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3 Social Media Data, Analytics, Recruiting, and Selection

3 Social Media Data, Analytics, Recruiting, and Selection

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7 Social Media, Big Data, and Employment Decisions: Mo’ Data, Mo’ Problems?


Some companies use a more mature analytic process and only pull data from one

social media source (e.g., Facebook or LinkedIn). Other technology recruiting companies combine information from multiple SNS and online sources (Axelson, 2014;

O’Brien, 2014; Waxer, 2013). For example, many companies scan multiple social

media profiles, in some cases up to 50 profiles on different sites, and use descriptive

analytics to compile a “super-profile” for thousands of job candidates. These “superprofiles” contain information such as individuals’ job titles, skills, experience, and

contact information and psychographic information such as attitudes and interests

(Waxer, 2013). These companies then work with organizations and use diagnostic

analytics to develop “fit” scores identify candidates with that have the background

suitable to open positions and similar attributes to the current employees (Axelson,

2014; O’Brien, 2014). Other recruiting companies go a step further and use descriptive, diagnostic, and predictive analytics to examine over 70 variables from 300

million social profiles and flag candidates that are likely to change jobs in the next

3 months (Waxer, 2013). In analyzing this vast amount of information, these technology companies use prescriptive analytics to help automate the decision-making

process and identify the best possible candidates for a job opening.

It is easy to see why organizations are beginning to embrace this use of big data,

analytics, and social media to assist in their recruiting. The combination of these

technologies and methods allow organizations to recruit from a wider applicant

pool. Some technology and recruiting companies even explicitly promote the ability

to recruit a diverse applicant pool and help employers increase their diversity. Once

organizations have developed this rich applicant pool through automated recruiting

techniques, social media data and analytics can also be used to help streamline and

improve the selection process.


Social Media and Selection

Organizations are also turning to information posted by users on social media to

help make better hiring decisions. It is well known that job candidates engage in a

series of impression management techniques during the selection process. Research

has shown that the use of different impression management techniques can have an

influence on employment decisions and hiring recommendations (e.g., Kacmar,

Delery, & Ferris, 1992; Rosenfeld, 1997). Thus, getting to know the “real” person

is desirable when attempting to assess the extent to which an applicant will fit with

the job and/or the company’s organization culture. This is where hiring managers

and some human resource professionals believe that information gleaned from

social media can provide an enhanced and unfiltered look at individuals’ background, personality, expertise, and attitude (e.g., Florentine, 2014; Urshel, 2012).

One of the larger concerns about personality and its use in selection revolves

around the “fake-ability” of personality measures. When applying for a job, applicants may be highly aware of the organization’s expectations for its employees and

intentionally adjust their responses to be more favorable in the eyes of the


S. Guilfoyle et al.

organization in an effort to enhance their chances of being hired (Heggestad,

Morrison, Reeve, & McCloy, 2006; Mueller-Hanson, Heggestad, & Thornton III,

2003; Ones & Viswesvaran, 1998). To offset these concerns, organizations are looking for alternative personality measures to confirm, or even replace, self-report personality questionnaires (e.g., measuring personality in a job interview; Van

Iddekinge, Raymark, & Roth, 2005). One potential way organizations have found to

gain additional information about job applicants’ personality is through their surveillance of applicants’ social media pages.

In essence, using social media information to assess individual attributes is simply an extension of previous work examining the validity of rating such attributes

based on behavioral artifacts that require personal knowledge of, or interaction

with, the person being rated. For example, observer ratings of personality have been

obtained based on photographs (Naumann, Vazire, Rentfrow, & Gosling, 2009;

Vazire, Naumann, Rentfrow, & Gosling, 2008), living and working spaces (Gosling,

Ko, Mannarelli, & Morris, 2002), personal possessions (Burroughs, Drews, &

Hallman, 1991), creative writing samples (Küfner, Back, Nestler, & Egloff, 2010),

self-selected e-mail addresses (Back, Schmukle, & Egloff, 2008), e-mail messages

(Gill, Oberlander, & Austin, 2006), visual online representations of the self (a.k.a.

avatars; Bélisle & Bodur, 2010), and personal websites (Marcus, Machilek, &

Schütz, 2006; Vazire & Gasling, 2004). These ratings have been shown to be reliable and to significantly correlate with self-report measures of personality. Behaviors

on social media can be thought of as the same type of “behavioral residue” that is

left behind in other situations. However, unlike the other situations in which this

“residue” has been in observed, information on social media covers a wider variety

of behaviors and is continually updated.

This unfettered and continual self-disclosure on social media has led many

human resource professional to believe that social media may allow for accurate

personality inference. This belief comes from the generally accepted premise that

people project a more polished and positive portrait of themselves shine in the strong

and more “professional” situations inherent to the selection process (e.g., interviews, resumes, self-rated assessments), but let the let their “real” persona out on

“personal” SNS, such as Facebook or Twitter (Urshel, 2012). Indeed, some argue

that information on social media, even information on “professional” SNS such as

LinkedIn, accurately reflects the “real” person because users’ social media network

connections can challenge and/or correct inaccurate information (Davison, Maraist,

& Bing, 2011) and because putting out fake information on social media run counter

to the basic purpose of social media platforms (Kluemper, Rosen, & Mossholder,

2012). This perspective is supported by research showing that ratings of personality

gleaned from users’ social media profiles more closely aligned with the user’s actual

personality than with the user’s self-reported “ideal” version of themselves (Back

et al., 2010). Other studies have shown that users of “personal” SNS tend to engage

in an unusually high level of self-disclosure (Naaman, Boase, & Lai, 2010).

It is easy to see why some human resource professionals believe that sites like

Facebook, Twitter, and Google + allow employers to get a glimpse of their applicants

outside the confines of the information they collect during the traditional selection

7 Social Media, Big Data, and Employment Decisions: Mo’ Data, Mo’ Problems?


process and provided valuable insight into what these individuals would be like to

work with on the job. Indeed, a growing number of employers (52 %) are using

social media to research job candidates (CareerBuilder, 2015). As with recruiting,

the overwhelming number of social media profiles and online information available

about a job applicant makes the union of big data and analytics to help making hiring decisions seem intuitive. However, a review of the industry revealed that, when

compared to the number of companies that disclose the use of social media data for

recruiting, there are fewer companies that publically disclose the use of social

media data to assist with selection decisions. While the exact reason for this is not

known, one possible explanation is the legal implications for using social media

data in hiring decisions (e.g., Davison et al., 2011; Society for Human Resource

Management, 2015).

However, social media data does not represent the bulk of the data that organizations use to inform decisions: the vast majority of technology companies that are

using big data and analytics in the selection process use information from social

media only as a part of the information they input into their predictive and prescriptive analytic algorithms. For example, one technology company offers a software

tool that assesses candidates by examining half a billion data points on everything

from gas prices to national and local unemployment rates in combination with their

social media usage (Marrs, 2014). Big data and analytics are most often used in the

hiring process with private, in-house data. Analytics are used on these data to help

organizations select the best employees for open positions ranging from call-center

operators to department store cosmetics department sales staff to information technology professionals (Bloomberg, 2011; Kutik, 2014; Pearlstein, 2014). While

there are a number of issues surrounding hiring managers manually scanning social

media profiles when making employment decisions (see Davison et al., 2011), it

appears that this is the primary way social media data is being integrated into the

selection process.

An emerging line of computational linguistics research could possibly automate

and improve the scanning of social media profiles to extract information traditionally contained in an applicant’s selection measures. This line of research uses

extracts unstructured data in the form of the words and phrases that users post on

SNS and other social media sites, more specifically Facebook, and then uses text

analytics to examine this information. This line of research has steadily increased

its accuracy in the assessment of the five factor model of personality (see Park

et al., 2014; Schwartz et al., 2013). While this line of research looks promising and

could be used to more accurately assess “fakeable” characteristics such as personality and attitudes, more research is needed before these techniques can be used in

selection (see the section on Challenges and Unforeseen Consequences of Big

Data and Analytics).

One of the most promising ways to integrate social media technology into the

selection process is to use the information produced by an organization’s internal social network platform. More and more companies, large and small, are

turning to internal SNS to solve problems faster, share information more quickly

and efficiently among internal and external employees, develop new and


S. Guilfoyle et al.

improved products more efficiently, and redesign organizational business processes using in-house software that often resembles Facebook’s easy-to-learn

style (Huertas, 2012; Mullaney, 2012). While this internal social networking

platform can make work more effective and efficient for the employees, it also

provides human resource researchers and professionals with structured and

unstructured data to objectively assess work processes, employee engagement,

performance, and job satisfaction.

Using big data collection techniques to capture this internal social networking

information along with internal other data could give human resource professionals

and researchers access to unprecedented objective process and performance metrics. Analyzing this type of data with modern analytic techniques will allow human

resource researchers and practitioners to examine relationships in ways they have

not been able to do in the past. In the area of selection, for example, traditional

methods of validating selection batteries rely on a retrospective approach. That is,

both traditional predictive and concurrent validation strategies require data to be

collected and analyzed to determine how useful a selection system has been. This

approach provides an extremely useful, albeit retrospective, examination of the

validity of a company’s selection system.

These traditional validation strategies can be improved through the implementation of big data and analytics. Specifically, companies capture structured and

unstructured data from employees’ emails, computer logs, internal social media

and intranets, and project tracking timesheets. Descriptive and diagnostic analytics

can then be run on this data to develop performance, engagement, and satisfaction

metrics. Diagnostic and predictive analytics can then be used with these metrics and

the company’s traditional selection system data to continually validate the selection

battery. Further, these engagement and performance metrics can be combined with

traditional employee culture and job satisfaction survey data and, using diagnostic

and predictive analytics, help develop a model of employee morale and retention.

These data and analyses would even allow for a more valid assessment of personenvironment fit during the hiring process. Prescriptive analytics could then be used

to help identify, in near real-time, employees or departments that have low morale

and are at risk to leave the company and propose interventions that could help

improve the situation.

Finally, all this information can be combined with employee compensation data,

HRIS project tracking information, and the financial data from the various internal

and external projects on which employees spend their time. Linkage or path-analysis

help can then be used to a return on investment model. This model would help quantify the financial return on a variety of human resource activities, including recruiting and selection. Given that only about one in five human resource professionals

use data and analytics to assess return on investment (Chaudhary, Subramanian,

Sinha, & Bhattacharya, 2012), using internal social media data in combination with

existing company data and analytics can both improve human resources function

and provide clear evidence for human resources’ financial impact on an organization’s bottom line.

7 Social Media, Big Data, and Employment Decisions: Mo’ Data, Mo’ Problems?



Challenges and Unforeseen Consequences of Big Data

and Analytics

Despite the potential upside of using big data, analytics, and social media for

employment decisions, there are some logistical challenges that need to be considered and addressed before these benefits can be fully realized. There are also some

extremely important, and potentially unforeseen, consequences of using social

media information when making employment decisions that need to be carefully

considered and thought through. This section highlights a few of the technical and

logistical challenges and legal, ethical, and social implications of using big data,

analytics, and social media when making human resource decisions.


Scale and Timeliness of Data

If an organization wishes to undertake the task of monitoring and assessing applicants’ social media activity they will be continually faced with the issue of scale.

That is, staying up-to-date on the ever-changing types and amounts of information

being produced in the online environment is an uphill battle. Given that social

media features are continually changing, the types of data being generated are also

continually changing. These rapid changes mean that organizations that wish to

incorporate social media data must constantly evolve, refine, and adapt to the types

and amounts of information they are collecting, cleaning, and storing. Continuous

changes also mean that these organizations need to continually validate and revalidate their analytic algorithms to ensure the accuracy and validity of the conclusions

they are producing.

In addition to the scale of the data, the timeliness of the information is also

equally important. Due to the frequency with which people use social media platforms and the subsequent speed with which data is generated on social media sites,

timeliness is of particular concern when considering the use of big data. Organizations

must be careful to ensure that any data collected from social media platforms that is

used to make employment decisions is not only accurate, but up-to-date. Different

SNS will vary in the rate at which information is generated and updated. For example, LinkedIn activity is fairly static, with users updating their pages with mastered

skills and making connections with other professionals. LinkedIn users may also

share business-relevant links with other users with whom they are connected.

Unlike LinkedIn, data on Twitter and Facebook pages is often constantly being

updated by users. By using such dynamic and fluctuating sources of information

about their job candidates, organizations run the risk of not being able to efficiently

organize and effectively analyze social media data in a timely manner. If organizations begin to fall behind on data collection and cleaning and start to make decisions

with out-of-date information, the accuracy and validity of those employment decisions may falter.



S. Guilfoyle et al.

Issues of Privacy, Consent, and Accountability

Privacy concerns surrounding big data and its use in human resources leave practitioners in a legal and ethical quagmire. While organizations currently use massive

amounts of data simultaneously collected from a variety of different sources, including social media platforms, the ethicality and legality of such practice is unclear

(Miller & Weckert, 2000). For example, using social media when making employment decisions may be considered an invasion of privacy (Brandenburg, 2008;

Clark & Roberts, 2010), defamation (Davison, Maraist, Hamilton, & Bing, 2012),

or a violation of the Fair Credit Reporting Act and/or the Stored Communications

Act (Smith & Kidder, 2010). IBM recently identified several important factors to

consider when collecting and using applicant data, including context, consent, and

accountability (Chessell, 2014).

First, IBM highlights the importance of considering the original purpose for collecting the data, or the data’s context (Chessell, 2014). For example, job-seekers

who engage in social media often fail to consider the implications of their latest

politically incorrect tweet or posted pictures of a particularly bacchanal evening,

yet these same people carefully groom their LinkedIn page in the hopes of attracting a future employer. If job-seekers were aware that potential employers may view

their Facebook profiles or activity on Twitter, hopeful employees would most assuredly take additional measures to censor behaviors or comments that would be

unpopular (or illegal) in the workplace. While some people recognize the fact that

employers are increasingly using social media to make employment decisions and,

therefore, take care to limit controversial content on their social media pages

(Florentine, 2014; Urshel, 2012), others are lulled into a false sense of security with

platforms’ security features.

On a similar vein, IBM also suggests examining the idea of consent and the

options available to the generators of data. In the case of social media, users have

moderate control over privacy settings and the access that outside entities have to

social media users’ personal information (Chessell, 2014). Despite security controls, organizations and outside individuals are able to gain access to individuals’

data with relative ease through the use of security bypasses and the majority of

users’ lenient privacy settings. The Electronic Communications Privacy Act

(ECPA) of 1986 prohibits the intentional interception of “any wire, oral or electronic communication”; however, the ECPA does include a business use exemption, which allows for the monitoring of email and phone calls provided that

several elements, including consent, are present (Beesley, 2012). What is not

known is how this act extends to social media. Thus, several legal questions

remain: Is there an implied lack of consent when users choose to attempt to protect their personal information? Or are lax security settings the equivalent of

applicants’ or employees’ consent?

While the issue of consent has potential legal and ethical implications, there are

also social and cultural implications for using social media information when making employment decisions. Specifically, there is anecdotal evidence that employees

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3 Social Media Data, Analytics, Recruiting, and Selection

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