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.
As is the case with the other 2 parts of this assignment, students work in the computer lab on their Netlytic portion of the assignment during class time. I provide a basic lab guide of how to use Netlytic. The great thing is, Netlytic provides a lot of instructions on their YouTube page on how to use the software.
This great video shows you how to analyze your network map.
The lab guide I created for my students can be found here: bit.ly/435_netlyticlab
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.