Teaching Digital and Social Media Analytics: Exploring Best Practices and Future Implications for Public Relations Pedagogy ABSTRACT One of the growing areas within public relations is digital and social media analytics. Teaching the use of analytics to communication students is not new, but studying what is being taught is almost non-existent. The public relations research literature has supported exploring the value of data analysis to gain audience insights, to measure communication strategies, and to evaluate campaign efforts. The purpose of this study is to explore the ways in which faculty are teaching social media analytics. Two content analyses were conducted to explore trends of digital and social media analytics training. Authors analyzed related course syllabi and a Twitter chat on the subject sponsored by the AEJMC PR Division and PRSA Educators Academy. Findings and future implications in teaching digital and social media analytics for educators and public relations practitioners are discussed.

1. Public Relations Education
Association for Education in Journalism and Mass Communication
Journal of
JPRE
Volume 4, Issue 2, Fall 2018
A publication of the Public Relations Division of AEJMC
ISSN 2573-1742

2. Volume 4, Issue 2, Fall 2018
A publication of the Public Relations Division of AEJMC
ISSN 2573-1742
© Copyright 2018 AEJMC Public Relations Division
Journal of Public Relations Education
Editorial Staff
Emily S. Kinsky, West Texas A&M University, editor-in-chief
Tiffany Gallicano, University of North Carolina at Charlotte,
senior associate editor
Lucinda Austin, University of North Carolina at Chapel Hill,
associate editor
Chuck Lubbers, University of South Dakota, associate editor of reviews
Kathleen Stansberry, Elon University, web manager
Note from the Editor-in-Chief:
In this issue, you will find three research articles, all five of the top
AEJMC PR Division’s Great Ideas For Teaching presented in Washington,
D.C., and two reviews by Matt Kushin, which relate to one another on the
topic of teaching social media.
Volume 4, Issue 2 reflects an incredible amount of work done prior to
my editorship. The previous editor-in-chief, Chuck Lubbers, handled the
review assignments for each of the research articles for this issue prior to
me moving into this role on Jan. 1, 2018, and two of them were accepted
for publication under his tenure in 2017. The first acceptance letter I was
honored to send as the editor went to Drs. Brunner, Zarkin and Yates. A
special thanks to Chuck for his work with authors and reviewers to get us
ready for Volume 4.
Thank you to Tiffany, Lucinda, Chuck and Katie, who have invested
countless unpaid hours proofreading, editing and formatting this issue.
Without your service, this issue would not exist. Special thanks go to
Rebekah Grome, who also came to our aid with proofreading.

3. Table of Contents
Research Articles
1-20
21-50 What do Employers Want? What Should Faculty Teach? A
Content Analysis of Entry-Level Employment Ads in Public
Relations
Brigitta R. Brunner, Kim Zarkin, & Bradford L. Yates
51-86 Teaching Digital and Social Media Analytics: Exploring Best
Teaching Briefs
PRD GIFT Winners from AEJMC 2018
87-98 Building a Social Learning Flock: Using Twitter Chats to
Enhance Experiential Learning Across Universities
Amanda J. Weed, Karen Freberg, Emily S. Kinsky,
& Amber L. Hutchins
99-106 Diagnosing Health Campaigns: A Campaign Evaluation
Assignment
Laura E. Willis

4. Teaching Briefs (continued)
PRD GIFT Winners from AEJMC 2018
107-114 Teaching Trolling: Management and Strategy
Leslie Rasmussen
115-122 Sparking Creativity Through Purpose-Driven Storytelling
Chris Cooney
123-127 Looking in to see out: An Introspective Approach to Teaching
Ethics in PR
Regina Luttrell & Jamie Ward
Reviews
128-133 Social Media Campaigns: Strategies for Public Relations and
Marketing
Matthew J. Kushin
134-145 Meltwater Media Intelligence Software
Matthew J. Kushin

5. Journal of Public Relations Education
2018, Vol. 4, No. 2, 51-86
Teaching Digital and
Social Media Analytics:
Exploring Best Practices and Future
Implications for Public Relations Pedagogy
Michele E. Ewing, Kent State University
Carolyn Mae Kim, Biola University
Emily S. Kinsky, West Texas A&M University
Stefanie Moore, Kent State University
Karen Freberg, University of Louisville
Abstract
One of the growing areas within public relations is digital and social
media analytics. Teaching the use of analytics to communication
students is not new, but studying what is being taught is almost
non-existent.The public relations research literature has supported
exploring the value of data analysis to gain audience insights, to
measure communication strategies, and to evaluate campaign
efforts. The purpose of this study is to explore the ways in which
faculty are teaching social media analytics. Two content analyses
were conducted to explore trends of digital and social media
analytics training. Authors analyzed related course syllabi and a
Twitter chat on the subject sponsored by the AEJMC PR Division
and PRSA Educators Academy. Findings and future implications
in teaching digital and social media analytics for educators and
public relations practitioners are discussed.
Keywords: social media; social media analytics; public relations
education; digital analytics
The field of public relations, like many other professional
disciplines, has been compelled to respond to the growing demands and
shifts in the digital social landscape. Within the public relations education
sector, there has been a rise of social media research (Duhé, 2015). One
of the challenges in social media research and practice is to determine
how to effectively bridge the expectations of practitioners with what is
being taught in the classroom. Several pedagogical studies looking at
social media (e.g., Kim & Freberg, 2016; Zhang & Freberg, 2018) have

6. 52
attempted to make these connections stronger within the discipline,
yet with social media changing so quickly, professors face significant
challenges keeping up with the trends, as well as addressing the key
areas and skills students need to be successful in the field. Teaching
the use of analytics to communication students is not new, but studies
examining what is being taught in this area are almost non-existent; thus,
an investigation of current curriculum trends related to digital analytics is
a goal of the current study.
Literature supports the value of data analysis to gain audience
insights and shape and measure communication strategies (DiStaso,
McCorkindale, & Wright, 2011; Elkin, 2017; Grates, 2016; Jain, 2016).
Kent, Carr, Husted, and Pop (2011) pointed to the benefit of advances
in technology to students: “With new tools like analytics in the hands of
communication professionals, understanding stakeholders and publics
becomes easier, and students become stronger professionals” (p. 543).
As Stansberry (2016d) explains, the usefulness of social media goes
far beyond sending messages; social media allow practitioners to better
understand their target publics. Thus, a key skill students need to learn
is how to make sense of the data available. According to Elkin (2017),
the majority of marketers (72%) value employees’ data analysis abilities
even more than other social media skills (65%). Beyond that, of the 12
“professional values and competencies” listed by the Accrediting Council
on Education in Journalism and Mass Communications, five closely
connect to the idea of teaching digital analytics. The ACEJMC (2013)
guidelines have the following requirements:
irrespective of their particular specialization, all graduates
should be aware of certain core values and competencies and
be able to . . . understand concepts and apply theories in the use
and presentation of images and information . . . think critically,
creatively and independently; conduct research and evaluate
information by methods appropriate to the communications
professions in which they work; . . . apply basic numerical
and statistical concepts; apply current tools and technologies
Ewing et al.

7. Vol. 4(2), 2018 Journal of Public Relations Education 53
appropriate for the communications professions in which they
work, and to understand the digital world. (para. 9)
The importance of instruction in analytics at all levels was emphasized
by Kent et al. (2011), who said introductory students should be presented
with the ideas and tools connected to analytics, while actual data gathering
should be done regularly by advanced students. The authors pointed
toward the ability to understand data and how to communicate the insights
clearly and correctly because numbers, by themselves, do not tell the story.
According to Kent et al., students need actual data to learn from so they
do not rely on “stereotypes and guesses” in their campaigns; “having data
allows professionals to make better decisions. Just as many professors use
scenarios and case studies to teach ethics, having access to real data and
helping students learn how to interpret data is valuable” (p. 541). Teaching
data analytics to students in public relations is important because of what
can be learned about relevant stakeholders and the environment in which
an organization exists.
The purpose of this study is to examine how U.S. public relations
professors are teaching digital and social media analytics. Following
further examination of literature in the next section, the current study will
fill some of these gaps through new research efforts into what is currently
taught on the topic of digital analytics and what some experts say should
be taught.
Literature Review
Much of the research related to digital training in public relations
classrooms focuses on the use of social media (Childers & Levenshus,
2016; Fraustino, Briones, & Janoske, 2015; Kim & Freberg, 2016);
however, gaps remain in scholarship that specifically focus on the area of
teaching social media analytics. This is an important gap to address, as the
use of measurement and the ability to understand data analytics is crucial
to future public relations professionals.
In the 2017 report on undergraduate education from the

8. 54
Commission on Public Relations Education (Toth & Lewton, 2018),
both educators and practitioners identified “research and analytics” as a
highly desired skill (p. 87). The desirability of that skill was rated 4.30 by
educators and 4.08 by practitioners (1 = not desired, 5 = highly desired).
The educators participating in the survey also rated how well “research
and analytics” is covered in their programs (m = 3.78), and practitioner
participants rated how frequently that skill is found in new graduates
hired by them (m = 2.70). Additionally, when asked to rate specific
topics of importance for PR curriculum, both practitioners and educators
rated analytics highly. On a scale of 1 (not essential) to 5 (essential),
educators rated the importance of “data analytics” in the curriculum at an
average of 4.15, and practitioners rated the topic 3.93 (p. 89). The topic
of “measurement and evaluation” was also rated highly by educators (m
= 4.57) and by practitioners (m = 4.42), as well as the topic of “social
media” (m = 4.60 by educators; m = 4.46 by practitioners) (p. 89).
Social Media Pedagogy Research: Concepts and Skills
Early on, Anderson and Swenson (2008) studied what public
relations educators should cover in class related to “new media” (p. 109).
They solicited advice from PR professionals about what they should
teach to best prepare their students, and one of the emerging themes
was measurement. The authors followed up this research effort with a
study about digital competencies (Anderson & Swenson, 2013), which
also sought advice from PR professionals, specifically via a Twitter chat
(#PR20Chat) and a survey of top bloggers, including Brian Solis, Arik
Hanson, Gini Dietrich and Deirdre Breakenridge. Prior to the current
study, the examination of social media curriculum has been rather broad;
no one has yet focused specifically on teaching digital analytics in public
relations.
In order to best prepare students for the professional world,
researchers have examined the use of social media in the industry (e.g.,
McCorkindale, 2010; Sundstrom & Levenshus, 2016; Wright & Hinson,
Ewing et al.

9. Vol. 4(2), 2018 Journal of Public Relations Education 55
2017). Other researchers have focused on practicing social media skills
in the classroom (e.g., Fraustino, Briones, & Janoske, 2015; Kinsky &
Bruce, 2016; Kinsky, Freberg, Kim, Kushin, & Ward, 2016; Kinsky,
Kuttis, Nutting, & Freberg, 2016; Tatone, Gallicano, & Tefertiller,
2017), including the use of multiple platforms (e.g., Janoske, Briones, &
Fraustino, 2016). Researchers have also studied the use of new media by
students to communicate with professors outside of the classroom (Waters
& Bortree, 2011).
Several studies have focused on the use of particular social media
tools. Most of the research about the use of social media in the classroom
has focused on Twitter (Anderson & Swenson, 2013; Carpenter & Krutka,
2014; DeGroot, Young, & VanSlette, 2015; Forgie, Duff, & Ross, 2013;
Fraustino et al., 2015) and Facebook (Frisby, Kaufmann, & Beck, 2016).
Facebook and Twitter have been the most frequent social media
platforms utilized for public relations classroom exercises; however,
LinkedIn (Edministon, 2014; Peterson & Dover, 2014), YouTube
(Madden, Briones Winkler, Fraustino, & Janoske, 2016), and blogs (e.g.,
Moody, 2010) have also been used in communication courses. Although
much of the extant research examines one platform at a time, some
professors have shared their use of multiple social media platforms within
their campaign client projects (Childers & Levenshus, 2016; Melton
& Hicks, 2011) to teach students in public relations classes about the
fundamentals of writing, campaign strategy, and research approaches.
Some researchers, such as Anderson and Swenson (2013), have
suggested training students to use social media professionally by using
role-playing exercises and case studies, as well as using social media
platforms in class. Providing assignments that create a realistic experience
allows students, who will become future professionals, the opportunity
to apply what they have learned in the classroom setting (Anderson,
Swenson, & Kinsella, 2013). Similarly, Neill and Schauster (2015)
recommended integrating math practice related to social media analytics
into public relations budgeting projects in capstone courses to help
students prepare for professional demands.

10. 56
Although many skills related to social media have been referenced
in previous literature, there is a lack of research exclusively focused on
what professionals and educators see as needed concepts and skills in
the curriculum related to analytics. This lack leads to the first research
question:
RQ1: What digital analytic concepts and skills do both public
relations students and practitioners need to understand?
Digital Analytics Outcomes
Certain research has focused on particular outcomes rather
than platforms, one of which is analyzing target publics. According to
Stansberry (2016d), “The information shared by key publics on social
media sites has been a goldmine for public relations practitioners
looking to understand the concerns, needs, and preferences of their target
audiences” (p. 76). The public nature of so many social media platforms
gives students access to an enormous amount of data for free. Stansberry
(2016d) argued “teaching students to perform publics research not only
exposes them to advanced social media analytics tools and techniques,
it helps prepare them to thrive in a rapidly changing profession” (p. 88).
This training allows students to analyze data while also brainstorming
creative ways to apply their findings into campaigns, strategic plans,
and situational analyses for clients and brand audits, to name a few
possibilities.
Social media provide practitioners with valuable data, but they
are not the only digital sources that should be analyzed. Website traffic
is also important to consider. Kent et al. (2011) expressed that website
analysis is an important addition to social media monitoring in order to
gain information “about the full range of organizational visitors” (p. 542).
Moody and Bates (2013) also looked at website-related content in their
study of students’ knowledge of search engine optimization and of current
trends in SEO within the PR industry.
Digital analytics training must not just cover collecting data, but
Ewing et al.

11. Vol. 4(2), 2018 Journal of Public Relations Education 57
should also include identifying the metrics that can be used for evaluation
and measurement purposes for public relations professionals and
researchers. Kent et al. (2011) recommended testing students on analytic
terms (e.g., bounce rate), using case studies to explain how analytics
can be used in public relations, and providing real datasets for students
to analyze and use to propose strategic communication changes for an
organization based on the analytic results gathered. There are still some
measurement concerns and issues pertaining to social media. Waddington
(2017) discussed how some of the issues that occurred in traditional
PR measurements are translating into the same challenges for social
media. This concern about what to measure points to the importance of
understanding how to analyze and interpret the data collected on social
media into actionable strategies.
Kent et al. (2011) recognized the different training opportunities
between introductory public relations classes and advanced courses.
Beginning students might simply be shown what data looks like,
while upper-level courses should involve more advanced tasks such as
monitoring website traffic. According to Kent et al. (2011), students can
engage in more advanced work after understanding terms and concepts:
The next move is to be able to understand how one variable
influences another (“bounce rate and time on site are related . . .”).
The third move is to be able to explain how variables change and
interact over time or because of external forces (“the outbreak of
Malaria drove up TOS during the month of April and also drove
down the bounce rate . . .”). This sort of sequential, cause and
effect, reasoning takes some time and practice to master. (p. 543)
In addition, some digital analytics strategies taught in classes do not tie
directly into how they impact business or communication objectives. Thus,
integrating the principles and framework of social media measurement
protocols from AMEC (International Association for the Measurement
and Evaluation of Communication) and digital analytics frameworks and
connections to DAA (Digital Analytics Association) is necessary. AMEC’s
Integrated Framework (2016) helps guide communications professionals

12. 58
in measuring the impact of their work. The interactive website tool guides
professionals through the process of “aligning objectives to establishing
a plan, setting targets and then measuring the outputs, outtakes and
outcomes” (para. 4). The Digital Analytics Association Competency
Framework (2015) serves as an industry reference for employers and
educators by providing an overview of the necessary knowledge, skills
and competencies needed for careers in digital analytics.
Most of the research exploring digital analytics courses and
curriculum do not emphasize these two associations’ frameworks, which
raises a point of concern. Without this bridge, there is a divide between
what is being taught in the classroom and what is being implemented in
practice. A first step in filling missing gaps in the curriculum is to find out
what is currently expected of students in courses that include analytics
training. This leads to the following research question about what students
are expected to accomplish by the end of a course related to digital
analytics:
RQ2: What outcomes related to analytics do faculty incorporate
into syllabi as part of their courses teaching analytics?
Social Media Course Communication Methods
Instructional methods in public relations classes have been
examined by many previous researchers, and the discussion of creating a
class hashtag goes back to at least 2011 (Lowe & Laffey). However, no
previous studies were found that examined the inclusion of class hashtags
or Facebook groups across social-media-related public relations classes.
This use of particular social media communication methods within
analytics-related classes leads to this study’s third research question:
RQ3: What social media communication methods are embedded
into courses that teach social media analytics?
Ewing et al.

13. Vol. 4(2), 2018 Journal of Public Relations Education 59
External Training and Certification Opportunities
For students to be prepared to process their future employers’
data, they must be trained. Like previous researchers, Stansberry (2016d)
pointed out the necessity of adding new training modules to classes so
that public relations students can keep up with industry: “The percentage
of individuals who used social media to share multimedia content has
risen rapidly, and it has become imperative that future public relations
professionals be equipped with the skills to research and measure this
popular form of communication” (p. 76). According to Fraustino et al.
(2015), “young practitioners increasingly must develop social media skills
to be competitive on the job market and successful in the workplace, and
such training can start in the PR classroom” (p. 1).
A number of companies have begun to offer training programs
online (e.g., Hootsuite Academy, HubSpot), with some programs designed
specifically for college classrooms (e.g., Meltwater). Public relations
professors have taken advantage of analytics tools and tutorials for their
students to learn from, as well as certain programs’ certification options,
allowing students to prove their new knowledge and skills (e.g., Kinsky
et al., 2016). The increasing availability of free analytics tools has made it
easier to incorporate analytics training into the classroom.
In light of research showing employer demand for students to meet
today’s digital analytics challenges (Ewing, 2014; Fraustino et al., 2015;
Kim & Freberg, 2016; Neill & Schauster, 2015; Stansberry, 2016d) and an
increase in social media experiential learning in the classroom (Childers
& Levenshus, 2016; Fraustino et al., 2015; Frisby et al., 2016; Kinsky &
Bruce, 2016; Kinsky, Freberg, et al., 2016; Kinsky, Kuttis, et al., 2016;
Madden et al., 2016), this study will also seek to explore the ways in
which faculty are teaching social media analytics by integrating analytics-
related certification testing:
RQ4: In what ways do faculty incorporate external certifications as
part of their courses teaching analytics?

14. 60
Incorporating Professional Expertise
In addition to online training programs with analytics tools,
professors can recruit public relations professionals with data analysis
experience to speak to their classes, whether they are present in the room
or joining the class via video chat technology such as Skype. Research has
found value in guest speakers sharing experiences from their work (e.g.,
Riebe, Sibson, Roepen, & Meakins, 2013), which prompts the study’s final
question about inviting external professionals as guest speakers related to
analytics:
RQ5: How are faculty utilizing professional experts to enhance
their courses that teach analytics?
Methods
Phase 1: Course Syllabi
To understand the ways in which professors teach social media
analytics within a classroom, the authors conducted two content analyses.
The first was a content analysis of course syllabi (N = 31) from faculty
who teach social media analytics to communication, public relations,
journalism, business, or advertising students. The syllabi were gathered
from universities around the country through requests on the listservs
of the Public Relations Division of the Association for Education in
Journalism and Mass Communication (AEJMC) and the Educators
Academy of the Public Relations Society of America. These syllabi were
gathered by May 2016 and represented both undergraduate and graduate
courses.
Coding Procedure for Syllabi
The authors coded the information from the course syllabi using 32
factors, including names of the courses, types of assignments, tools used in
Ewing et al.

15. Vol. 4(2), 2018 Journal of Public Relations Education 61
the class, days dedicated to teaching analytics, and integration of industry
professionals within the course. A variety of institutions were represented
within the sample, including private and public, large and small, as well as
universities from various areas of the U.S. (see Appendix).
Intercoder Reliability for Syllabi
The codebook and coding procedure were tested by the authors
who independently coded each of the syllabi, randomly assigning specific
ones to each author. After the initial coding, the authors examined the
results, which revealed inconsistencies across multiple coding categories.
To address this, the authors adjusted the codebook to provide more clear
definitions for manifest syllabus content versus latent content. After
the revisions, two of the authors independently coded each syllabus.
Despite the initial revisions to the codebook, finding an appropriate way
to evaluate the agreement between coders remained challenging due to
the non-standardized structure of the syllabi and general topics listed.
For example, exams and extra readings were prevalent, but whether they
related specifically to analytics (one of the coding items) was not always
clear. Another example of coding challenges was found in coding “course
outcomes.” Some syllabi listed “objectives,” others listed “goals,” others
mentioned “outcomes,” and some had none of the above.
As a result, the researchers used Krippendorff’s Alpha for this
study’s inter-coder reliability analysis because it is an appropriate
approach when having a number of observers or levels of measurement
applied in content analysis (Hayes & Krippendorff, 2007). In addition, this
measurement equation looks at “observed and expected disagreement”
(Joyce, 2013, para 2).
After the revision of the codebook, the values for agreement
among coders for these courses were as follows: courses that employ
analytics within the title (α = .93); requiring textbooks (α = .67); requiring
additional readings (α = .69); case studies to read (α = .69); students
conducting a case study during the course (α = 1); professionals presenting

16. 62
case studies (α = .89); guest lectures by professionals (α = .86); the use of
professional certifications as course requirements (α = .85); listing “KPIs”
as a course outcome (α = .89); listing specific tools in course outcomes (α
= .77); listing “listening” as a course outcome (α = .82); listing “insights”
on the course outcomes (α = .68); listing “ethical implications” on the
course outcomes (α = .72); incorporating a class hashtag (α = 1); using a
class Twitter list (α = 1); and using a class Facebook group (α = 1).
According to Krippendorff (2004), it “is customary to require α > .800.
Where tentative conclusions are still acceptable, α > .667 is the lowest
conceivable limit” (p. 241). Using these standards of measurement, the
above elements each fall within the range of acceptable agreement.
Phase 2: Twitter Chat
The second phase of the study included a content analysis of a
Twitter chat, which was held in April 2016 to allow an opportunity for
crowdsourcing among public relations professionals and educators with
digital analytics expertise (see Figure 1). Social media channels can be
beneficial to researchers by cultivating public participation, via an open
forum, where participants can respond to questions quickly (Glowacki,
Lazard, Wilcox, Mackert, & Bernhardt, 2016). Similar Twitter chats have
been analyzed by Anderson and Swenson (2013), Carpenter and Krutka
(2014), DeGroot et al. (2015), and Fraustino et al. (2015).
The chat for the current study included 56 participants and 300
tweets. Two professors and two practitioners hosted the discussion.
Participants were invited through memberships in public relations
academic and professional associations, as well as personal outreach to
faculty networks via email and social media channels. Twitter messages
were captured during an hour-long live Twitter chat, which used the
hashtag #PRAnalytics. Questions were posed by the hosts, who used
identifiers (e.g., Q1, Q2, Q3,) to present each question. Participants
indicated which question they were responding to using identifiers (e.g.,
A1, A2, A3). A series of nine questions were proposed to spur discussion
Ewing et al.

17. Vol. 4(2), 2018 Journal of Public Relations Education 63
about digital analytic concepts both public relations students and
professionals need to understand.
A thematic analysis of the tweets was conducted to determine the
content that industry leaders and educators thought were best practices
and to identify helpful tools for teaching digital analytics. The thematic
analysis involved looking for patterns; those emerging themes became
categories in the analysis for each question posed in the chat (see Fereday
& Muir-Cochrane, 2006). The authors then grouped the data by category
(see Riessman, 2005) to identify final concepts that emerged from the
Twitter chat.
Figure 1
Summary Statistics from #PRAnalytics Twitter Chat
300 tweets
54 Text Tweets
18%
116 Retweets
38.67%
68 Replies
22.67%
67 Links/Images
22.33%
General Overview April 21, 2016 8:43:41 p.m. - April 22, 2016 9:56:24 a.m.
581,631 potential impacts
2,102 followers per contributor
117,686 potential reach
56 contributors
5.36 tweets per
contributor
Findings
Concepts and Skills
RQ1 explored the digital analytic concepts and skills that both
public relations students and practitioners need to understand. Core
themes from the Twitter chat on #PRAnalytics included measurement,
contextualizing data, critical thinking skills, social listening skills,
knowledge of social media and analytical tools, and digital storytelling
skills.
Twitter chat participants emphasized the importance of students
understanding measurement (n = 12 tweets) and contextualizing data
(n = 10 tweets). For example, MasterCard’s Bernard Mors (2016a)

18. 64 Ewing et al.
tweeted, “Digital PR produces a lot of data, the challenge is to turn this
data into actionable insights. #PRAnalytics.” PR professional Michael
Brito (2016b), from LEWIS Global Communications, said, “THE most
important data is audience intelligence. PR & Marketing must understand
the behaviors of very specific audiences #PRAnalytics.” PR professor
Kathleen Stansberry (2016a) said, “We focus too much on brand mentions/
engagement. Need to teach the importance of using data to understand
audience concerns #PRAnalytics.”
Measuring results. Participants in the Twitter chat advocated that
public relations students should understand definitions of metrics, analysis
of metrics, and use of metrics to measure strategic communication.
Practitioners tended to emphasize the importance of showing business
value for public relations, and one practitioner mentioned that employers
are evaluating students’ understanding of digital analytics in terms of
how students connect back to business objectives. Jennifer Trivelli
(2016) tweeted, “The key is zeroing in on metrics that truly support biz.
goals and that you can influence. That which is measured is managed.
#PRAnalytics.” During the chat, professor Tim Marshall (2016) wrote,
“Employers want students who connect measurement/eval back to overall
biz objectives, rather than platform vanity metrics. #PRAnalytics.”
Practitioners and educators also agreed on the differentiation of
volume metrics and engagement metrics as one of the most important
concepts for students to understand. Rather than looking at vanity metrics
such as likes or retweets, these individuals recommended focusing on
metrics testing engagement, while not confusing terms like volume, reach,
and influence. When people directly interact with a brand through writing
a comment, sharing a post and extending the reach or influencing other
levels of publics that the brand could not directly reach, this type of social
media activity would be considered engagement. In other words, students
should understand how to specifically track and measure direct interaction
with publics that can show outcomes for social media activities as opposed
to simply grabbing quick data points (vanity metrics) that do not show
whether the public is truly interacting on social media with the brand.

19. Vol. 4(2), 2018 Journal of Public Relations Education 65
Understanding context. Contextualizing data (n = 10 tweets)
and critical thinking skills (n = 10 tweets) were recurring themes among
all Twitter chat participants for questions about concepts, skills, best
practices, and pitfalls students have when analyzing data. Participants
emphasized the importance of understanding how to transform the
data into actionable insights. Critical thinking abilities included asking
questions, analyzing metrics, and operationalizing key terms. Overall, both
practitioners and educators articulated the struggle with getting lost in the
data and recognizing which data to mine and analyze, and then developing
meaningful insights to drive communication strategies. For example,
Mors (2016b) said, “Same practices 4 social & traditional PR: set
objectives & KPIs, tools to capture data, visualize results, derive insights.
#PRAnalytics.” PR professor Ai Zhang (2016b) posted, “Contextualize
data to draw meaningful conclusions → drive strategic decision-making.
#PRAnalytics.” Professor Stansberry (2016c) tweeted, “Learn to speak
(and write) in the language of the C-Suite. Ask the right questions. Always
be critical of your data. #PRAnalytics.” Brito (2016a) pointed out that
“anyone can look at data, run a report, spew out #s. Very few can extract
an insight that can drive a narrative/program. #PRAnalytics.”
Using tools and listening. Social listening skills and knowledge
of social media and analytical tools also emerged as valuable digital
analytic skills for public relations students and graduates, with each
topic generating at least eight responses. Listening skills (n = 8 tweets)
focused on the ability to monitor social environments, including using
listening tools. Winkler (2016a) tweeted, “Social listening is the process
of monitoring digital media channels to devise a strategy that will better
influence consumers. trackmaven.com #PRAnalytics.” PR professor Katie
R. Place (2016) tweeted this assignment suggestion: “Basic one, but we
learned so much from taking on a real client and producing monthly social
listening/monitoring reports. #PRAnalytics.”
Connected to both RQ1 and RQ4, knowledge of social media tools
(n = 8 tweets), native analytic tools (n = 5 tweets), Google Analytics (n =
5 tweets) and Hootsuite (n = 4 tweets) encompassed a student’s ability to

20. 66 Ewing et al.
stay up-to-date with the latest digital platforms and tools, and the student’s
ability to then choose an appropriate platform given an organization’s
goals or clients. In line with the Twitter chat, the content analysis of
syllabi showed faculty use a variety of tools and resources to prepare
students. Some of the popular social media tools mentioned on the syllabi
were Google Analytics (n = 11), Hootsuite (n = 10), Facebook analytics
(n = 6), Twitter analytics (n = 4), Storify (n = 3), Google Adwords (n = 3),
Excel (n = 3), Crimson Hexagon (n = 2), Radian6 (n = 1), Canva (n = 1),
Klout (n = 1) and Sprout Social (n = 1). Despite the plethora of analytic
software available, some Twitter chat participants (n = 3) noted that it
is not necessarily important for students to have familiarity with a wide
range of tools, but it is more important for them to understand the data and
methods behind specific platforms, so they have the ability to transition
from platform to platform.
Since analytics tools come and go, professor Itai Himelboim’s
syllabus provided a valuable assignment faculty could consider.
In his Listening and Engagement course (I. Himelboim, personal
communication, Feb. 2, 2016), students are assigned to work in groups for
the duration of the semester, and in one of the assignments, they are asked
to find, learn, and generate a report based on a new social media analytic
or listening tool. Students are required to find a free social media listening
tool or one that offers a free trial. Students must choose the tool or tools
that help them address their client’s questions/meet their goals best. Their
final report is to summarize social media activity related to their client/
topic, using Crimson Hexagon, which they learn in class, as well as the
free tool used to collect and analyze the data.
In another analytics course evaluated in the study (S. Moore,
personal communication, March 21, 2016), students worked individually
and in groups to define, measure, analyze and report on a client’s website
activity based on the client’s objectives. Students identified and included
key performance indicators (KPIs) and a summary of their findings along
with recommendations for improvement. They incorporated visualizations
and graphics to best represent and accurately communicate important data

21. Vol. 4(2), 2018 Journal of Public Relations Education 67
and findings to the client. They used Excel and created a custom Google
dashboard for reporting.
Another project related to those found in the syllabus analysis
was found in the review of literature. Stansberry (2016d) created a five-
week project where her students worked in teams and used free tools
(e.g., Hootsuite, Google Trends, BuzzSumo, IssueCrawler) to identify key
publics and to conduct a content analysis, a social media audit, an online
social network analysis and content tracking, which her students rated as
valuable; they appreciated the applied, experiential lesson as something
that would help distinguish them from others applying for the same job in
the future.
Storytelling. Another prevalent digital analytic concept identified
by participants was digital storytelling, or the ability to look at data,
extract insights, and then present the data in a compelling manner. When
it comes to analytics, students need to integrate their critical thinking
skills with their storytelling abilities to share the data in a meaningful
way that connects with audiences. For example, PR professor Hilary
Fussell Sisco (2016) said, “I always want . . . students to visualize data.
Infographics and other visual tools to explain data makes it #munchable.
#PRAnalytics.” Zhang (2016a) tweeted, “Tell digital stories. Use live
videos. I am playing with @Animoto & PowerDirector. Love them very
much #PRAnalytics.” While Stansberry (2016b) commented, “Seems
counterintuitive, but writing & visual comm. Again, if you can’t give the
data meaning, it’s pointless. #PRAnalytics.”
Other concepts discussed during the Twitter chat included
understanding Excel pivot tables, functions, and formulas (n = 4 tweets)
and search engine optimization (n = 3 tweets). The Twitter participants
commented that students shouldn’t be “afraid of math” and should learn
how to use Excel to sort and analyze data.
Outcomes
RQ2 focused on understanding stated outcomes for courses that

22. 68 Ewing et al.
teach digital and social media analytics. Many outcomes stated on the
syllabi contained more generic wording with only 6% listing “KPIs” (n
= 2); 35% listing specific tools (n = 11); 10% listing “insights” (n = 3);
and 13% mentioning ethical implications (n = 4). The most frequently
mentioned analytics tools included Google Analytics (n = 11), Hootsuite
(n = 10), Facebook Insights (n = 6) and Twitter Analytics (n = 4).
RQ3 focused on understanding specific social media
communication methods that were used in courses. Results from the
content analysis of syllabi indicated that a class hashtag was the most
popular, with 26% of the syllabi incorporating this (n = 8). Based on the
syllabi, it was difficult to know if hashtags were used for synchronous
Twitter discussions or if they were simply used to categorize and share
online resources among the class. Additional required online interactions
noted on syllabi included participating in live-tweeting events, reading
and/or posting to a course or professor’s blog, tagging a professor in
tweets, and working to improve individual Klout scores. Only one syllabus
mentioned using a Facebook group, and none mentioned a required
Twitter list.
RQ4 focused on what ways professors were utilizing external
certifications to train students in analytics. Findings from the syllabus
content analysis indicated that the majority of courses did not require
students to complete an external certification that had an analytic element.
The 28% that did incorporate certifications (n = 9) primarily required
Hootsuite, Google Analytics, or Google AdWords. Results from the
Twitter chat related to RQ4 included three participants advocating Google
Analytics certification as one of the most valuable certifications in the
industry. Additional online resources mentioned on syllabi to supplement
classroom instruction included Code Academy, Google’s Analytics
Fundamentals, Khan Academy, Lynda and the Marketing Analytics
Initiative at Darden website.
RQ5 focused on the ways faculty utilized outside professionals or
organizations to help teach analytics. Based on the content analysis of the
syllabi, 66% of courses (n = 21) relied on outside professionals to share

23. Vol. 4(2), 2018 Journal of Public Relations Education 69
their expertise.
Also related to RQ5, the Twitter chat participants discussed the use
of several outside resources, including the Institute for Public Relations,
AEJMC, and other relevant academic or professional organizations. For
example, PR pro Mors (2016c) suggested, “The @InstituteForPR has
some great resources on website http://instituteforpr.org #PRAnalytics.”
Twitter chat participants also mentioned outreach to professors and
practitioners to serve as class speakers and/or to offer insight about
teaching digital analytics. Professor Rowena Briones Winkler (2016b)
said she wanted to “give a shout out to my @AEJMC_PRD friends” for
being “SO helpful, re: teaching help! #soblessed #PRAnalytics.” Further,
online tools such as Microsoft, Lynda, and Google Video were emphasized
during the Twitter conversation. Professor Matt J. Kushin (2016) tweeted,
that Microsoft has “an academic alliance program that provides many
tools.”
During the Twitter chat, several themes emerged for assignments
focused on teaching digital analytics, such as working with an actual
client, using dashboards, performing listening projects, and generating
reports. Educators stressed the importance of tying these assignments
to real-world clients. The responses indicated that these assignments
would give students realistic application by requiring them to submit
client-monitoring reports and to develop strategic-communication
recommendations based on insights gleaned from the data analysis.
Responses from students who participated in the Twitter discussion
indicated that assignments requiring the creation of a blog and the teaching
of SEO best practices helped them understand digital analytics and drive
traffic on their own websites.
During the Twitter chat discussion, both educators and practitioners
advocated for ongoing opportunities to access, mine, and analyze data.
These activities were thought to be key to creating an understanding of
digital analytics in the practice of PR. Professor Jamie C. Higdon (2016)
said, “Integrate analytics throughout educational journey. Require SMART
objectives and metrics plan for all major projects. #PRAnalytics.”

24. 70 Ewing et al.
Discussion and Conclusion
Incorporating digital and social media analytic training is a crucial
component of the future of social media education (Kent et al., 2011). This
study examined specific pedagogical practices identified within manifest
content on syllabi and in a Twitter chat among educators and practitioners
in order to explore current practices and standards for analytic training.
To address whether courses were meeting employers’ demand for
new analytic skillsets, it made sense to begin this study by examining
learning outcomes stated on syllabi. Outcomes are designed to set the
tone for a course and also identify the primary goals of student learning.
Therefore, looking at student learning outcomes stated on syllabi is
particularly important when examining an instructor’s approach to
teaching digital analytics.
With the growing efforts to measure and evaluate digital
activities, analytic competencies were a natural focus for social media
and digital communication courses. Thus, it was expected that courses
would have clearly identified learning outcomes for students related to
digital analytics. However, very few courses had outcomes specifically
mentioning analytics. While educators embedded analytic concepts and
training within their courses, the wording of their learning outcomes did
not reflect the focus on digital analytic competencies. For example, only
two of the syllabi reviewed mentioned KPIs, and only three mentioned
listening or insights, which are basic analytical competencies. This initial
finding indicated that, while analytics are taught in these courses, classes
might not be focusing on this area, resulting in the course outcomes often
ignoring or only leading to inferences about course expectations in this
area.
With the Commission on Public Relations Education report (Toth
& Lewton, 2018) identifying the value both educators and professionals
place on analytics and measurement competencies, it seems important for
educators to not only embed these competencies within courses but to also
explicitly identify them as a learning outcome that students will be gaining

25. Vol. 4(2), 2018 Journal of Public Relations Education 71
through these courses. The Twitter discussion among educators and
practitioners clearly conveyed the importance of public relations students
and graduates understanding digital analytics.
Based on feedback from practitioners, existing research, and
analysis of syllabi, the following are recommended learning outcomes
faculty might consider incorporating in their digital analytics course
syllabi:
1. To identify the importance of online data in strategic planning and
validating ROI.
2. To identify online influencers and the major users of various types
of digital and social media.
3. To use analytics tools and technologies to capture data, generate
reports and glean insights.
4. To analyze ethical implications associated with interpreting and
using online data.
5. To discuss the impact of digital and social media on relationships
between organizations and their stakeholders.
6. To evaluate how stakeholder engagement on social media channels
affects organizational operations.
7. To articulate definitions and measurements of social media
engagement and website traffic.
8. To apply basic numerical and statistical concepts to evaluate, plan,
and implement strategic digital tactics.
9. To apply concepts and theories in presenting findings and in
creating visualizations and dashboards to share with management/
client.
10. To become Hootsuite and/or Google Analytics certified.
One of the key areas that is suggested in social media education is
for faculty to help students understand professional uses of the platforms
(Kim & Freberg, 2016), including analytic information (Anderson &
Swenson, 2013). Recognizing this need, the current study examined
the ways in which faculty incorporate professionals into the classroom.
Numerous educators who participated in the Twitter discussion shared

26. 72 Ewing et al.
that they either taught a digital analytics course or included digital
analytic concepts in existing courses. The majority of syllabi indicated
that faculty were including professionals by bringing them in for guest
lectures; however, it was difficult to identify within the syllabi whether
these professionals specifically addressed topics of analytics or other areas
incorporated within the class such as campaign management, content
creation, or platform functions.
An area of growth between professional organizations and the
classroom has been the opportunity for student certifications on specific
platforms such as Hootsuite, Google, and HubSpot (Kinsky, Freberg, et
al., 2016). While this is an increasingly popular choice to help students
gain competencies, the authors were surprised to find only about a fourth
of the 31 syllabi mentioned an external certification as part of the course
requirements. In addition to previous literature pointing to the value of
certifications (e.g., Kinsky, Freberg, et al., 2016), three Twitter participants
mentioned the importance of external certification. The availability of free,
high quality, external training programs offered online (e.g., Hootsuite,
HubSpot, Google) makes it easier for educators to provide up-to-date,
industry-relevant preparation for students, and educators should take
advantage of these programs. We predict their inclusion on future syllabi
will increase.
Another key finding of the study is the lack of consistency in
resources on the subject of digital analytics, including required textbooks
and online sources. Syllabi included a wide range of industry books used
to teach students about the subject (see Table 1). This is, in part, due to
the content and structure of the course and whether analytics was the sole
topic or if it was only a smaller component of the social media or digital
curriculum. This inconsistency in required books is something that has
been noted in previous studies looking at the social media curriculum
(Kim & Freberg, 2016). Due to the nature of the rapid changes in the field,
educators have to frequently update their sources. Textbook and resource
choices are also impacted by where the class is being taught within a
university (e.g., marketing programs may use different textbooks than

27. Vol. 4(2), 2018 Journal of Public Relations Education 73
Title Times
mentioned
Likeable Social Media, Revised and Expanded: How
to Delight Your Customers
3
Measure What Matters 3
Groundswell Expanded and Revised Edition 2
Web Analytics 2.0 2
What Happens on Campus Stays on YouTube 2
AP Stylebook 1
Advertising and Public Relations Research 1
The Basic Practice of Statistics 1
Contagious 1
Cutting-Edge Marketing Analytics 1
Digital Marketing Analytics 1
Good Strategy Bad Strategy 1
How to Measure Social Media: A Step-by-Step
Guide to Developing and Assessing Social Media
ROI
1
How to Use Google Analytics the Tutorial 1
Maximize Your Social: A One-Step Guide to Building
a Social Media Strategy for Marketing and Business
Success
1
Measuring the Networked Nonprofit: Using Data to
Change the World
1
Mediactive 1
The Power of Visual Storytelling 1
Predictive Analytics 1
Primer of Public Relations Research 1
ProBlogger: Secrets for Blogging Your Way to a
Six-Figure Income
1
Table 1
Required Textbooks for Digital and Social Media Analytics Classes

28. 74 Ewing et al.
public relations programs). In addition to books, many syllabi included
references to required online articles, white papers, and PDFs, but few
syllabi specified titles of these resources.
For RQ3, in examining pedagogical practices to teach social media
and analytics, the authors examined other social media communication
methods professors had incorporated in their syllabi to facilitate online
interaction. Some of the interactions mentioned involved the use of a class
hashtag, Facebook groups, Twitter chats, Storify, and live tweeting.
Future Research and Limitations
This study explored basic questions related to pedagogical
practices and teaching social media analytics. In order to provide a
foundational knowledge, the authors examined the manifest content of
31 syllabi and a Twitter chat among 56 public relations practitioners and
educators. One limitation of the study is that the themes identified through
the Twitter chat were based on a small number of affirmative responses;
however, this is typical because of the dynamics of a Twitter conversation.
Title Times
mentioned
Real-Time Marketing & PR 1
Share This 1
The Social Current 1
Social Media Intelligence 1
Social Media Marketing 1
Social Media ROI 1
Socialnomics: How Social Media Transforms the
Way We Live and Do Business
1
The Signal and the Noise 1
Your Brand, the Next Media Company: How a Social
Business Strategy Enables Better Content, Smarter
Marketing and Deeper Customer Relationships
1
Table 1 (continued).

29. Vol. 4(2), 2018 Journal of Public Relations Education 75
Participants are unlikely to tweet the same theme to minimize repetitive
content. Another limitation to using the chat data is that people who valued
the topic were more likely to participate in the Twitter chat than people
who were disinterested or didn’t value it.
Future studies may consider in-depth explorations through
discussions with the faculty who are teaching the courses. Future studies
could incorporate a mixed-method approach involving focus groups
and interviews with professionals to determine if these digital analytics
assignments were effective in preparing students for their new roles,
perhaps following the methods of Gallicano, Ekachai, and Freberg’s
(2014) study of an infographic assignment. In addition, testing the
effectiveness of certification programs (e.g., Kinsky, Freberg, et al., 2016)
for analytics could be beneficial as well.
Educators can also integrate and test how certain assignments are
implemented and accepted within digital analytics by using guidelines
and frameworks accepted in digital analytics associations and professional
circles. Many frameworks, like the ones proposed by AMEC and DAA,
can be integrated and used in current courses for lessons and used as
inspiration to create assignments for students to test their knowledge
and application skills in digital analytics. Further research could explore
classes that use a specific framework for assignments and those that do
not, and compare the end results. In addition, interviews with digital
analytics professionals who are a part of these associations could be
explored in future research to determine what they feel are key areas to
emphasize, growing trends, and challenges and opportunities in the field.
Although course syllabi provided a general overview, often
information seemed missing or vague. It does not mean faculty failed to
incorporate certain pedagogical practices in their classes; their absence
may indicate that they were simply not shared through the syllabus,
and this could have been done with the purpose of keeping the class
nimble as technology changes. Future researchers can learn from and
anticipate coding challenges encountered in this foundational study.
Direct conversations with professors would allow more specific details

30. 76 Ewing et al.
of each course’s content to be explored. In addition, in-depth interviews
with practitioners who are experts in this area would allow for deeper
exploration of digital analytics concepts, tools, techniques, and resources
that could be used to teach the subject.
Social media pedagogy, especially exploring digital and social
media analytics, is one of the emerging research concentrations that will
help align the public relations profession and education community for
the foreseeable future. A bridge can be created to help teach digital and
social media analytics for both educators and professionals to agree on,
for the sake of the young professionals entering the workplace. Like most
research concentrations and perspectives within a discipline, identification
of future directions, questions, and calls-to-action must be recommended
to address some of the growing challenges and opportunities involved
when it comes to social media pedagogy, especially in the area of teaching
digital analytics.
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37. Vol. 4(2), 2018 Journal of Public Relations Education 83
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University Name Course Names
Biola University • Social Media, SEO and
Digital Strategy
Carnegie Mellon University • Digital Marketing Analytics
Cleveland State University • Media Metrics and Analytics
Elon University • Strategies for Emerging
Media
University of Florida • Consumer and Audience
Analytics
• Introduction to Social Media
• Social Media Skills
University of Georgia • Public Relations Research
• Social Media Analytics,
Listening and Engagement
• Coding for Interactive Media
Iona College • Applied Communications
Research
Kent State University • Digital Analytics for Ad and
PR
• Public Relations Online
Tactics
Louisiana State University • Public Relations and Social
Media Strategy
Loyola University • Audiences and Distribution
University of Maryland • New Media Writing for Public
Relations
Massachusetts Institute of
Technology (MIT)
• Digital Marketing and Social
Media Analytics
Appendix
University Syllabi Used for Coding

40. 86
Editorial Record: Original draft submitted to JPRE March 30, 2017. Revision went
under review August 7, 2017. Manuscript accepted for publication October 8, 2017. Final
edits completed July 20, 2018. First published online August 17, 2018.
University Name Course Names
Ohio Northern University • Social Media Principles
University of Oregon • Social Media Insights and
Measurement
• Analytics and Adwords
New York University (Stern
School of Business)
• Coding for Interactive Media
San Diego State University • Digital and Social Media
Analytics
University of Southern
California
• Data Analytics Driven
Dynamic Strategy &
Execution
• Digital Analytics
University of South Dakota • Internet Marketing and
Communication
Syracuse University • Social Media Theory and
Practice
• Using Big Data and Analytics
(Maymester Course)
Texas Christian University • Social Media Measurement
University of Virginia • Marketing Analytics
West Texas A&M University • New Media
• Seminar in Media
Innovations and
Management
Ewing et al.
Appendix (continued)
University Syllabi Used for Coding