How to analyze social media data?

Discovering themes and patterns from social media content by knowing how to analyze social media data better can be a very exciting endeav...

Discovering themes and patterns from social media content by knowing how to analyze social media data better can be a very exciting endeavor. This is the part of analysis where the detective in the analyst really comes out. This post focuses on the common goals of a majority of social data analysis projects. These goals fall into two broad categories: validation of hypothesis and discovery of themes. In the first category, the researcher already has a hypothesis (a prediction of what the likely outcome will be) in mind that he or she wants to validate. In the second major category, discovering themes or topics or trends, there is no preconceived notion of what the researcher is likely to find. The analyst begins the analysis with an open mind and attempts to discover what the data is implying either in terms of definitive insights or answers to specific questions, or in terms of the trends being implied by the underlying data. Here, we discuss the analysis steps that are typically taken to satisfy these goals in the context of some specific project examples.

Many social network analysis projects require an iterative approach. We have made reference to this approach throughout this book, and we refer to it in this post also. Toward the end of this post, we have included a discussion of iterative methods and how we recommend applying this method for social media analysis projects.

Validating the Hypothesis

Many social media analytics projects start with some preconceived idea of the results or insights they are expecting to find. With that as a starting point, the analyst identifies the appropriate datasets and performs analysis iteratively to arrive at the conclusion. At the end, the results might either confirm the hypothesis or reject it. It is possible that an analysis may end up with inconclusive evidence, so the analyst may have to refine the approach and try again. In the following sections, we look at a few examples and describe the process end-to-end to highlight the specific steps that need to be taken.

We discuss three specific examples:
  • Youth unemployment—In this example, we describe a project that we executed for a marketing team in Europe. The subject was youth unemployment in Europe. The hypothesis was that we would be able to find evidence in social data analytics to support the conclusions being reported by official government reports.
  • Cannes Lions 2013—In this example, we describe a project that we executed for a marketing team in the United States. The hypothesis was that a very popular movie in general media would win an award in this competition.
  • 56th Grammy Awards—In this example, we describe a project that the social media analytics team executed as an experiment to test the hypothesis that social media chatter could give very effective clues about who would ultimately win an award.

Youth Unemployment

A marketing team in Europe contracted our IBM social media analytics team to see if social media analysis could be used to confirm or deny youth unemployment in Europe. At the time of this project, the topic of youth unemployment in Europe was dominating the news media. The IBM marketing team was interested in exploring the benefits of IBM’s social media analysis tools and techniques in an effort to see what could be uncovered in this space. The theory was that if our team could identify some insights by just using data that is freely available (social media content), it could be used in a case study discussing product or service opportunities with some of our government clients in Europe. The hypothesis was that there would be a sufficient amount of chatter across the social media sites to corroborate the reports that were being generated by the official channels in the countries being severely affected by this problem. As we have discussed before, these projects required an iterative analysis. Based on this concept, our approach for this project included the following steps:

1. Identify the relevant data sources.
2. Develop or modify a set of keywords to bring in the most appropriate data from these sources.
3. Develop or modify a model to classify and categorize the information.
4. Evaluate the results.
5. If the results are satisfactory, the project concludes. If not, we go back to step 2 and iterate.

Data Identification and Data Analysis

Given that this project focused on the views of Europeans, we needed to look at all of the social media content originating in those countries. With location as the first filter, we needed to identify content that matched a known set of keywords that would indicate discussion around the term youth unemployment. We utilized the service of a data aggregator called Boardreader to search and discover social media content matching our keywords. We started with an initial set of keywords based on input from the marketing team and the judgment and experience of our lead analyst, Mila Gessner.

From a data identification perspective, we came up with a list of countries that we needed to focus on: Spain, Italy, Ireland, France, Portugal, and Slovakia. We configured the data identification tool to select content from these countries.

We then needed to come up with an initial set of keywords that we were going to use to pull in content from the social media universe: youth, unemployment. We configured the analysis tool to focus on search keywords like youth, young, and teen. We used regular expressions to ensure all variations of these terms were captured and included in our data model. We discussed the method of using regular expressions to “cleanse” the data, or to limit that data that we collected for further analysis. We demonstrate a few more examples of regular expressions here. See Table 7.1.

analyze social media data
 The tool also allows the analyst to specify related context keywords, ensuring that only content relevant to the project is included. For example, in our initial model, we used context keywords such as unemployment and unemployed. This ensured that we captured all conversations that referenced young people but within the context of talking about topics of “unemployed” or “unemployment".

We took the results of this phase forward and conducted our analysis. For the sake of discussion, let’s call this iteration 1. We quickly learned that we were missing much of the relevant content because our search keywords were too limiting. For example, we learned that we needed to add the word graduates to our mix (implications of being young). We also learned that we needed to add a few additional context keywords like jobless and out of work.

During this phase, we also learned that quite a bit of irrelevant content was being captured in the model. The analysis tools allowed us to exclude that irrelevant content by using “exclude” keywords, or keywords that, when found, cause the content to be ignored. As a result, in the next iteration, we used exclude keywords such as movie, cricket, and world war to eliminate content that perhaps matched our keywords and context words but was not relevant for our analysis.

We hope this discussion gives you an indication of how a typical analysis phase, with multiple iterations, is executed on projects. Rarely does a first attempt work without any changes to the model or collection. Here, we have reproduced the final model at the conclusion of this project.

Countries of interest:

EU, Spain, Italy, Ireland, France, Portugal, Slovakia


(young|younger|youngest) .{0,80} worker.{0,1}
worker.{0,1} .{0,80} (young|younger|youngest)
(young|younger|youngest) .{0,80} (adult|adults)
(adult|adults) .{0,80} (young|younger)
(young|younger|youngest) (worker|generation|people|folks|individual
(young|younger|youngest) .{0,80} generation.{0,1}
generation.{0,1} .{0,80} (young|younger|youngest)
(young|younger|youngest) .{0,80} people.{0,1}
people.{0,1} .{0,80} (young|younger|youngest)
(young|younger|youngest) .{0,80} folks
folks .{0,80} (young|younger|youngest)
(young|younger|youngest) .{0,80} individuals
individuals .{0,80} (young|younger|youngest)
(young|younger|youngest) .{0,80} citizens
citizens .{0,80} (young|younger|youngest)

Context terms for keywords :

not employed
(looking|searching) for work
(looking|searching) for job.{0,1}
no job.{0,1}
no employment
without employment
without job.{0,1}
out of work
between jobs
(looking|searching) for .{10} work
(looking|searching) for .{10} job.{0,1}
without work

Exclude keywords:

World War.{0,3}
Holy Roman Empire
drone strike.{0,1}

Once the model is ready, then we need to look at the results to see if they are ready for interpretation. One common issue that we run into in projects like this is called “duplicates.” A particular piece of content may be referenced in different social media venues by different sets of people. We typically have to weed out these duplicates to ensure clean analysis.

Based on the amount of content we were obtaining from our aggregator, we decided to limit the analysis to a duration of three months (January to March 2013). At the end of this phase, we identified a number of qualified mentions about youth unemployment from a number of European countries. These mentions were then ranked by the countries that showed evidence of high incidence of youth unemployment. We then compared this data with official unemployment data published in Europe by the Heritage Foundation.

The results are shown in Figure 7.1.


From this analysis, we were able to conclude that indeed there was a strong correlation between countries with high levels of youth unemployment and the chatter related to youth unemployment in social media venues.

This use case is an example of validating a predetermined hypothesis. In this particular case, we were able to confirm the hypothesis.
So what’s the big deal?

That’s a good question. Just because we’ve shown a relationship exists between the social media postings of unemployed youth and the real unemployment number doesn’t really answer any questions for us (or quite honestly, provide any business value on its own).

The important thing is that we have shown we have a working model that would contain social comments made by individuals who are unemployed. So if we want to target them in marketing campaigns, blogs, or social media, we can use this model to understand the issues concerning them, such as education, health care, cost of living, and so on.

This is the point where there is a close tie between social media analytics and marketing. Let’s assume that a company that provides higher education services is looking at this data during some of its decision-making processes. If that company is trying to get members of this segment to consider educational classes or additional education, it launches a marketing campaign. Using the concepts in this model, it could perform a before-and-after analysis to understand if its marketing message made any inroads into the community. It could also determine if its message is having a positive (or negative) effect on its intended audience. The point is that the company now has a tool to measure the pulse or general feeling of a particular segment. As we said before: knowledge is power.

Cannes Lions 2013

The IBM Social Analytics team was again contacted by the IBM Marketing team to do some social media analysis around the Cannes Lions 2013 event. This is an event that honors and celebrates creativity in media. During this time, an IBM movie called A Boy and His Atom was creating quite a stir in the media. We were approached to see if we could determine how popular this movie was when compared to other campaigns that were receiving a similar buzz at the festival.

Since this was a real-time event, we built a model analyzing Twitter data that was being generated during the event. We first noticed that A Boy and His Atom was receiving mentions (27 to be precise) in the context of IBM (342 total mentions). To us, this meant that the movie was clearly being noticed and being talked about in the context of technology companies (in this case, IBM). See Figure 7.2.

Next, we compared the mentions of this movie as compared to others in the same category, and we realized that it was nowhere in the top (see Figure 7.3).

So, the conclusion at this point was that our hypothesis was rejected. There was clearly a lot of external media buzz about this particular movie, but other movies in the category had many more mentions within social media channels.

We’ve seen this scenario time and time again: a topic is discovered in an analysis, and we assume that it is relevant based on the frequency of use. But what always has to be considered is the context in which that topic is discussed. So naturally, when users of social media were referencing the movie A Boy and His Atom, they probably posted something like this:

Check out IBM’s “A boy and his atom”—a great piece of work. So the movie was discussed in the context of IBM (in other words, when we looked for mentions of the word IBM, we also found discussion about this movie). This is logical and makes perfect sense. But what that means is that in the context of movies, it gained far less traction (and perhaps appeal).

56th Grammy Awards

In 2013, we challenged our social media analytics team to see if they could use the tools available to predict the winners in Grammy awards based on chatter in social media.

Hypothesis: There is a correlation between the number of mentions (and positive sentiment) in social media about an artist and the fact that he or she won. Table 7.2 shows some sample categories and the nominations in those categories.

We analyzed content in social media and public media up to one month prior to the day of awards. About 81 categories were identified. There were an average of 5 nominations in each category.

We captured mentions about each category and each person nominated. We then tabulated the winners that were determined purely by number of mentions in social media. We compared this list with the eventual winners. We observed that in 62 out of the 81 categories (77%), the eventual winner was in the top three list based on social media mentions. We concluded that in a majority of cases, the winners identified by social media analysis prior to the actual announcement were in the top three of the actual winners.

So, what is the business value of this experiment?

The first important lesson our analysts learned from this experiment was that social media mentions are quite powerful. Even when they had no idea of the real process that was utilized to select the winners, they were able to obtain a prediction about who the winners might be (in a majority of the cases) by listening to public opinion. From a business perspective, social media analytics becomes a tool that can be leveraged for some quick analysis using publicly available data before spending large amounts of money in focus groups or other formal information-gathering mechanisms. Consider that a company with many established products in the marketplace could perform a quick market reaction analysis of its products without spending much money before deciding on further formal research that may be needed. Thus, perhaps it could make an educated guess before proceeding.



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