You have fewer close family and friends (strong ties) than you have friends/acquaintances and cohorts (weak ties).

If you make a recommendation and it goes onto your wall – the number of weak ties that see the message will be greater than the number of strong ties. If you have 100 Facebook Friends, power law that out along weak tie and strong tie. There are far more weak ties.

However, what we do not know (yet) is if (or how much) the attention paid to such statements by weak ties is inversely less. The degree of attention fragmentation on wall posts is something only Facebook would really be able to answer.

In the meantime then, for the foreseeable future, strategic analysts will have to go back to Grannovetter and Godes/Mayzlin.

Guess it depends if you consider family to be friends…:0

Indeed, this paper was a snapshot in time – it doesn’t answer the question ‘what is the incremental effect of Facebook on a user’ outright. The answer to that question would have a lot to do with the impact of the strategy upon that base.

Such an analysis, on the incremental value of fan-dom, is the next logical step. It would require a very different instrument.

I’m pulling out the key questions you’ve posed below because they’re very salient.

I have hypotheses on these. It’s going to be fascinating to do the incremental study and use it to really inform/drive strategy.

Thanks for recognizing this as the first step. I hope (a fourth hope) that now we have a baseline, we can start talking delta.

Key questions:

1. “What is the spend like when a non-fan becomes a fan through family or friend interactions?”
2. “How much of that value would have occurred anyway without Facebook, and how much is *because* of Facebook? It’s one thing to say “Fans spent X”, quite another to say they spent *because* they were fans.”
3. “Is “de-fanning” or “re-fanning” predictive of purchase behavior?”
4. “What if there is a correlation between de-fanning or non-activity as a fan and reduction in likelihood to purchase?”

“Facebook fans were more loyal to the fanned brand than consumers who were not fans” is the nub of the issue when looking for incrementality here – of course they are more loyal, that’s why they “fanned” in the first place. So how did they get “loyal”? They were loyal first, then fanned. So in many cases, Facebook doesn’t create loyalty, it simply allows for the expression of it.

Which brings us to “Fanning has a demonstrable impact on others”, which seems to me, where you start generating incremental benefits. The question here is, does “would likely become a fan of a brand” generate actual sales? We know loyals outspend non-fans, that’s logical and mostly pre-determined by other marketing efforts.

But what is the spend like when a non-fan becomes a fan through family or friend interactions? I didn’t see that number anywhere, but that’s the one that would convince me to invest in Facebook – that these incremental contacts / fannings generate incremental Sales. If they don’t, then dollar for dollar, I would just buy media, where I can measure the incremental sales generated.

I understand imputing McDonald’s Facebook Fan value to be $580,003,461. The real question is, how much of that value would have occurred anyway without Facebook, and how much is *because* of Facebook? It’s one thing to say “Fans spent X”, quite another to say they spent *because* they were fans. We know the most loyal customers are the most likely to buy already, right?

I’m sure you’re working on that, Christopher…

Would also love to see anything on “de-fanning” or “re-fanning” as predictive of purchase behavior. I have seen this anecdotally and might be where the real power lies in social media – not as a media substitute, but for the saving or re-capture of sales, where the incrementality would be crystal clear, and data is typically quite scarce for the Brand folks. End user merchants can measure customer defection quite easily, Brand folks are just getting their first data points through social.

What if there is a correlation between de-fanning or non-activity as a fan and reduction in likelihood to purchase? THAT would be something every Brand would need on their dashboard – could be used to study changes in formulation, packaging, pricing, all of it.

Nope. The term ‘topic bearing’ originates from the computational linguistics field. Word-of-Mouth’ originates from the marketing science field (or so I believe). The combination of both terms is unique to a person at that nexus. The term is distinguished from ’sentiment bearing’ or ‘opinion bearing’. It’s not a buzz word though. Both terms exist in their in respective communities. It’s a mashup.

– Is there something out there that could answer a particular framed question rather than giving sentiment? For instance, “I want to know how *shocking* North American 65+ Christians find that brand/topic/concept”, or “How *surprised* is the user/buyer/payer of my product by our last product launch. Were they *anticipating* a product launch?”… Indeed, *sentiment* is pretty useless.

Not to my knowledge. Not with any degree of reliability or validity yet. Such specific databases might exist somewhere, but not in a format that you or I can use. Subjective subjectivities are subjective.

– As for simple metrics, can we use a few simple emotions/moods/attitudes/values typologies and generate some good-looking matrix? I mean, there’s ton of that stuff out there, what refrain us from using it?

Have at’er.

- When talking about analysis of the real data, don’t you think you’re limited by the simple fact that talk is cheap? When researching motivation, values, attitudes, you can’t just take what people *say* for granted, you need to use disguised techniques (I’m not even talking about qualitative research) and dig further, right?

Why do people say anything online at all? What motivates somebody to generate a piece of content? Is it effective. Those are the right questions to be asking at this juncture.

-Hypothesis 1, you are testing this:
“There is no unanimous agreement among all respondents on the sentiment score of a single response to any question within the survey.”

Indeed. No two people agreed on the whole sequence of questions. I legitimately didn’t know if two people would match exactly. So, we tested for it. Whereas a few people agreed on the Total Score, the underlining composition of the answers on how they arrived at it were all different. In effect, there was underlining error masking the overall score. Tricky to show in SPSS, but that was the clearest way.

-It shows that cases where humans are randomly or equally distributed between negative/neutral/positive are rare. However, answers tend to be skewed on one sentiment (i.e a very few negative, some neutral, the majority positive), and rarely spread across extrema. So without going into chi-square tests and all of that fanciness, it looks pretty consistent, even if you can’t get a clear trinomial categorization, you still have a “sentiment”. Now, how is this helping me getting laid is another question!

Excellent! I’m really happy that you’re engaging with the data! This is excellent! That said – yes, the distribution is pretty normal. There’s an average there. That’s the average sentiment score. There underlining instability among the respondents is pretty interesting though. While there might be an average of averages, if you were to drill down, you’d get a distribution of responses. If your personal point of view diverges from what the plurality says – isn’t that going to generate dissatisfaction? Moreover – there’s fuzzyness there.

We’re not going to eliminate all that error within our lifetimes. It’s going to persist. At least by dragging it out from the shadows and saying “look. there. now can we go on”, we can get into some solid actionable insight. That’s my hope at any rate.

So, I was reading (with passion) your white paper on sentiment analysis, and this raised a few questions:
- Is there something out there that could answer a particular framed question rather than giving sentiment? For instance, “I want to know how *shocking* North American 65+ Christians find that brand/topic/concept”, or “How *surprised* is the user/buyer/payer of my product by our last product launch. Were they *anticipating* a product launch?”… Indeed, *sentiment* is pretty useless.
- As for simple metrics, can we use a few simple emotions/moods/attitudes/values typologies and generate some good-looking matrix? I mean, there’s ton of that stuff out there, what refrain us from using it?
- When talking about analysis of the real data, don’t you think you’re limited by the simple fact that talk is cheap? When researching motivation, values, attitudes, you can’t just take what people *say* for granted, you need to use disguised techniques (I’m not even talking about qualitative research) and dig further, right?
Now, let me have a try at challenging your paper. I’ve just passed the exam of my marketing research class a few days ago, had done a project with SPSS, so I’m a enthusiastic noob

Hypothesis 1, you are testing this:
“There is no unanimous agreement among all respondents on the sentiment score of a single response to any question within the survey.”
I’m shocked! you’re testing the absolute 0% variation in answers. How significant is that?
I’m looking at another insight. I wanted to know if responses were *consistent*, I don’t mind if 90% said positive, 5% neutral and 5% positive. For me, that’s acceptable, and it shows human appreciation is somewhat consistent. I don’t understand your six-sigma obsession of sentiment analysis
To back up your argumentation, you said that because the range was 2, it means there is a dichotomy in every set of answer (“For every question, there was somebody who thought the statement was positive and somebody else who thought the statement was negative.”) Again, you weren’t interested in testing consistency, but simply looking at the least inconsistency, which is unfair to my opinion.
Well let’s have some fun, and look at distribution mister Berry!
My God it’s 5:41 am. I’m waiting for SPSS to download on this computer.
–**–
Yeah so I still have to work my way through stats. Here is what I came up with, simple percentage distribution bar charts.
http://docs.google.com/View?id=dcjz9dh2_10gj6gznc5
It shows that cases where humans are randomly or equally distributed between negative/neutral/positive are rare. However, answers tend to be skewed on one sentiment (i.e a very few negative, some neutral, the majority positive), and rarely spread across extrema. So without going into chi-square tests and all of that fanciness, it looks pretty consistent, even if you can’t get a clear trinomial categorization, you still have a “sentiment”. Now, how is this helping me getting laid is another question!