LinkedIn Answers, nptech, Online Fundraising, Social Networking, socialmarkets, Strategy, Yahoo!

My LinkedIn network says nonprofits need to be more accountable, transparent and professional

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Earlier this month, I asked a question on LinkedIn Answers:

As a donor, if you had one wish for the nonprofit sector, what would it be?

After nearly two weeks, I received 18 answers. Beth Kanter asked me to do an analysis of the answers. She’s very good at this sort of thing so I didn’t want to let her down by doing a crappy job on it. This posed a neat little dilemma. What kind of analysis should I use? I decided to go the extra-geeky route (of course). I decided to do the following:

  1. Extract all the answers off the LinkedIn Answers page.
  2. Concatenate all the answers into one text stream.
  3. Use the Yahoo! Term Extraction API to create a list of keywords from the text stream I created by entering it into a form over at Blogoscoped. Those of you who don’t know anything about the Yahoo! Term Extraction API can think of it as a way for software to assess unstructured text and pull key terms out of it. It’s actually used by some blog plug-ins to help you auto-tag your blog entries. I gave it my LinkedIn answers and gave me a list of keywords.
  4. That allowed me to do a series of simple finds in Word to find the frequency of the word or phrase in the keywords list. The following is the results of that first pass over the list of words created by the Yahoo! Term Extraction API. I had to delete the key term “achieve financial” since Yahoo! mistook the text “achieve, financial” for an actual phrase. Think of this list as a kind of tagcloud for the LinkedIn answers I received.
    Number of appearances in text	Extracted Term
    8	nonprofits
    5	accountability
    4	transparency
    4	one wish
    2	professionalism
    2	charities
    2	loyalty
    1	pet peeve
    1	business acumen
    1	financial sustainability
    1	glossy magazines
    1	hot buttons
    1	independent sector
    1	valid concern
    1	professional managers
    1	waste resources
    1	volunteerism
    1	nonprofit organizations
    1	endlessly
    0	achieve financial
    
  5. I then dumped out things that referred to the question or only showed up once or obvious words like “nonprofits”. I also had to take a look at the keywords “professionalism” and “professional managers” and realized that it and the word “professional” were actually tied together and showed up four times instead of 2. That gave me the following list:
    Number of appearances in text	Extracted Term
    5	accountability
    4	transparency
    4	professionalism
    

Clearly, a pattern is already being generated here. The question was asked of a mixed-crowd of nonprofit and private sector individuals. A large portion of them were asking for accountability, transparency and professionalism. What’s fun about this entire exercise is that we now have a way to extract good data from LinkedIn Answers without having to have Beth Kanter’s cyberbrain when it comes to recognizing patterns in data. And of course, it says a lot about the nonprofit sector and definitely makes me feel like I’m on the right track with my socialmarkets project. It’s also a neat demonstration of how to use Web 2.0 properties to actually derive knowledge from a mass of unstructured data.

I’d love to continue the experiment on a much larger basis and I hope there’s enough information in this posting for you to try out your own Yahoo!-fueled experiments with unstructured survey questions. If you wouldn’t mind using your LinkedIn network to ask the same question, I’d love to see if the responses change depending on who asks it and what kind of network they have. My suspicion is that it won’t simply because LinkedIn networks expand exponentially by the 3rd degree thus negating any unique aspects of a person’s network but I’d still love to confirm that suspicion.

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20 Comments

  • On 08.18.07 TeacherJay said:

    I hadn’t heard about Yahoo! Term Extraction, but that definitely sounds like a useful tool. As for the terms, it’s interesting to see “accountability” being brought up – is that a sign that people don’t trust non-profits with their donations? BTW – you’ve been tagged. http://teacherjay.wordpress.com/2007/08/18/teacherjay-has-been-tagged/

  • On 08.18.07 David Geilhufe said:

    How would the answer change to a reformed question? “As a donor, if you had one wish for the nonprofits I give yearly too, what would it be?”

    It is fashionable to dis the sector, but do the people that have a standard donor relationship with a nonprofit feel that the organizations they give to need to be more accountable, transparent and professional?

    Clearly, if we want to provide donors a marketplace (like a stock market) where they can pump and dump the latest sexy nonprofit with excellent quarterly outcomes, then the general perception is key… those people will have a relationship with the sector, not with a specific nonprofit.

  • On 08.18.07 Allan Benamer said:

    I’d have to say this issue isn’t “fashionable”. I’ve seen Professor Paul Light’s polls speak about these issues since 2001. It shouldn’t be surprising that the nonprofit scandals has sensitized people both in and out of our sector to these issues. The question always remains… so what are are nonprofits going to do about that loss in trust? I hate to say this David, but your dismissal of these concerns just doesn’t make sense to me. If the loss of trust is real, surely there should be some course of action that nonprofits should take.

    Let’s test your theory that people only distrust the sector and not the nonprofits they give to. Why not ask your question to your LinkedIn network and see what comes out? We’ve got an interesting methodology for parsing the answers so why not use it? And those of you who agree with David, should do the same with your networks. The data is out there — let’s be willing to learn to be data-driven in our opinion making.

  • On 08.19.07 VillageTalk Mediadesk said:

    My LinkedIn network says nonprofits need to be more accountable, transparent and professional – Non-Profit Tech Blog…

    As a donor, if you had one wish for the nonprofit sector, what would it be?…

  • On 08.19.07 michael stein said:

    David – Limiting your analysis to existing donors might be very useful – but it is extracting very different info. By analogy, a few years ago we did an analysis of prospects who DID NOT buy our software and extracted a list of top features they wished we had provided. One of my colleagues looked at the list in surprise and said “But none of our users has ever asked for those!” Pleasing your existing users (or donors) and acquiring new ones can be quite different tasks.

  • On 08.19.07 Beth Kanter said:

    I’m going to have to check out that API term extractor if it isn’t too geeky.

    Interesting results, but you can’t really say this represents all people or all views because the sampling is pretty biased and too small. It is from 18 people who self-selected to answer your question versus random selection. And, also I wonder if nonprofit professionals might have a particular bias and what about the views of people who don’t use linked in.

    With that said, it looks like a good method to assist with some survey wording so you can get a larger sampling.

  • On 08.19.07 Allan Benamer said:

    Sure, there are limitations to the survey technique but aren’t all surveys self-selected in some respect especially those on the web? There are interesting constraints on the answers themselves as every person who answers can see the answers before them as well as the fact that their survey answers are public to those in their own network. As a result, there’s social censoring going on as people are not only striving to answer the question but also to maintain their visibility within their own social network.

    And sure, it’s biased but that’s the point of a LinkedIn network right? Notice I said that it was my LinkedIn network as opposed to David’s or yours. Your network may vary. Again, this technique can be repeated on your own. It’s easy enough to do. Just stick the stream of answers into that text box I linked to and out will pop out your keywords.

  • On 08.19.07 Ed Schott said:

    Umm…what if someone was said, “I think the non-profit sector has a high degree of accountability, so I’d like to see it raise it’s level of transparency so people trust it more”?

    Wouldn’t your term extraction lead to erroneous results? In other words, doesn’t it suffer from the usual problem with keywords, that is, lack of context?

  • On 08.19.07 Allan Benamer said:

    True, but no one will ask that question because it’s too obviously leading. Notice that in my original question, I left the answers to be as open as possible so that I couldn’t be accused of stacking the deck as it were. As for lack of context, notice that I had to adjust the score for the word “professional” because it covered other words such as “professionalism” and “professional managers”. You have to know the text you’re entering and adjust accordingly. The Yahoo term extraction API shouldn’t be used as a black box as only humans can truly understand (for now) the context of the words as they appear. However, it’s a heck of a lot easier to simply snap over to the next occurrence of a keyword in a word processor instead of just guessing. Imagine if I had 180 LinkedIn Answers instead of just 18 to cover. It would be extremely difficult at that point to create a kind of gestalt that I could summarize. And in fact, this kind of text crunching is what runs many Web search engines.

  • On 08.21.07 Catherine Carey said:

    Allen,

    18 responses is not enough.

    18 responses from people in your Network is worse. It’s worse because it’s likely folks in your network are like you – while they probably like you what I mean is that they are similar to you. Being similar means they are biased. Bias is bad.

    If you were asking your network for dog food, vacation locale or doctor recommendations bias is good. You trust opinions more when people are like you.

    Bias is bad when you are trying to act dispassionately using data.

    The best group of people to ask is everyone. However, how can you ask everyone? Serious drawback – I’m a former New Yorker who thinks there are 8 million people in the world (talk about bias!) – how can you ask everyone in the world. Ok,Ok maybe everyone in the world is dumb, but we need to ask the question of everyone who donates. You could slice it down with parameters like in the USA or donations of 100 dollars or more, etc.

    Here’s what I want to know – does your LinkedIn network reach a cross section of folks who donate money to nonprofits? I’d say no. No way, not at all likely.

    I can’ agree with this: “A large portion of them were asking for accountability, transparency and professionalism.”

    Why?

    5 divided by 18 is about one-quarter.
    4 divided by 18 is about one-fifth.

    I love this: “It’s also a neat demonstration of how to use Web 2.0 properties to actually derive knowledge from a mass of unstructured data.”

    Catherine

  • On 08.21.07 Allan Benamer said:

    When you ask a question of your LinkedIn network, you’re not asking just the people in your immediate 1st degree relationships. The question is asked of everyone down to your 3rd degree. For my network, this is roughly 390,300+ people. Does it reach a cross-section, no? My network is split 60/40 between software industry people and nonprofit types. Again, this is why I say that “my” LinkedIn network is the different from say, YOUR LinkedIn network and again, this is why I ask them to repeat the experiment. However, my guess is that because of the large networks that are derived from even a handful of connections, is that there will be a strong correlation between the answers I got and the answers people will get on their networks. I’m willing to be wrong on this but it does make some sort of intuitive sense, right? So, again, let’s try the experiment — if you ask your LinkedIn network the same question, would you get similar results?

  • On 08.24.07 Ed Schott said:

    I’m still very pessimistic about keyword extraction for this purpose. It’s just bad science.

    How can one make assumptions based on single words in essay answers? The first answer posted includes the phrase “I might be answering with too narrow a focus…”. Would keyword extraction lead you to conclude that that person would like to see more “focus” among non-profits or perhaps non-profits should be more/less “narrow” (in what?)?

    If I had answered “I think non-profits are accountable and transparent enough, I’d like to see them narrow their focus”. You would have assumed simply by the appearance of the words “accountable” and “transparent” that I was another person who wished non-profits to be accountable and transparent.

    The only way I can see this analysis working to gain any real insight is if you made the question so specific that people could only answer one word, eg. “In one word, what do non-profits need to work on the most?” and then only allow space for one word in the answer.

  • On 08.24.07 Allan Benamer said:

    Ed,

    You want signal without noise. Good luck on that one. That’s not how people will answer a LinkedIn question. A question that demands a one-word answer will be seen as offputting by much of the LinkedIn community. Again, why not try the experiment on your own — see if the question you propose to ask will be answered. “Science” is not a mass of comments on a blog page or just generally being snarky and cynical, you have to see if the experiment can be repeated and report back on the results. Go back to your LinkedIn network, do not pass go, do not receive $200 and see if you can repeat the experiement.

  • On 08.30.07 Gayle Roberts said:

    Hi Allan,

    Loved this post. Though many others seemed to want to challenge the scientific validity of your technique (not a big enough sampling or the question’s phrasing impacts the results) as a professional fundraiser my sense is that your results are spot. Good work!

    I’ve one tech question for you. I tried using the form provided by Blogoscoped to run though some of my own text files. It extracted the key words, but not the number of times each word appeared. How’d you make that happen? You didn’t manually count them did you? Am trying to create more of a tag cloud like you did. Thanks for any tips.

  • On 08.30.07 Allan Benamer said:

    Thanks for the kind words, Gayle. I use a two step process:

    1. Extract the key terms at Blogoscoped.

    2. Use http://rainbow.arch.scriptmania.com/tools/word_counter.html to do a word frequency count or do it via Microsoft Word (one key term at a time).

    It’s a darn shame that LinkedIn doesn’t have an API that we can use for this sort of thing. We could use LinkedIn as a kind of personal Oracle hehe.

  • On 08.30.07 Gayle Roberts said:

    Cool, thanks!

  • On 09.08.07 Allan Benamer said:

    James Lewis has been kind enough to reproduce this experiment over at LinkedIn. You will see that the 15 answers here are somewhat different but there are several responses with “transparency” or “visibility” as a keyword in them. This experiment isn’t finished but my guess is that similar responses will occur with similar keywords. Disagree? Try it yourself on your own LinkedIn account but please report back on the results.

  • On 09.14.07 Doug Yeager said:

    Allan- sorry not to see this before… I just got finished doing the same thing with some process analysis inside a client’s organization.

    two tools stood out:
    http://www.tagcrowd.com/
    quick and useful for a cloud
    http://wwww.usingenglish.com/
    requires a free membership, but gives you more analytics

    pattern recognition is a powerful tool.
    doug

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