In case you missed the dustup, here’s an excerpt from the WSJ.com article:

At 1 a.m. on May 7, the website PatientsLikeMe.com noticed suspicious activity on its “Mood” discussion board. There, people exchange highly personal stories about their emotional disorders, ranging from bipolar disease to a desire to cut themselves.

It was a break-in. A new member of the site, using sophisticated software, was “scraping,” or copying, every single message off PatientsLikeMe’s private online forums.

Bilal Ahmed wrote about his health on a site that was scraped.

PatientsLikeMe managed to block and identify the intruder: Nielsen Co., the privately held New York media-research firm. Nielsen monitors online “buzz” for clients, including major drug makers, which buy data gleaned from the Web to get insight from consumers about their products, Nielsen says.

I felt totally violated,” says Bilal Ahmed, a 33-year-old resident of Sydney, Australia, who used PatientsLikeMe to connect with other people suffering from depression. He used a pseudonym on the message boards, but his PatientsLikeMe profile linked to his blog, which contains his real name.

After PatientsLikeMe told users about the break-in, Mr. Ahmed deleted all his posts, plus a list of drugs he uses. “It was very disturbing to know that your information is being sold,” he says. Nielsen says it no longer scrapes sites requiring an individual account for access, unless it has permission.

Coincidentally, the 11/1/10 issue of Newsweek has an article entitled “Privacy is Dead.”  The author, a young woman, describes a new “score” created to help employers understanding social media behaviors of potential employees.  For this article, she hired a company called ReputationDefender (who works to clean up online reputations, but has a mixed reputation themselves) to see what they could learn about her (a job they usually don’t do).  As she said:

Three Silicon Valley engineers, several decades of experience, and access to publically available databases like Spokeo, Facebook, and LinkedIn (no, they didn’t do any hacking) – and voila.  Within 30 minutes the company had my Social Security number; in two hours they knew where I lived, my body type, my hometown, and my health status.

As she points out, it’s one thing if employers get this info; another if it’s a credit-card company or data aggregator who is turning around and selling info on you to other companies for a profit.  And maybe these companies aren’t talking about what they’re doing;  end-clients may not know (or don’t ask) how these companies are so effective in reaching a marketer’s  target audience.

The “debate” centers on several factors: 

What is “Public”?  I think we all believe that if we post something to our own public blog, or to a public LinkedIn or Facebook group, or Twitter, there’s the expectation that people can “take it” for analysis.

The Gray Area.   I was never naïve enough to believe that sites weren’t using our information for profit in some way.  However, I did assume that there was a gatekeeper in place: 

Case 1:  Amazon makes recommendations to its own customers based on past shopping and purchasing behaviors.  This is a closed system; I know I have a relationship with Amazon.

Case 2:  Site A describes its user profile to Advertiser B.  Advertiser B says they’d like to reach just those who are, let’s say, unmarried women under the age of 30.  Site A says no problem, send over your ad, and using an ad-server engine delivers the ad to just the desired target.  (Okay, a lot more complicated than that, but similar to how magazine lists were “rented” in the past.)

The Ugly Underbelly.  But what’s going on is often ugly and/or dishonest:  lying to join sites to scrap content; changing a privacy policy after someone has signed up to create a loophole for selling what had been private info.  Facebook’s privacy issues over the past year is the most well-known example of this right now – and Congress continues to investigate. 

Nielsen is a very large and respected media research company.  However, this Nielsen case, in my opinion, is just another part of the ugly underbelly – “mindless parasites” that weren’t programmed to consider the ramifications of their methodology.    Nielsen joined PatientsLikeMe.com with the intent of eavesdropping, not participating as a member.  Not okay – and I respect them less for having done so.

The good companies doing social media (text) analysis will seek permission from a site to analyze its content for a client (who’s likely to be a potential advertiser).  You might ask why a company would agree to have their content scraped.  It’s simple:   It’s another revenue stream.

Setting Some Standards Today. 
There are many aspects to this debate and I really urge you to think about what the possible solutions might be.  The leading market research organizations around the world, including ESOMAR, are dealing with these topics right now.  In the meantime, here are my initial thoughts:

• If a comment was made on a public blog, it can be quoted in a report delivered to a client.
• If a quote was made by a member of a private site, with the permission of the site owner to look at the data, those findings should not be quoted, but can become part of the aggregated analysis.
• Reporting is a greater challenge for qualitative approaches which tend to rely on quotes. 
• Finally, Terms of Service will need to evolve so consumers have greater buy-in when they sign up and as policy changes are made.  Easy to say and really hard to do. 

Clearly this is a topic I’ll continue to watch closely as it impacts marketing research, including qualitative analysis of social media content.

Your thoughts?

From their website:  “The Klout Score is the measurement of your overall online influence. The scores range from 1 to 100 with higher scores representing a wider and stronger sphere of influence. Klout uses over 35 variables on Facebook and Twitter to measure True Reach, Amplification Probability, and Network Score.”

I am at best an infrequent Facebook user; I tweet maybe once a week or when I see something of interest.  My blog posts are automatically tweeted.  I comment on posts I see that are of interest, on websites and in private forums.  That’s about it.

Klout had no idea who I was when I entered my Twitter name @CarynGoldsmith.  Once I signed up, I got my (again very humbling) score of 5.  Here’s what it looked like:

Seth Godin, a well-known marketing guru who focuses on digital delivery of his books and blogs, but isn’t into Twitter, has a Klout score that looks like this:

Clearly, much more influence due to the reach of his blog, which must be why his score is as high as it is.

Turn this around and look at a very active younger research professional I know and see that he’s much more likely to be driving the conversation online between people:

Reach=size of your engaged audience; Amplification=likelihood your info will be acted upon by others; Network=influence level of you engaged audience.

I don’t have an issue with a score like this being created, but one must suspend logic and believe that the online space is the entire universe.  That’s too much for me.  Just last year, I quoted the very respected Spike Jones (Brains on Fire) when he said that 90% of word-of-mouth marketing happens offline.  That’s right, offline.

The online space is very verbal.  And while it can be visual with YouTube videos and the like, a score like this focuses on words which are often tangible – tweets, retweets, mentions, likes, etc.   Further, the focus really is on influencers and not trendsetters.  Someone’s ability to influence others is terrific, but if I’m looking for those who can help refine a product concept or redesign a service, that may be someone completely different.

Nevertheless, Klout seems to have done a good job with building their score.  And we do need a good way to identify influencers online.  Yet, if we are recruiting for qualitative research – even if it’s to take place online –  we need to take some care here:

• On what topics are they influential?
• Who are they influencing (other leaders, buyers, etc.)? And what is that sphere of influence?
• Finally, how can we augment these influencers with others who are also influencers in other ways and/or offline?

Great tool.  Glad I learned about it.  Hope to have a chance to integrate it into a project soon!

Your thoughts?

Within a couple of days, I attended a local QRCA meeting where social media was discussed at length.  It reminded me that I needed to clarify that my issues with discourse/text analytics were primarily on the quantitative level.  Clearly, any of us in marketing research, and specifically in qualitative research, have been following social media with great interest and using it where we can.

Today, my take is this:  social media review is another aspect of the due diligence I conduct when preparing to meet a client for the first time or when preparing a discussion guide for a study.  It’s always important to learn as much as we can and to have our fingers on the pulse of what’s being said.  (For the record, there are those who believe that qualitative social media mining is comparable to a qualitative research study.  I don’t agree with that point of view at all!)

The limitations I highlighted regarding quantitative text analytics apply to qualitative explorations as well.  Specifically, are you listening the “right” people online, are they a representative mix of your target?  And are the people that may have the most important things for us to hear saying it in an online public forum?

The advantage to qualitative exploration of social communications – a term I like better than social media – is that we are able to read the tweets and posts and do a better job of making sense of what we’re reading vs. machine classification (positive/neutral/negative or some other tagging structure).  More often than not , we still don’t know who the people are, but we can get an overview of what’s being said and the terms/tone being used.

The biggest hurdle we have is how to aggregate this information.  For the casual searches (those I do for myself, without client commission), I like Addictomatic.com and SocialMention.com.  (Images below.)  Addictomatic allows me to read more from their dashboard; SocialMention provides me with a directional overview and top keywords (often useful when I need to dig further).  For clients seeking more detailed, automated solutions, there is Radian6 and ObjectiveMarketer, among others.

I haven’t yet had a client give me their social-media report as a springboard for developing a qualitative study, but the time is coming.  Until then, I’ll remain proactive in including social-communications analysis as part of my process, in the best way I can.

What’s your take on including social media/communications analysis as a routine part of the qualitative process?

Addict-o-matic

SocialMention

Discourse analysis, sentiment analysis, text analytics:  whatever you call it, you’re looking at a huge volume of information.  Unstructured information.

When CRM started in the 1990s, the challenge was to link customers to behaviors and make recommendations to keep them happy/buying more.  Think of the Amazon “recommendation” feature:  “people who looked at this, bought this” or “if you liked this, may we recommend…”  They were able to “mine” purchase data to make better shopper suggestions.

Text mining is infinitely more complicated.  The longer a blog post, Facebook comment, or product review, the more difficult it is to categorize.  Twitter posts and other microblogs are theoretically easiest because they’re so short.

How It’s Done.  Comments are filtered based on keywords (e.g., brand name).  Next, as I understand it, using proprietary algorithms (depending on the software), comments are grouped into three buckets:  positive, negative, and neutral.

The longer the post, the more likely the comment is to be classified as neutral.  This isn’t surprising.  Think of the complexity in nuance, sarcasm, slang, humor and differences in word usage across different cultures speaking the same language.  (Anyone watch the Emmy’s Sunday night and see Ricky Gervais?  He was laughing at Bucky Gunts’ name and it’s taken me 10 minutes to figure out why – so bad I can’t repeat it here!)

I’ve seen statistics showing that between 65-80% of all comments are tagged as neutral.  The number is somewhat lower among the microblogs – maybe only 40%.  Nevertheless, it’s obvious that huge biases can be introduced using this approach.

Elsewhere I’ve read that humans aren’t very accurate either.  Using Mechanical Turk (which isn’t something I’d personally recommend), humans agree only 79% of time on how items should be coded.

We should be moving forward in this discipline. Tom Anderson, of Anderson Analytics, agrees.  In a correspondence with him several months back, Tom said that he was using three different software packages and gained confidence based on the intersection their joint findings.  I’m not sure if more data is successfully coded or if this just provides more confidence in what he reports as findings to his clients.  I think this is very responsible, given the accuracy of single-solution approaches today.

Currently, I’m leaning in the direction of relying more on Market Research Online Communities (or Insight Communities), which straddle the space between pure discourse analysis and more structured qualitative or quantitative marketing research.  I view them as a hybrid, where I can get out of the way and follow the discussion.  With only 200-500 members, keeping up and staying accurate is more manageable.  Further, when custom research is needed, the recruit is a snap.

And sometimes we don’t need to build communities.  One may already exist because the product or person built their following online.  In this case, the fan group may be the “go to” community because it’s larger, more representative and more cost effective than creating a new community.  Think Justin Bieber, who was “discovered” by his YouTube videos.  Those subscribing to his stream, particularly in the early days, would have been a great insight-mining resource.

I’ll be keeping a close eye on both these topics as they appeal to both my quant and qual sides.  And if you have any thoughts, I’d love to hear and learn!

Some reading:

The best of the articles I read, BrandSavant:  http://brandsavant.com/the-hidden-bias-of-social-media-sentiment-analysis/

August 2010 Quirks has several articles on social media research

Mashable:  http://mashable.com/2010/04/19/sentiment-analysis/

No wonder marketers are confused about how to use social media effectively!  The juxtaposition of the two article titles made me laugh.  (Actually, the FeverBee.com piece is rather good, offering some creative pointers on how to engage community members.)

Staying with the subject of online communities, this month’s “Quirk’s” piece on the 2009 Globalpark Market Research Software Survey reported that only 17% of companies worldwide manage at least one online community and that 56% have no plans for starting one.  (A bit more detail:  60% of small and medium companies say they have no plans, with 41% of large companies responding the same way).

Bottom Line:  From their actions, most marketers seem aware of the a fine line between staying attuned to customers and invading their privacy.  Sure, an online community is a major investment of both time and money.  But there are other ramifications that marketers are having to consider which may be slowing down this new research method’s adoption.