Tag Archives: social media analytics

Teaching Social Media Analytics in PR Classes: Focusing on the Python Program

Editorial Record: Submitted June 4, 2022. Revised October 21, 2022. Revised January 8, 2022. Accepted January 26, 2023. Published May 2023.


Kim, Seon-Woo
Ph.D. Candidate
Manship School of Mass Communication
Louisiana State University
Email: kr.seonwoo@gmail.com

Chon, Myoung-Gi, Ph.D.
Associate Professor
School of Communication and Journalism
Auburn University
Email: mzc0113@auburn.edu

The teaching brief introduces what to teach and how to teach social media analytics for PR educators in a university. It suggests a semester-long curriculum for an independent research method class for both graduates and undergraduates. First, we discuss why students can better learn programming languages over industrial platforms. In addition, we compare three different ways of data collection (crawling, API, and download) and discuss the pros and cons. Then, it presents (1) data collection through API, (2) text mining, and (3) network analysis with the shared Python code on GitHub and the step-by-step tutorial for PR educators who are unfamiliar with programming languages. This brief is expected to help to bridge the gap between the growing demands of programming-based analytics in PR practice and education.

Keywords: Social media analytics, Pedagogy, Python, API, Text mining, Network analysis

Social media has become integral to digital public relations (Ewing et al., 2018). PR companies perceive social media analytics (SMA) as a useful tool to identify who is a target public, understand the current environment around an organization, measure PR campaign outcomes, build relations with stakeholders and influencers, and many more (Kim, 2021). Responding to the growing demands of social media analytics in the PR industry, analytics curricula in PR programs need to be developed to educate PR students (Commission on Public Relations Education, 2018). 

However, public relations educators have faced challenges in learning and teaching social media analytics. Most PR instructors have not had an opportunity to learn computer programming knowledge for analytics during their academic careers, such as Python or R. Moreover, teaching analytics requires understanding new methodologies and data types, such as natural language processing, network theory, and deep learning. Given this background, the current analytics classes in PR programs mostly focus on conceptual knowledge and the use of other commercial tools. 

For example, students have learned how to use proprietary platforms, such as Brandwatch and Sprinklr, and interpret the results on those platforms. It is also common for instructors to ask students to get certificates in Google Analytics and Hootsuite as evidence of their analytics competence (Ewing et al., 2018). Despite the above efforts, PR professionals recommend that PR graduates have programming knowledge for PR work automation and tailored PR services to clients (Szalacsi, 2019; Trafalgar Strategy, 2022). Heavy reliance on industrial analytics platforms would limit students’ SMA competency within the platforms’ modality, thereby preventing them from developing advanced analytical abilities.   

To fill the gap, this teaching brief aims to provide a pedagogical foundation for utilizing Python as an SMA tool. Particularly, this teaching brief explains an SMA class material based on data collection, text mining, and network analysis. We provide Python codings that PR educators can use in classrooms to teach Python programming language. Python is the most frequently used programming language in data science (TIOBE, 2022; Woodie, 2021). The Python codes are designed as simplified as possible for a PR analytics introduction class. We provide step-by-step instructions about the Python codes to help readers understand and follow the programming function. This teaching brief is expected to encourage programming-based SMA classes in public relations classes.

Teaching Objectives

Table 1 summarizes the learning objectives of what this teaching brief delivers and required Python packages. This brief consists of three parts: data collection, text mining, and network analysis. First, students are expected to obtain knowledge about Tweet data collection through API. Because APIs tend to provide free versions and have similar ways of use across social media platforms (e.g., Facebook, Instagram), the practice of collecting tweets via API helps students equipped with social media data collection skills for various SNSs without cost. In addition to this, we introduce ways to find content published by influencers and popular tweets. Lastly, students can learn to save the collected data as a spreadsheet format (i.e., xlsx).

Table 1: The Overview of Learning Objectives

 Learning outcomesPython package
Python downloadhttps://github.com/formulated/PR_education_Python 
Data collection– Learn how to apply for Twitter Academic Research Access
– Apply a Twitter access code to Python
– Create search query, including keyword and date
– Collect Tweets through the shared Python code
– Sort out tweets by the number of likes, retweets, and followers
– Save the collected data as the excel format on your local computer
– pandas
– twarc
– os
– requests
– time
Text mining– Load the collected Twitter data
– Text data cleaning
– Create Word cloud
– Calculate word frequency and visualize text mining results
– pandas
-nltk.corpus – re
Network analysis– Create data for network analysis from the collected Twitter data (i.e., mention relation and retweet relations)
– Network object generator
– Generate a network graph
– Export network data for visualization on Gephi
– Calculate various centrality scores
– pandas
– networkx

Next, students can apply text mining and network analysis to their own data collected by API. Through the text mining section, students learn to load and clean data, and create a word cloud and calculate word frequency with its visualization. The network analysis section introduces a simple conceptual understanding of network data, how to construct network data, how to calculate centrality scores, and visualization preparation through Gephi, a popular network visualization tool utilized in academia and industry. 

Teaching Preparation

To use the shared Python code for teaching, educators need to have some basic Python skills. Also, educators and students must install some Python applications and packages. Python code for this teaching brief is available on GitHub (https://github.com/formulated/PR_education_Python). We assume that readers have already installed Python 3 (https://www.python.org/downloads/) and Jupyter notebook (https://jupyter.org/install). Python is a programming language, and Jupyter is a web-based interactive computational environment for Python programming. If not, we recommend installing Anaconda (Anaconda, n.d.), which includes Python 3 and Jupyter Notebook. After installation of Python 3 and Jupyter, launch Jupyter and open the shared code in a new notebook. 

Then, readers need to install the required Python packages, such as pandas and networkx, for each Lesson (see Table 1). 

When running the shared Python code without installing required packages on a computer, it will show the following message “ModuleNotFoundError:no module named ‘XXX’.” Module is synonymous for package in Python. 

To install a Python package on Mac, open Terminal application and type “pip3 install [package name]”. For example, below is the command for installing pandas (see Figure 1). 

Figure 1

On Windows, open Anaconda Prompt program and type “pip3 install [package name]” as below (see Figure 2). 

Figure 2

This short brief cannot cover every single Python code one by one. Instead, we focus on which codes should be edited to properly run the Python code and serve different learning objectives in classrooms. For example, some classes would focus more on organizational PR while others political PR. In this case, the code may require unique search keywords depending on the subject. 

PR educators should have some basic knowledge in Python (e.g., installation, running code, basic built-in functions) prior to giving students a demonstration of the shared Python coding and modifying the codes for class projects and activities. To develop this basic proficiency, we recommend Python books for beginners (e.g., Codeone Publishing, 2022; Matthes, 2019) and freely-available online resources from YouTube, such as Learn Python in 1 Hour (Programming with Mosh, 2020, September 16). Also, online Python bootcamp courses, such as DataCamp (https://app.datacamp.com), are valuable resources for PR educators and students as they provide interactive web environments of Python for beginners. PR educators may connect the online bootcamp course to a part of their SMA course curriculum as assignments or pre-class activities. 

Lesson 1. Data collection from Twitter through API

There is nothing to analyze without data. Data collection is the start of extracting valuable insight from analytics before moving to gather information by organizing the data. Thus, among many required skills, data collection is the foundation of SMA (Kent et al., 2011). Growing PR jobs require data collection skills from the web and social media (Meganck et al., 2020). Before digitized public relations, PR practitioners had to manually scan and gather the environment around an organization, such as news monitoring and clippings. However, today’s digital society creates a massive number of user-generated contents about organizations on the web and social media, which makes it nearly impossible for PR practitioners to collect them manually. 

There are three main ways to collect web data: crawling, API, and downloading from industrial platforms. Table 2 compares the data collection methods. Web crawling, or scraping, refers to a mechanical collection of web data (e.g., text, image, sound, and video). A web crawler automatically extracts data from a website based on programming. Technically, it is possible to crawl data using freely-available packages in Python for most web pages, such as social media, news media, and web communities. These packages can be implemented for news clipping and issue/crisis monitoring as a daily PR practice.

Table 2: Comparison of Data Collection Methods

Level of difficultyDifficultModerate – difficultEasy – moderate
PriceFreeFree or paidPricy
Data accessibilityPartialFull or partialFull or partial
VariablesLimited – SomeSomeMany but blackbox

Writing crawling programming requires advanced programming language and web structure knowledge such as HTML, HTTP, and CSS. Also, they should be updated whenever a website changes its layout and structure. In addition, social media companies present limited, personalized feeds and content to each account based on their algorithms and other variables (e.g., follower network, search history, location). Thus, a web crawler often cannot access the full-archived data because it can only collect data visible on the website, which may raise content representativeness issues. A crawler also cannot get invisible metadata and variables that a social media company provides to API and industrial platforms, such as user profiles (e.g., when an account was created) and metadata (e.g., the name of the app the user posted from). If necessary, you have to construct variables from crawled data. Crawling may face some legal issues if you do not get an agreement from a social media company prior to collecting the data.

Another way to collect data from social media is to use API (application programming interface). Many software companies provide API to let other third-party services and programmers use their service in a convenient way. For example, Apple and Google use weather API to provide weather services to customers without collecting weather data by themselves. Major social media platforms (e.g., Twitter, Meta) also provide API for users to collect data within the companies’ policy and authentication. Thus, it is relatively easier and safer to collect social media data compared to crawling because it is free of committing a violation of a website’s Policies and Terms of Service. 

Free version APIs usually have a basic data access level like a trial version, providing limited requests that you can make within a day and shorter historical data. There are paid API services with more or full-archived data access and functions. Major social media companies have opened their premium API for research and education purposes. For example, Twitter allows researchers to access the full tweet archive through Twitter’s Academic Research Access (Twitter Developer Platform, n.d.). After filling out an application and it being accepted, Twitter will provide an access code. Currently with API access, ten million tweets can be collected per month. Meta also runs CrowdTangle, where PR educators can access Facebook, Instagram, and Reddit data. APIs present some variables, such as message type (e.g., retweet, original), the number of engagements (e.g., likes, shares, comments), and geographic location.

Lastly, industrial platforms, such as Brandwatch (https://www.brandwatch.com/) and Sprinklr (https://www.sprinklr.com/), allow paid subscribers to download social media data from their platforms. Click-based user interfaces do not require programming. However, those platforms are pricey because their business model is B2B with governments, companies, and universities. Due to high prices, a few universities are not subscribing to those services for teaching and research purposes. If a department already subscribes to such a service, they are a good resource for PR analytics teaching. Like API, there are no legal issues in data collection and use within the companies’ policy and authentication, and many industrial platforms provide full historical archive access. Industrial platforms also provide a rich amount of metadata, such as users’ gender, sentiment, and users’ profession or organizations (e.g., journalists, politicians). However, it is not clearly known how the data resellers construct those variables for users (i.e., blackbox). Although some companies provide explanations about their variable construction, researchers typically cannot replicate the variables due to limited information.

Given the pros and cons of the three data collection methods mentioned above, this teaching brief introduces how to collect Twitter by using the Twitter Academic Research Access API. Because most major companies maintain Twitter accounts, and their contents are publicly available, a few researchers and PR practitioners choose Twitter for real-time issue monitoring and reputation management (e.g., Chon & Kim, 2022; Rust et al., 2021). In addition, data collection with API and Python is similar across social platforms. If educators and students understand the code for Twitter data collection, the code can be adjusted to get data from other platform APIs. 

Tutorial. Data collection

The teaching brief here shows how to collect Tweets by using Twitter API and Python. Twitter allows researchers to access the full tweet archive through Twitter Academic Research Access (Twitter Developer Platform, n.d.). After filling out applications, including research interest and affiliation, Twitter gives users access codes to collect ten million tweets per month. 

To run the Python code from the GitHub (Kim, 2022) that the author has created, you need to change the OAuth 2.0 Bearer Token (i.e., credential key or password for Twitter) and the query parameters (e.g., search keyword, date). The Bearer Token is given after achieving Twitter Academic API permission. In the below code line, the coder would insert their Bearer Token. The Bearer Token format is a long combination of alphabets and numbers (see Figure 3).

Figure 3

In query parameters, query indicates search keywords. Hashtags (i.e., #) and mentions (i.e., @) can be used as a search query (e.g., @PR, #PR). Tweet.fields indicates which variables are collected. The coding includes user numeric IDs (i.e., author_id), timestamp (i.e., created_at), and public metrics (i.e., retweet, reply, like, and quote). Also, data period should be set in start_time and end_time. If the code is run, tweets will be collected in excel data format (see Figure 4). 

Figure 4

We use this data for basic text mining and network analysis. There is a code for exporting the below data as an excel file (see Figure 5). 

Figure 5

[Figure 5 Should Be Here]

Next, because the data has variables such as the number of likes and retweets, it can figure out which tweets have the most engagement. Also, the number of tweets posted by user accounts indicates who are active and potentially stimulated publics on social media. Sorting users by the number of followers results in a list of influencers around a topic. 

The code below filters the top 10 most-retweeted tweets (see Figure 6). To get the most-liked tweets, a variable name in sort_value parameter should be changed (e.g., from ‘retweet_count’ to ‘like_count’). Additional codes filter users who wrote tweets the most about the issue and users with the highest number of followers. Depending on PR campaigns and activities, practitioners would edit to yield other valuable information. For example, combining these metrics with the time variable (i.e., created_at) may produce the best time/weekdays to post a social media posting. Practitioners may summarize weekly engagement with publics from social media campaigns by summing or averaging likes, shares, or the number of replies.

Figure 6

Lesson 2. Text mining 

Text mining (i.e., computational text analysis, natural language processing) is one of the most promising areas in public relations for listening to publics and stakeholders. Digitized communication environments continue to create an unlimited number of digital texts. Knowledge discovery from text data is recommended to increase an organization’s performance and efficiency beyond data retrieval. Excellence theory posits that listening to publics is more important than disseminating information (Grunig & Grunig, 2009). When PR practitioners instill publics and stakeholders’ voices into an organization, it can make effective strategic communication, which contributes to organizational success (Kim & Rhee, 2011). 

There are also many possible ways for text mining to assist public relations practices, such as topic discovery and opinion mining. For example, the topic analysis provides insights about the main topic, issue, and trend around an organization based on descriptive analysis (word frequency, co-occurrence) and algorithm (e.g., topic modeling). Opinion mining, or sentiment analysis, can be used to investigate reputations of an organization and a brand, issue, and crisis (Liu, 2011).

Text mining covers collection, preprocessing, analysis, and summary of text data based on mathematical algorithms. Analyzing a large amount of unstructured text requires different statistical methods and tools (Grimmer et al., 2021). For example, texts are unstructured, unlike traditional structured data (e.g., data in excel), so data cleaning is necessary to transform them into a structured format. Conventional statistical tools, such as SPSS and SAS, provide a limited text mining function, as originally designed to analyze structured data. Hence, programming skills in Python and R are preferred for text mining.

Tutorial. Text mining

In the shared Python code, text mining includes (a) loading the Tweet data, (b) text data cleaning (e.g., low transformation, stopwords removal), (c) word cloud, and (d) word frequency calculation and visualization. Also, this code can be used to analyze other text data from social media and other web pages if a data structure is the same (i.e., data with the same column names). Otherwise, the column names in other data should be edited. The first task for text mining is to load data (see Figure 7). For this example, the code imports the excel file collected through the Twitter API. Pandas is one of the best Python packages to load, preprocess, and analyze data. The pandas package is imported with the abbreviated name, pd, with the following code, “import pandas as pd” in the first code cell.

Figure 7

The next step is text cleaning, or preprocessing. Though any data needs some level of data cleaning before analysis, text data requires more effort in preprocessing due to the complexity of human language. User-generated content tends to include noise elements such as emojis, URLs, and stopwords. It is recommended to remove irrelevant elements for analysis purposes to improve computational efficiency and validity (Hickman et al., 2020; Welbers et al., 2017). Stopwords are functional words that have no substantial meaning, such as article (e.g., the, a, an), conjunctions (e.g., and, but), and prepositions (e.g., of, in) (see Figure 8).

Figure 8

Also, as computers are case-sensitive (e.g., computers cannot identify Computer and computer as having the same meaning like a human), text data are often converted to lowercase before analysis. Beyond the simple steps, there are different types of text cleaning methods, such as stemming/lemmatization, dimensionality reductions, bag-of-words, Word2vec, and so on. Text cleaning depends on which type of algorithms would be used and what the purpose is. The shared code removes URLs, emoticons, special characters (e.g., !, @), and stopwords. 

Next, a word cloud is created to visualize the contents. A word cloud is one of the most frequently used visualizations in text mining. It is similar to the descriptive analysis in statistics (e.g., mean, sd). A word cloud is often seen as a preliminary analysis in PR-published papers (e.g., Plessis, 2018; Macnamara, 2016). The size of word fonts is proportional to the word frequencies. The generated word cloud in the example shows that rt, new, year, happy, and prsaroadsafety are prominent in the text data (see Figure 9).

Figure 9

The next code calculates a word frequency and sorts the result in descending order by frequency. Word frequency generates insightful information, such as daily/weekly issues around an organization (see Figure 10). Also, a PR practitioner may evaluate a campaign’s performance by tracking the relevant hashtag frequency over time.

Figure 10

The last code is to make a word frequency visualization. If the index (e.g., from the current 0:20 to 0:50) is changed, the number of words in the graph will accordingly change (see Figure 11).

Figure 11

Lesson 3. Network analysis
Network analysis is gaining much popularity in public relations (Yang & Saffer, 2019). Network analysis deals with “structure and position” (Borgatti et al., 2013, p. 10). The network actor is an individual, group, organization, or inter-organizations. For example, companies have different types of relations (Borgatti et al., 2013), such as similarities (e.g., type of business), business relations (e.g., joint venture, alliance), interactions (e.g., trade), and flows (e.g., technology transfer). Network analysis has been applied to various PR topics such as organization-public/stakeholder relations, employee communication, crisis communication, and CSR (Yang & Saffer, 2019).

Centrality, the classical structural properties of a network, is one of the most commonly used concepts for network analysis and visualization (Freeman, 1978). A few PR studies have used centrality to investigate key publics/stakeholders (Hellsten et al., 2019; Himelboim & Golan, 2019), issues management (Sommerfeldt & Yang, 2017), agenda-setting (Guo, 2012), content diffusion network (Himelboim & Golan, 2019), and CSR performance (Jiang & Park, 2022). 

Also, network analysis can be combined with text mining to figure out how words occur together in text. Specifically, PR practitioners can illustrate brand images and salient issues of an organization by looking at co-occurrence results with the organization name (Gilpin, 2010). In addition, PR practitioners identify a community network (e.g., friends, followers) around influencers and target them to encourage them to pay attention to the PR campaign, which, in turn, may motivate the influencers to share the content (Zhang et al., 2016). Another possible application of network analysis for PR is to identify potential publics who show several advocacy activities with positive sentiments toward a relevant issue but not yet toward a client’s issue. Organizations target them to foster supportive postings on social media.

Tutorial. Network analysis

Loading data is the same in the text mining section. Because network analysis is based on relations, data should have relational information. Relations are expressed in many different ways. You may construct a relationship variable between organizations and/or publics from outside social media data, such as joint ventures, alliances, and NGO coalitions. You may also infer relationships from social media data. For example, follower-following relationships are a relationship example. If User A follows User B, you may use the relationship information for network analysis (e.g., User A → User B). Likewise, if User A mentions or retweets a User B’s tweet, you may set a tie from User A to User B. The tie direction could be reversed depending on your perspective. For example, some people think that the relation should be User B → User A when User A retweets User B’s tweet because User B’s information flows into User A. The example code shows how to make mention relations. “From” indicates users who mention a certain account, while “to” is a mentioned account by “from.” If you want retweet relationships, replace the red text in the first line (i.e., the regular expression) in the below code with r “RT @([A-Za-z]+[A-Za-z0-9-_]+)”. If so, the data indicates that users in the from column retweet posts generated by a user in the to column (see Figure 12).

Figure 12

The following screenshot shows two codes: network object generator (i.e., G) and its visualization in Python (see Figure 13). If there are more than a few nodes (i.e., actor) and edges (e.g., relation), Python network graphs are not visually attractive. Instead, a few researchers use other visualization tools such as Gephi (e.g., Raupp, 2019; Yang et al., 2017). The software is free to use on Windows and Mac (download and see in detail at https://gephi.org).

Figure 13

The code in Figure 14 transforms the network data into an excel for Gephi. To import the excel spreadsheet on Gephi, click file → import spreadsheet → open excel file (Gephi_df.xlsx) → import as “Edges table” in general excel options → finish.

Figure 14

Here, n indicates the number of the relations (i.e., how many times a source mentions a target on Twitter). Compared to a Python graph, Gephi generates visually attractive and easy-to-understand network graphics (see Figure 15).  

Figure 15

Centrality is one of the most frequently used metrics in network analysis. There are many different types of centrality, such as in-degree/out-degree centrality, betweenness centrality, eigenvector centrality, and so on. In the shared code, the NetworkX Python package provides different types of centrality calculations. See more network algorithm parameters at NetworkX (n.d.). For example, when “degree_centrality” in the below code is replaced with “betweenness_centrality,” it generates between-centrality scores for each node (see Figure 16).   

Figure 16

Suggested Curriculum

If an introductory level SMA course is provided within a semester of 16 weeks, it is possible to design the courses as in Table 3. It is critical for students to type and edit the shared codes rather than just read or see them in order to achieve the learning objectives in this teaching brief. The suggested curriculum, therefore, focuses on hands-on experience for PR SMA with Python. The first week introduces the course. Then, the next two weeks teach PR in the digital era, social media and its application in PR, and the SMA case study. After the conceptual understanding of SMA, two weeks would be required to teach each practical programming section: Python basic, data collection, text mining, and network analysis. Finally, the remaining weeks will be used for final projects and presentations. Considering students’ abilities and prerequisite courses, the curriculum would be adjusted to serve unique class demands.

Table 3: Example of PR SMA Course Curriculum

1Introduction– Introduction to Course and Python
– PR in the digital era
– Social media and its application in PR
– Understanding social media analytics
3-4Python programming– Installation and Setup of Python and Jupyter Notebook
– Installing Python packages
– Reading and writing data (e.g., XLXS, CSV)
– data types (e.g., list, dictionary, tuple, JSON)
– Pandas data structure
– Data cleaning (e.g., data selection, merge, recode)
– Basic functions (e.g., define, for, if-else, while)
5-6Data collection– See learning outcome in Table 1 for programming contents
– Three different ways of data collection: crawling, API, and industrial platform)
– Introduction to data collection with API
– Data collection assignment
7-8Text mining– See learning outcome in Table 1 for programming contents
– Conceptual understanding of text mining
– Text mining assignment
9-10Network analysis– See learning outcome in Table 1 for programming contents
– Conceptual understanding of network analysis
– Network analysis assignment
11-13Applications of social media analytics– SMA case study – Social media metrics and evaluations
– Social media campaigns based on SMA
14-15Final project– Final project introduction
– Group Work days
16Student presentation– Final project presentation

What if educators can’t offer a separate class focusing on social media analytics and PR? We suggest a short course in a PR research class. Generally, PR research classes should cover many topics, such as qualitative research and quantitative research. However, research methods in the digital age should teach how to use social media to solve PR problems. PR educators may suggest multiple research methods using qualitative skills (e.g., focus group interview), quantitative skills (e.g., survey), and social media analytics through Python (e.g., text mining and network analysis). Students will be allowed to analyze unstructured data by choosing between text mining and network analysis.   

Assessment of Student Learning

Simply put, students can be assessed via three assignments (15% each worth of final grade) and a final group project (45% worth of final grade) with the remaining 10% points (e.g., attendance) for a semester class. 

Regarding the data collection assignment, students are required to submit a Python code file edited to collect tweets via their search queries. If it works without error, they get full credit. Instructors would consider extra credit when students collect data from other social media or web crawling. The text mining assignment asks students to submit a text mining Python code to create a word cloud and word frequency visualization with the collected data through the data collection assignment. In addition, students would be required to submit a document file analyzing the text mining results, as editing a few codes is too easy of a task for 15% credit. If students conduct additional analysis, such as sentimental analysis and topic modeling, they can be given extra credit. Likewise, network analysis would require a Python code of edited network analysis and a report. Network analysis assignments get extra credit when students present network visualization through Gephi beyond the suggested code. 

Lastly, the final project is group work with a team of three members. Students select a big organization (e.g., S&P 500) so that students can collect large enough social media data. They are asked to conduct (1) traditional formative research, (2) data collection, (3) text mining, (4) network analysis, and (5) social media campaign plan. Table 4 presents an example of the final project rubric. 

Table 4: Final Project Rubric

CriteriaContentsWeight (%)
Traditional formative research– Organizational history & mission
– Industry background & trend
– Identification of stakeholder, public, and society
– Traditional news media analysis
– SWOT analysis
Data collection– Social media data collection (e.g., tweets, Facebook)
– Identification of popular social texts
– Identification of key individuals (e.g., influencers)
Text mining– Main topics about company, brand, or products
– Sentiment analysis
– Text mining visualization (e.g., word cloud)
Network analysis– Identification and network positions of key public and stakeholder
– Network visualization with Gephi
Social media campaign planning– Discussion of current PR-related problems from formative research and social media analytics.
– Making three social media assets/tactics with target audiences
– Presentation of expected outcomes and impact on stakeholders, public, and society and measurement plan of campaign success

This project allows students to have a chance to apply the skills and knowledge they learn from the suggested SMA class in practice. Through the final project, they would realize the necessities of SMA along with traditional PR formative research (e.g., media coverage). The final project would also be adjusted if students in the class did not take a PR strategy or campaign class.


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© Copyright 2023 AEJMC Public Relations Division

To cite this article: Kim, S. and Chon, M. (2023). Teaching Social Media Analytics in Public Relations Classes: Focusing on the Python Program. Journal of Public Relations Education, 9(1), 117-146. https://journalofpreducation.com/?p=3663

Teaching Digital and Social Media Analytics: Exploring Best Practices and Future Implications for Public Relations Pedagogy

Editorial Record: Original draft submitted to JPRE March 30, 2017. Revision went under review August 7, 2017. Manuscript accepted for publication Oct. 8, 2017. Final edits completed July 20, 2018. First published online August 17, 2018.


Michele Ewing Photo by David LaBelle

Michele E. Ewing, Kent State University

Carolyn Mae Kim, Biola University

Emily Kinsky

Emily S. Kinsky, West Texas A&M University

Stefanie Moore

Stefanie Moore, Kent State University

Karen Freberg

Karen Freberg, University of Louisville

Teaching Digital and Social Media Analytics: Exploring Best Practices and Future Implications for Public Relations Pedagogy


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.

Key words: social media, social media analytics, public relations education, digital analytics

Teaching Digital and Social Media Analytics: Exploring Best Practices and Future Implications for Public Relations Pedagogy

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 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 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 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.


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 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, 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.

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 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 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?

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?

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?


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 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 A).

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 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 Appendix B). 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 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


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) 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.

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 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 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.


RQ2 focused on understanding stated outcomes for courses that 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 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 public relations. Professor Jamie C. Higdon (2016) said, “Integrate analytics throughout educational journey. Require SMART objectives and metrics plan for all major projects. #PRAnalytics.”


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 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 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 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.

Table 1
Required Textbooks for Digital and Social Media Analytics Classes
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
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

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. 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 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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University Syllabi Used for Coding

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
Ohio Northern University
  • Social Media Principles
University of Oregon
  • Social Media Insights and Measurement
  • Analytics and Adwords
New York University (Stern School of Business)
  • Social Media and Digital Marketing Analytics
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