex marriage were unconstitutional, over one million Facebook users applied a rainbow-colored filter to their

individual profile pictures as a show of support. This visible show of support could

have negative ramifications for job applicants if hiring managers with particular

sexual orientation biases access applicants’ SNS information.

Also in 2015, the #HeForShe campaign was launched, aiming to inspire men to

become advocates for ending the inequalities females face throughout the world. This

campaign sparked heated discussions on social media about gender equality, feminism,

and male superiority globally. If a social media or SNS user partook in these public

discussions or used the hashtag for the campaign, #HeForShe, they may have been

subject to gender or other biases of employers examining their online presence.



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