3D DIGITAL DIMENSIONS 2011
(ONLINE + SOCIAL MEDIA + MOBILE) RESEARCH
MIAMI / 26 – 28 OCTOBER

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TELL ME WHAT YOU WANT, WHAT YOU REALLY, REALLY WANT
CREATING DESIRED RESULTS FROM SOCIAL MEDIA RESEARCH

Annie Pettit, Conversition and Research Now, Canada
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This presentation will teach you how to generate the social media research results you desire regardless of what the true results are. I will demonstrate how to gather social media data from the internet using inappropriate sampling methods, and how to select the wrong pieces of data and code it incorrectly. The topics of sampling, weighting, data quality, sentiment analysis, and text analysis will be highlighted so that you can understand the full range of options for mistreating data. The ultimate goal will be to create set of data that reflects our predispositions towards a topic as opposed to reality.

Attendees are required to come prepared with a sense of humour  (i.e., I will be speaking in jest!)

Photo puravida from morguefile

Many people are curious about the difference between social media monitoring and social media research. The distinction is clear, and fairly easy to see if you have experience with market research.
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Social media monitoring

• The act of reviewing and tracking social media data
• May include tracking the volume of data meeting specific criteria, possibly tracking the sentiment of that data, determining which websites are producing greater or lesser volumes of data, identifying individual who are prone to discussing a brand, interacting with individuals contributing the social media data

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Social media research

• The application of scientific methods to social media data
• Like surveys, focus groups, and other established research methods, social media research incorporates the scientific principles that turn data into valid and reliable data sources, suitable for explaining past and current behaviour, and predicting future behaviour
• Established methods may include several of the following: developing research objectives, defining problems, defining and selecting relevant target groups and samples, applying sampling, weighting and standardized data quality methods, applying validation methods, evaluating data reliability
• Outputs include Usage and Attitudes studies, Brand Trackers, Ad Trackers, Segmentation studies, and other research traditionally using survey or focus group data

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An article in the New York Times this week discussed a research project that is attempting to track the mood of Americans using Twitter as the data source. The project involves researchers from Northeastern University College of Computer and Information Sciences and Harvard Medical School. It is certainly reasonable that a group of scientists can develop algorithms that accurately predict the mood of Americans. However, Twitter data is not simply and instantly predictive of the general population of Americans. Given that only 7% of people who are online even use Twitter, it is risky, and can easily lead to wrong conclusions.

Want to see a real example? No problem.

Let’s look at consumer opinions related to one specific product, the iPad.

1. First, we gathered thousands of opinions from across the internet, from blogs, microblogs, forums, question and answer sites, personal sites, all of which mentioned the iPad. Sites like YouTube, Blogger, Twitter, and thousands more were included.
2. Then, we categorized all of the conversations into two groups, 1) everything from Twitter and 2) the entire internet space.
3. Next, we determined the level of emotion for every online conversation. Specifically, we determined whether the emotion of the conversations was extremely happy, somewhat happy, neutral, somewhat unhappy, or extremely unhappy.
4. Finally, we created the pretty little charts that you see on the right of this page.

What’s the first thing you notice from these charts?

Not one single chart has two bars that look the same. What is the percentage of tweets that reflect an extremely happy opinion? 15%. What is the comparable number for the entire internet? 5.6%. I hope it’s not just me, but 15% doesn’t look like 5.6%, not even if the 5.6% is averaged up to 6%. There is a big difference in the percentage of people who have extremely happy opinions on Twitter vs the entire Internet.

The same trend is apparent when we look at the percentage of people who are extremely unhappy with the iPad. 11.3% of tweeple are extremely unhappy compared to just 1.9% of the entire internet space. All five of the charts lead to the same conclusions. Twitter results do not equal Internet results.

It’s not 1 to 1

Clearly, the relationship between Twitter data and total internet data is not 1 to 1. It’s impossible to gather Twitter data,  analyze the sentiment, and be confident that it represents a wide, more general audience.

Perhaps people on Twitter have more extreme opinions than everyone else; perhaps they are less likely to guard their remarks so that the more extreme opinions are shared; perhaps Twitter opinions are in fact the closest to the average American opinion. Whatever the reason, it is undeniable that the mood on Twitter is unlike anywhere else.

Prepare to be wrong. Prepare to explain contradictions. Generalize Twitter mood at your own risk.

Links that might interest you:
iPad on EvoPlay
New York Times article
Conversition on Facebook