Skip main navigation

New offer! Get 30% off one whole year of Unlimited learning. Subscribe for just £249.99 £174.99. New subscribers only T&Cs apply

Find out more

Exploring Data Science

Social Media Analytics is a branch of Data Science: deriving insights from trace data collected from social media content and behaviors
Social media analytics is a tool kit with many specialized devices. It is also on the border of several domains of inquiry that combine theory and methods. Now, how do you practice this art, how do you start? What are some of its challenges. The best way to understand social media analytics is to talk with one of its practitioners. I asked Natalie White, a Professor of Communication and Social Media analyst to give you a peak into her shop.
>> Data science is actually a very broad field where data scientists utilize analytic methods to identify patterns within datasets and to extract meaning from those datasets that otherwise would not be clear at all about what’s going on. And social media analytics to me is a subfield of that where the data that you’re focusing on is social media data. And it’s collected from places where people interact online and are creating original content online. So very related, but with social media analytics, you’re really focusing on those places where people come together and interact online.
I started out working in environmental PR. So I worked for a city county agency engaging with the public to try to reduce water pollution in the area. And I found that what I really loved about that was trying to figure out what stakeholders really thought about the works that we were doing and trying to collect their opinions and utilize them in a really effective way. And I realized that what I really cared about was gathering and making sense of data. So I went back to school and got my PhD, and while I was doing that I took a lot of analytic courses similar to what people would be learning in this course.
And from there I started focusing on why people go online to talk to each other when there could be people offline to provide information. So, for example, women who go online to talk with other women with breast cancer in order to get help and information about problems they’re having during treatment. Why are they going online for that information? What I want to learn is what information people really need and how do we get it to them more effectively?
The most important thing to do when you’re starting a new social media analytic project is to first know what are your goals? What are you really trying to learn from a particular dataset? And that will completely change how you go about analyzing the data. So, for example, if you have a data set that is information about customers interacting online or online reviews about your products, your goals could be what are the most common themes. So how do people talk about our products, or it could be what are the really positive reviews that our products get or what are the really negative reviews?
So you might be dividing out the data based on what we call sentiment, sort of the emotional valence of the different post. Or your goal might be who are the most influential posters online or the people who post most frequently or their most effective ads spreading information. So really the goals that you have and what you wanna learn from the dataset, that will really inform the different analytic techniques that you use.
There’s definitely a balance between understanding the theory behind how people behave online or how people communicate using social media. And then your ability to apply analytic tools in those types of spaces. For me where theory comes into play is often designing how I’m going to analyze a dataset. So really understanding what behaviors do I care about? How do those behaviors play out in real world situations? But in theory is also really useful on the other end of analysis. So after you’ve analyzed your data, you’ve found this really exciting pattern. How do you interpret that? How do you make sense of that?
Because there are hundreds of years, sometimes the research for social media analysis, maybe just a couple of decades that can help you explain the patterns that you found. So, I find the balance is really having it theory and form, you’re thinking on the front end of a project, and then helping you really make sense of things at the end.
To start on your journey towards being a social media analyst, I really recommend first of all, learning as many analytic techniques as possible. Because the more tools that you have in your tool box, the more flexible you can be as you’re trying to analyze data and pull out information you really need. And secondly, I would say make sure that you always understand the context of whatever data set you’ve collected. So if you’ve collected data from a website online, is that website really a community? Do people know each other well? Or is it more a place that people go to sort of rant their displeasure about a product they’ve received, or to try to respond that full of commentary.
So really what is the purpose of why people going to a particular space? So once you have enough tools and you really understand the context of your dataset and you’re thinking in that manner that will really get you started to being an effective social media analyst.
Starting out as a new social media analyst, it’s really important to really start that journey with the mindset that trial and error is okay. And that you’ll be learning to run analysis and maybe even do some programming that you’ve never done before. And that it’s okay to not have everything run perfectly initially. It’s important to have an open mindset that if you try to run something and it doesn’t work, that doesn’t mean you’re not good at it. It’s all part of the learning process.
So I wish that that was something that I had known early on that many hours of running an analysis and getting results and rerunning it again, it’s just part of the natural process of learning and it means that you really are making progress.

Are data science and social media analytics synonyms or are they different concepts?

Social media analytics is a toolkit with many specialised devices. It’s also at the border of several domains of inquiry: statistics, sociology, psychology, and even linguistics. The best way to understand this domain and profession is to listen to someone who practices it. Natalie White, a professor of communication and a social media analyst, gives you a peek into her world as she shares her perspective on how social media analysts approach projects, balance theory and method, and provides some advice for those starting out.

  • We often think of data analytics as a tool for business, but Natalie discussed using it in ways beyond the business world—environmental public relations and research into why people go online for information. If you’re starting a social media analytics project and had access to any type of data, what questions would you want to answer? What would your goals look like? Let us know.
This article is from the free online

Digital Media Analytics: Introduction

Created by
FutureLearn - Learning For Life

Reach your personal and professional goals

Unlock access to hundreds of expert online courses and degrees from top universities and educators to gain accredited qualifications and professional CV-building certificates.

Join over 18 million learners to launch, switch or build upon your career, all at your own pace, across a wide range of topic areas.

Start Learning now