As many of you know, this past summer was an especially exciting and busy one for me with the birth of my daughter!
Since becoming a dad, I’ve learned a ton about time management and still have a ton more to learn. I know it is going to be a bit more of a challenge to keep up with this blog in the semester ahead. But I am going to work hard to keep the posts coming. I have a lot of ideas for posts that I didn’t get to last semester, including discussing Ketchum’s Mindfire program, a personal branding assignment based on Karen Freberg’s book “A Roadmap to Teaching Social Media,” an exercise I did about Katy Perry and influencers, and the new message map activity my campaigns students did last year.
The big change that I am very excited about this year is that we’ll be adding Meltwater to my class. If you’re not familiar, Meltwater is a media intelligence software platform. While the software offers media relations tools, we’ll be focusing on its social media listening capabilities.
Meltwater has recently launched a university program providing educators and their students free access to their software in PR, social media and marketing programs. I’m excited and thankful that my students at Shepherd University will be among the first universities to be participating in this program. Programs like these are important for our students to gain hands-on experience with leading industry tools.
I had a tour of Meltwater this past summer and immediately had several ideas on how it could be very valuable to my classes. But, with so much going on, I’m going to start this semester with using it only in my social media class.
Inside the Meltwater software, one can find a slew of training videos to quickly learn how to use the software. I personally found it pretty easy to pick up as much of it is self-explanatory.
Participation in the Meltwater university program provides access to training videos, an assignments portal and in-class training via video lecture.
Carol Ann (Funkhouser) Vance, director of university relations for Meltwater, will be Skyping in with my class on Thursday to give us an intro to the software and provide training to students.
In the next post I will discuss how we’ll use Meltwater in my social media class.
Before I provide the syllabus to my social media class, I’d like to mention a few more quick notes about the syllabus. I am very happy with the book choices from last year. I will be sticking with them. Students will be reading:
Also, we will be continuing to participate in the Hootsuite university program, which is now part of Hootsuite Academy.
I’ll be offering extra credit to my students who choose to complete a Facebook Blueprint assignment I created. In short, this assignment asks students to complete several but not all of the Facebook Blueprint lectures. I do not ask students to complete all of the lectures or to complete the certification as it is rather expensive. With paid being an important part of the social media mix, it is important for us to offer our students more experience.
I offered Facebook Blueprint as an assignment in my writing across platforms class last semester. But I’ve decided that this year, I’m going to go in another direction. So, I want to provide students an incentive still to get this education.
This post is a quick reminder that, as always, during the summer months I will be toning back my frequency of posting on this blog. But don’t you worry! I’ll be picking up full steam with my regular publication schedule of posting every 2 weeks during the academic year.
This blog tends to get a lot of traffic during the summer with readers looking for assignments and syllabi as they work on updating courses for next academic year. So I’d like to point out a few resources below. But first…
A few Recommended Blog Posts, Content & Articles
My four part series detailing a social media analytics assignment I used in my Communication research class this past semester. Start here.
If you are looking for assignments and syllabi, you’ll see that I’ve written about many of my assignments and included the assignment documents themselves.
To access those select the green menu bar at the top titled “Blog topics” -> “Teaching Social Media” -> “Classes” and then select the class.
Syllabi can be accessed either via the “Syllabi” menu at the top of this blog and selecting the course, or by selecting the “Teaching Materials” menu and navigating to an external document repository to access all my uploaded syllabi.
If you have questions about any of the assignments or syllabi, please do not hesitate to send me a Tweet. I’d be happy to chat.
What’ll I be doing this summer?
Let’s just say it’s going to be a life-changing summer. Follow me on Instagram & Twitter to find out. 😉
This is post #4 in a four part series about a new assignment that I used this semester in my Communication research class (all posts on that class).
That assignment is a 3-part social media analytics project. Each part is related but unique, allowing students to pick up a new skill set. In this post we’ll discuss part 3 of the assignment. If you haven’t read the assignment overview post, and the earlier post about pivot tables in Excel or my other post on this assignment about Microsoft Social Engagement, I encourage you check those out. In the first post, you will see a copy of the assignment that is discussed below.
This last part, part 3 of the assignment, asks student teams to do basic network mapping of their client using Netlytic.org.
Why Teach the Basics of Social Network Analysis?
This semester, I wanted my students to get exposure to, and a basic understanding of, social network analysis. I am not a social network analysis. But it is something I find fascinating. And I think it is important that as professors we have at least a basic knowledge of this field and that our students do as well.
I feel this way in part because of the rise of professional tools that social media professionals have for visualizing social media data. While several of the questions that can be culled from the work students did in the portion of the project described below could also be answered through other means – such as Microsoft Social Engagement -, learning about social network analysis and having this experience offers a different and valuable way of understanding who is connected to whom within a network.
With the above said, the below-described project provides students a chance to visualize a social network and see how different actors relate to one another around a specific topic on Twitter or Instagram. Students explored the conversation around their client’s Twitter and/or Instagram account to see not only who was talking about their client but the connections among those people talking about their client, who was talking about the client the most, and who was talked about the most.
There are many ways to visualize a social network. It can take quite a bit of time to learn the software. For example, I spent a weekend working on a #hokies network map using Gephi (read that post for a step-by-step guide of how I created a basic Twitter social network visualization). As you can see in that post, there are a lot of steps involved in building that one network map. But the upside is that Gephi is pretty robust and in my weekend working on it, I only scratched the surface.
Fortunately, there is a much simpler way to do some social network mapping. Netlytic.org is described on its website as “a cloud-based text and social networks analyzer that can automatically summarize and discover social networks from online conversations on social media sites.”
I first began playing with Netlytic soon after realizing that, for the baseline knowledge I want my students to have, I could not afford enough class time during the semester to warrant the investment in time and effort that would be needed to teach students all the steps of getting Twitter data and analyzing it in Gephi.
The downside of Netlytic is that is not quite as powerful and the visualizations are not as visually stunning.
Despite those limitations, I found Netlytic to have many upsides. For one, it is very easy to use and learn. A basic account is free and Netlytic will pull down the social media data you want directly into its service. From there, you can begin analyzing the data with just a few clicks.
I provide resources for learning how to use Netlytic below.
Setting Up the Assignment
Weeks before the we analyzed Netlytic in class, students were to create their own free Netlytic account and program their client’s Instagram or Twitter account and any hashtags they wanted to track on Twitter or Instagram related to their client.
The free version of Netlytic only allows an account to track up to 3 different searches at a time. Thus, teams were limited to 3 different networks to map. Each team approached this slightly differently, depending on their client.
There are 2 days of the semester dedicated to social network analysis and working with Netlytic. Day 1 is primarily lecture based. I provide a lecture of what social network analysis is, why it is good to have a basic understanding of it, and basic concepts that we’d be exploring. Specifically, I introduced nodes (e.g., Twitter users) and directional and uni-directional edges (the connections between them, such as retweets), talked about degree centrality (e.g., in-degree and out-degree density) as well as reciprocity, centralization, diameter and density.
On day 2, we relate these concepts to social media via a 15 minute lecture. For example, we discuss how in-degrees (who is mentioned a lot) and out-degrees (who posts a lot) would relate to Twitter posts. I then show several examples of social network maps of Instagram and Twitter and how concepts discussed in the prior class relate to them. And the rest of class is dedicated to working on analyzing the students’ client network.
There is a third day that is dedicated to students finishing up their Netlytic as well as anything they didn’t get done related to their Excel pivot tables and Microsoft Social Engagement.
It walks you through all of the steps of doing the network analysis and provides a list of resources for further understanding basic social network analysis.
I provide a few different research questions to the students that they have to adapt from, based on whether they are analyzing hashtags or other search terms or if they are analyzing mentions of their client. I have provided the information below just as it is described in the assignment:
Depending on what your networks are, you’ll need to choose from the RQs below. Choose all that are appropriate. Feel free to create your own. Discard the rest.
For networks that analyze hashtags or search terms:
What Twitter accounts are popular in this network and how often is each popular account mentioned?
What accounts mention others or RT others a lot in this network?
What unique clusters exist in this network?
For networks analyzing mentions of your client
What Twitter accounts mention your client the most? How often does each mention your client?
What communities are talking about @USERNAME?
Reflections on the Project
This project has three parts to it. Part 1 teaches students about using Excel pivot tables to analyze Twitter data. Part 2 gets them using industry social media analytics software. Part 3 introduces students to social network analysis and mapping a social network. The project takes several weeks of hands-on learning during the semester. For the students, a lot of work goes into the final product.
Because there were many moving parts, it was a little difficult at times for students to grasp all of the details of what we were trying to accomplish. Some student groups lost sight of the fact that the data they were dealing with was from different time periods because it was not all collected at the same time (a weakness I discuss in post #1 on this project).
One area of weakness was that some of the groups struggled with interpreting what their data meant and offering actionable suggestions to their client on how to enhance their social media based on the data.
Next time, I am going to try and tighten the assignment up. For example, I want to slim down a few of the research questions students had to answer that seemed a little redundant across the 3 different parts. I also want to place more emphasis on helping students think about what their results mean and how they can use the knowledge they have discovered through this analysis to make meaningful recommendations for their clients.
In summary, this is the first time we’ve run the project, and overall I am pleased with the outcome. I feel that this project helped me further ‘modernize’ my communication research class by placing greater emphasis on getting students working with social media data and thinking about what that data means.
I hope you enjoyed this 4-part series on the new social media analytics assignment in my communication research class! If you haven’t yet, be sure to check out post 1, post 2 and post 3.
This is post #3 in a four part series about a new assignment that I’m using this semester in my Communication research class (all posts on that class).
That assignment is a 3-part social media analytics project. Each part is related but unique, allowing students to pick up a new skill set. In this post we’ll discuss part 2 of the assignment. If you haven’t read the assignment overview post, and the post about pivot tables in Excel, I encourage you to do so before proceeding. In the first post, you will see a copy of the assignment that is discussed below.
As I wrote in my prior post, Microsoft Social Engagement” is a social listening tool that enables users to track metrics for public social media accounts or posts (e.g., keywords or hashtags) such as posts on Facebook, Twitter and Instagram. You can also track mentions forums and blog.”
Keep in mind that you have to program what you want the software to track ahead of time. It isn’t like a Twitter search where you can go in and look into past 2,500 posts on a topic after the fact. So, we have to program the student team’s clients and 1-3 competitors into Microsoft Social Engagement several weeks before the students sit down to work on the assignment. That way, there is some data for students to analyze.
I required students to turn into me the Twitter, and if available Instagram account, for their client and their competitors. I programmed them about a month before we worked on the assignment. To make my life simple, students had to turn all of this in at the same time they turned into me the Excel file of their client’s Twitter data (discussed in the pivot table blog post).
In my social media class, students were introduced to Microsoft Social Engagement and were given some guidance on how to use it to complete a metrics tracking spreadsheet. The purpose there was for them to track data week by week. In this class, we went a bit deeper. My purpose here was for students to look at the sum of data over a given period and extract specific insights. I added geolocation (q 3), a look at top engagement across time (q 4), parsing top positive and negative keywords (q 6 & 7), and exploring critics of the brand (q. 8).
Taken together, my goal was for students to learn the software in my social media class by throwing them into it. In this class, I wanted them to gain more experience, think a bit deeper and dig a bit deeper into the software.
For this part of the assignment, I created specific questions I wanted students to answer (below). To guide them through the steps needed to answer the below questions, I created this lab guide. Students worked through the lab guide in class and I was on hand to assist them.
For the client and each competitor, the students were to answer the below questions.
For CLIENT’S NAME what is the total number of a) shares, b) replies, and c) posts on Twitter during TIME PERIOD?
For each keyword, what is the share of voice for the client and its competitors?
(repeat this for however many keywords you have – up to 3)
In what STATE/COUNTRY were the top posts posted that mention CLIENT?
What day(s) of the month was CLIENT talked about the most on each social media platform?
Note: if we only have data from Twitter, then just use Twitter.
What is the sentiment percentages (positive, negative, neutral) for CLIENT?
What are the top positive keywords associated with CLIENT on each social media platform?
Note: if we only have data from Twitter, then just use Twitter.
What are the top negative keywords associated with CLIENT on each social media platform?
Note: if we only have data from Twitter, then just use Twitter.
Who are the top fans and critics for CLIENT on each platform?
Of course, the above 8 questions are just a sampling of what you could do with the software.
The software can be a bit challenging to use. And I found that students struggled at times to navigate it. It is important to make yourself available in class to help students.
Also, because the students had to answer these questions for their client and then for their competitors, it was rather time consuming. Teams that tackled this project in a smart manner, divided up the work and then put their answers together and reviewed them.
Some students may feel that this part is somewhat redundant to what they do in Microsoft Excel pivot tables. Questions 1 and 2 from the pivot exercise are similar to questions 1 and 4 from Microsoft Social Engagement, respectively. But, in my point of view, it is different enough and, importantly, it is a different way of analyzing things. Still, because this project overall requires a good deal of work when you consider the pivot tables and the social network mapping ,which we’ll discuss in the next post, you may find it useful to remove some of the above questions.
Projects like these can be intimidating and challenging for students. But I truly believe the benefits outweigh the drawbacks. The opportunity for students to learn industry software in the classroom is highly valuable. And it is better for students to dive in while in school than have their first exposure be overwhelming on the job.
In the next post, we will discuss part 4 of this assignment which gets students using Netlytic.org to do some basic network mapping of their client’s online network. I will be publishing that post in 2 weeks.
That social media analytics project assignment contains 3 parts. Each part is related but unique, allowing students to pick up a new skill set. In this post, post 2 of 4 in the series I’m writing about this assignment, we’ll discuss part 1 of the assignment. If you haven’t read the assignment overview post, I encourage you to do so before proceeding. There you will see a copy of the assignment discussed in the below post.
Part 1 of the assignment asks student teams to analyze the Twitter data provided by their clients by creating pivot tables in Microsoft Excel.
If you aren’t familiar with pivot tables, they enable you to filter and visualize spreadsheets. This allows you to focus in on specific data points and quickly extract insights from large data sets.
I got the inspiration to create this part of the assignment from a very helpful conversation I had with Professor Stefanie Moore at Kent State University. A big thank you to Stefanie for taking the time to chat with me and for providing me with insights to how she teaches analytics. I am really impressed and inspired by what Professor Moore is doing at Kent State.
Preparation: Getting Twitter Data
In order to analyze Twitter data using pivot tables in Excel, you need to first download Tweets from Twitter’s analytics (ads) page. If you’ve never done this before, it is really quite easy.
The reason we use Twitter is because Twitter enables you to extract a ton of valuable account data from your account in the form of a CSV spreadsheet. But, as an aside, you could analyze just about any data with pivot tables.
My students were required to get the Twitter data from a client. Therefore, I created a step-by-step guide that they could provide to the client so that the client could extract the appropriate data and supply it to me.
To ensure we had enough data, I instructed the students to ensure that their client was posting at least a few times per week. I asked students to get 6 months of Twitter data if possible. In short, I wanted to ensure that there were at least 50 Tweets from the client in the time period we collected. This number is somewhat arbitrary. And ideally you’d like to have more. But, 50 Tweets is enough to sort and play with.
Here are the steps for extracting Twitter data from an account:
Step 1: log into your organization’s Twitter account at http://twitter.com. Next, select your account profile picture (as shown below) and select “Analytics.”
Step 2: A new window will appear. Click “Tweets” from the menu at the top. Then, select the date range (see below). A menu will open. Please select a date range of at least 3 to 6 months back so that there are enough Tweets for the students to analyze.
Important: Click “Update” to change the selected date range.
In the below example, I selected Feb 1 through May 1 (3 months).
Step 3: Once the dates have been selected, click “export data.” A new window will appear. Click “save file” to save the file to your computer. Email that file (it should be a .CSV file named something starting with: “tweet_activity_metrics…”). You have your data. If someone else is downloading the data – such as a class client – , they will need to email the file to you or your student.
Using Pivot Tables to Analyze Twitter Data
A few days were set aside in class to work with the pivot tables and learn how to answer the questions students were asked to answer in the project. On day 1, I provided a brief lecture (about 10 minutes). And then I instructed students to begin working with the lab guide I had created. If you’re a longtime reader of this blog, you know I am big on creating lab guides to assist students in learning software.
While working with the lab guide, students were to have a copy of the assignment that contained the research questions they needed to answer using the pivot tables. Those research questions were:
Which Twitter posts received the most (Fill in the blank – you need to decide what variables are important engagement data for your client. You’ll need more than 1 variable. And, you’ll want to show more than just the top Tweet for that variable, but the top few)?
What is the client’s Twitter engagement by month? (again, you choose the appropriate engagement metrics)
Come up with 1 other RQs for important data points you extract from your pivot table analysis that you believe will be of value to your client.
For the above questions, students needed to pick what engagement metrics they wanted to analyze. There are several engagement metrics in the CSV file when you download it from Twitter. Examples include retweets and favorites.
For research question #3, most groups analyzed engagement by Tweet category. As you’ll see in the lab guide, students learned how to comb through their Tweets and identify common themes by which to categorize their Tweets. Examples may include promotional Tweets, humorous Tweets, Tweets that ask a question, etc.
The above 3 research questions are just a sampling of what you could do with the pivot tables.
In the next post, we will discuss part 2 of this assignment which gets students using Microsoft Social Engagement to answer some research questions about their client. I will be publishing that post in 2 weeks.
In the meantime, if you want to get your feet wet, I encourage you to download your own Twitter data and walk through the lab guide above. Or, check out some of the sources listed below to learn how to analyze Twitter data with pivot tables.
As you will see when you take a look at the lab guide, you must first clean the data so that Excel can analyze it. I then walk you through a number of different ways you can analyze your Twitter data.
The fact is that I was a bit of a newbie to pivot tables when I created this assignment. To build the above-discussed lab guide I provided students to help them through learning how to use pivot tables, I relied heavily on several key resources. Much of what is in the lab guide is built directly on what I learned from these sources. To learn directly from the sources I learned from, check out the sources below. A big thank you to all of them for sharing their knowledge publicly. I hope I was able to honor them in adapting their work for a classroom assignment.
Update: You can now read the follow up posts to this blog series.
A few months ago I wrote about how students in my social media class were using Microsoft Social Engagement to track metrics and do some social listening. At the time, I said I’d follow up with a post about how we were using the software in my communication research class. Well, the time has come! But, this post will do more than dive into how we are using Microsoft Engagement in my class. It will share with you a whole new project my research students are doing.
This is post #1 in a 4 part series on a new assignment my students are working on in my communication research class. The assignment spreads over several weeks with a good amount of time in class working in the computer lab. The project is the result of continued and ongoing efforts I’ve been making in a few classes to enhance student education in social media analytics. The project replaces the sentiment analysis assignment I wrote about a few years ago.
This post will cover an overview of the assignment (A copy of the assignment is below). Post #2 will discuss using pivot tables to analyze Twitter data. Post #3 will discuss Microsoft Social Engagement. Post #4 will discuss Netlyitic.
First, let me provide some context. In my communication research class (see all posts related to the class), students work in teams to complete 3 projects. Each project gets progressively more difficult. The project we are going to discuss today is project #2.
Overview of Social Media Analytics Project for A Client
The purpose of the assignment is for students to get experience performing a social media analytics audit of a client using a variety of social media analytics and social network analysis tools. The goal is for the students to try and understand their client’s current use of social media and provide insights and recommendations for enhancing that client’s social media presence.
Each team was tasked with going out and finding a client that would agree to participate. While I had hoped that most groups would approach local businesses, they tended to focus more on on-campus groups like athletic teams. This may have been a result of convenience because each team had to acquire several months worth of Twitter data from their client. I will explain that in further detail when we discuss pivot tables in post #2. So students tended to go to on campus organizations where they already knew who ran the Twitter account.
The three main components of the project are:
Client Social Media Profile & Engagement Analysis
Students use Pivot Tables to explore your client’s posts on social media and analyze their overall engagement. For example, students determine the top posts by their client which made that have gotten the most likes.
Students use Microsoft Social Engagement to monitor and analyze the conversation surrounding the client’s brand.
Social Network Analysis
Students use Netlytic.com to build visual representations of their client’s social network on Twitter or Instagram and do some basic analysis.
For each component, I have created a set of research questions that students answer using the appropriate software. The students adapt the research questions a bit to their context when necessary. You can see the research questions in the assignment below.
The Plan in the Classroom
On day 1, I provide a 10 minute lecture on pivot tables. The rest of the class is a lab for students to work on learning how to create pivot tables to analyze Twitter data and answer the RQs.
On day 2, I give a 20 minute lecture about the social engagement software and talk a little about sentiment analysis so students understand what it is when they look at it in the Microsoft software.
Day 3 is a lab day to work on whatever they weren’t able to get done in the pivot tables or the social engagement software.
On day 4, I lecture about social network analysis and some basic concepts. (We cover some other material this day about writing research papers).
On day 5, we finish talking about social network analysis – about 15 minutes – and the students analyze their client’s data.
Research Write Up
After students complete all 3 parts of the project, they then have to write up their study. The research paper format I use in this class is inspired by Don Stacks book, Primer in Public Relations Research.
In the past, by the second project students are writing brief literature reviews. However, because this is the first time I’ve run this project and it has been a lot of work, I called an audible and removed the requirement for the lit review in this project. So, you will see in the assignment below that those requirements have been withheld.
Thus, by the second project students have been taught about writing research problem overviews (problem statement, campaign goals & objectives, research objective & RQs/hypotheses), methods, results and discussion sections.
The students write up their reports. And they are encouraged to share them with their client.
Limitations & Final Thoughts
There are a few drawbacks I’ve experienced thus far with this project.
First, there is a lot of info coming at the students with this project. The assignment sheet itself is several pages long. As such, it is important to explain things several times and work with the students as they are doing this project.
Students need to be responsible for getting the data for this project from their client, creating their own Netlytic account and setting it up to collect data. And, they need to provide me with who their client is and some competitors of the client far enough in advance that I can program it into Microsoft Social Engagement (I’ll go into more depth on this in the individual posts about each section). We had a few groups that made mistakes along the way and were short on data or had to do some last minute scrambling.
The data collection periods across the Twitter CSV file, the Microsoft Social Engagement and the Netlytic are not consistent. This is simply a result of the classroom setting and a lack of full control over when data collection happens. For example, a team’s client may have sent their Twitter data which covers the last 6 months one day, a teammate set up Netlytic to collect data another day, and the day I set up the Microsoft Social Engagement to collect data on their client on a third day.
With these another limitations in mind, the project has been fun thus far this semester. A major benefit of this assignment is that most of the tools used in this assignment are free or inexpensive and not too difficult to learn (and thus teach your students).
Over the next few posts, I will offer some depth on each section of the project. So check back soon! For now, you can get a copy of the assignment below.
Update: You can now read the follow up posts to this blog series.
This book is unlike any other book in the social media space that I know of presently. It is not a book that you assign to your students to learn about social media. It is a one-stop guide for professors with just about everything you would need to know to build a social media class from the ground up. And it is awesome.
Freberg covers key considerations that I’ll break down into 2 parts. The first part of the book deals primarily with publicity and public interaction surrounding your class, and the second half of the book focuses on assignments and rubrics that you can use in the class.
In the first part of the book, Dr. Freberg reminds us: “First, build self-confidence and project that you KNOW what you are doing. If you walk into the class with any self-doubts, the students will be able to read that in a hearbeat” (p. 11).
I like how Dr. Freberg gets the reader thinking of the important but often overlooked consideration of branding your class. After all, if you’re teaching a social media class, your students may be engaging with the public online. She touches on tips for building a hashtag for your classes (something I’ve honestly not done a good job of remaining consistent at) to foster interaction between yourself, your students, and thought leaders. Even if you have your social media class built and feel you don’t need any additional tips or assignments to enhance it, the book is valuable for the wider lessons in here for personal branding for professors. That is, in branding your class, you are branding yourself as a professor. And doing so can open many opportunities for you (e.g., networking opportunities, requests to speak, etc) as well your students (e.g., guest lecturers). There are also great time management tips that will help any professor dealing with the flood of information and the rapid pace of change that social media professors deal with on a day to bay basis. This section of the book then goes on to discuss social media etiquette for students and tips for inviting and working with guest lecturers.
In the latter half of the book, Dr. Freberg provides an in depth look at several valuable assignments that you can incorporate into your social media class. This includes an online reputation assignment, a social media strategy assignment, and more. A sample social media class syllabus is provided as well. The assignments include detailed explanations, instructions and rubrics.
There is much in this book that I found useful and am in the process of putting into practice. For example, I adopted the assignment and tips on personal branding from this book for my public relations principles class. I want to get my students thinking about personal branding early on, and this book and a panel I attended last fall at the PRSA Educators Academy Super Saturday inspired me to take the leap.
Altogether, a big congratulations and thanks to Dr. Freberg for creating this helpful resource.
I hope you found this post helpful. If you did, please share it. It means a lot.
A Social Media Education Blog by Matthew J. Kushin, Ph.D.