As part of that project, students are in charge of monitoring the conversation around our department’s social media. Last year, my students used Microsoft Social Engagement which is a great piece of software that we also use in my Comm 435 Communication Research class (all posts about that class). This year, my social media class students will use Meltwater to do the social media listening.
Students will use Meltwater to work on the “social listening” tab of that spreadsheet.
The other tabs in the spreadsheet are about tracking our own performance. The social listening tab is for seeing what is being said about our brand every week. So, students go into this spreadsheet and fill out the below questions from weeks 9-15 of the semester. Specifically, the spreadsheet asks the students to answer 5 questions each week. I modified the questions slightly from last year because the last question from last year could not be answered with Meltwater. You can see this year’s questions below. A hint is provided to students on where to look to find this answer by mousing over each question.
Even though students will have experience using Meltwater by the time we start doing the social listening about our brand about 8 weeks into the semester, I created a lab guide (about lab guides) to help students walk through the steps of answering these questions. My hope is that after they use the lab guide once, they’ll know what to do to be able to answer the questions.
The lab guide is linked in the spreadsheet. You can also access it directly here. If you are new to using Meltwater, the lab guide walks you through how to do some basic social listening. I encourage you to check it out.
In summary, I’m super excited about the growing opportunities my students have had to work with industry software like Meltwater and Microsoft Social Engagement to get real world experience with social listening. I know many of us have worked hard in the last few years in seeking out opportunities like this. And I am extremely pleased that companies like these are making their software solutions available to our students. It matters a lot! I know that my students will leave Shepherd with hands on experience using the same industry software used by many of the largest brands.
I hope you found this post helpful. If you did, please share it. It helps a lot.
In the below post, I will discuss my current plans to use the Meltwater media intelligence software in a 300-level strategic social media course.
First, some background:
Most of my students have searched social media sites for their own personal uses. But, before taking my class, few have had to put themselves in the seat of an organization that wants to see who is talking about them.
So, to get students thinking about why an organization would want to monitor the conversation about its brand and the sort of things the organization would want to monitor, I start students out with a brief lecture an a simple in-class exercise.
In the past, my social media students have used a slew of free tools to complete some of the early social media listening activities that I like to assign to get students thinking about the value of social listening.
This semester, students will use some of those tools. But, we’ll be adding Meltwater to really round out these activities.
Using free tools is fraught with dangers. The two biggest dangers are 1) the possibility that the free tool will be here today and gone tomorrow (think topsy.com) and 2) that they tend to be limiting. It can also be frustrating when using free tools because each free tool only provides so much.
So the chance to use real, industry software in my class this year for these activities is a huge leap up.
The Set Up
After the awesome training that Carol Ann Vance provided our students last Thursday, my students were given the following homework: Watch the training videos on the Meltwater platform (see image below) and to create a new dashboard for a social media search of interest to them.
Now that the students have played with Meltwater a little, I then provide them with a more structured activity using the software.
After a lecture on the importance of social listening along with some tips, the plan is to get the students using Meltwater for an in-class activity.
The in-class activity asks students to do some basic social listening for a brand. I choose Burt’s Bees because its a brand many students are familiar with that meets a specific niche: environmentally-conscious health and beauty products. Many people love Burt’s Bees, health & beauty blogs and YouTube channels are a big thing and Burt’s Bees is sometimes featured in videos by influencers in this space, and Burt’s Bees makes a variety of products. I also choose Burt’s Bees because some people have complained about allergic reactions to their products and because I know that they have received some backlash when they were bought out by Clorox several years back( the company was seen by some as selling out to their antithesis, a company that creates products using harsher, less environmentally-conscious chemicals). Of course, you could do this exercise with any brand.
I’m hoping that the students will uncover a diversity of sentiments about the company by doing this activity. And often times, the students aren’t aware of the negative feelings people have towards the company until they do this exercise. So it’s eye opening for the students to see how much they can learn with some basic social listening.
The activity takes about 15-20 minutes to complete. During the activity, I go around the room and help students use the software and make sure everyone has a grip on it. Afterwards, we discuss what the students found and look for themes.
You can access the activity through the following Google Doc. Feel free to make a copy and save it to your own Google Drive account.
The activity is not too complicated and fairly easy for the students to pick up. But it is a great way of getting students’ feet wet. Using analytics software can feel intimidating at first. So this is a nice, comfortable experience for the students.
The students are now prepared for the social media audit assignment. In that assignment, the students use Meltwater and free tools to conduct a social media audit of their client as well as 2 of their client’s competitors. Dr. Gallicano has some great examples of social media audits completed by students on her blog here. You can see a few of them cited in my social media audit assignment below. The students compare and contrast the client to the competitors and look for recommendations to the client on how they can improve their social media. The client in my social media class is our department’s social media, but you could apply this to any industry. (Read more about how I set up our department’s social media as the class client). The assignment is a group assignment with some time given to students work in class.
The assignment is the first major assignment students do in my class and is the foundation for creating the strategic briefs the students create after that.
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.