Saturday, May 06, 2017

My 2 new books out: Swarm Leadership and Sociometrics

This week my 2 new books Swarm Leadership and Sociometrics were published by Emerald:

Swarm Leadership and the Collective Mind
Using Collaborative Innovation Networks to Build a Better Business
(from the book cover) The future of business is swarm business. Whether it’s at Uber, Airbnb, Tesla, or Apple, it’s not about being a fearlessleader, but about creating a swarm that works together in collective consciousness to build great things and generate success. In this pioneering guide, MIT researcher and entrepreneur Peter Gloor shows how you can take your business on a journey from homo competitivus to homo collaborensis, channeling the competitive energies of all of your stakeholders toward collaboration.
The journey starts with recruiting and assembling an intrinsically motivated group of early enthusiasts, the Collaborative Innovation Network. These teams combine the principles of social quantum physics to create collective consciousness: empathy which builds entanglement, and reflection which leads to refocus. Gloor then demonstrates how collaboration can be tracked and boosted using the six honest signals of collaboration, which will further increase the performance of the swarm. These fundamental concepts are illustrated with a wealth of examples from leading ventures–from household names like Uber to Fortune 500 high tech firms and healthcare organizations.
buy from Amazon

Sociometrics and Human Relationships
Analyzing Social Networks to Manage Brands, Predict Trends, and Improve Organizational Performance 
Today we can use social media to not only find the next great restaurant but to address complex problems impacting society. Using a variety of tools and software, Sociometrics and Human Relationships provides an in-depth tutorial to analyzing social networks for practitioners and students with backgrounds in marketing, design, sociology, psychology, and the humanities. Employing these straightforward but powerful software tools, MIT researcher and entrepreneur Peter Gloor demonstrates how to gauge all types of online social networks such as Twitter, Wikipedia, Blogs, Facebook, as well as e-mail or Skype logs to predict election outcomes, perception and strength of brands, customer and employee satisfaction, and even fraudulent behavior. A targeted guide with step-by-step instructions, turning social buzz into business strategies by translating the latest academic research into practical techniques. Gloor provides a wealth of examples of how to apply social network analysis for prediction of trends and also illustrates how even email can improve organizational performance by optimizing communication and collaboration.
buy from Amazon

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Thursday, April 06, 2017

Why Should We Measure Happiness?

Our team is currently working on measuring happiness using the body sensors of a smartwatch. In particular we have developed "happimeter" software to measure individual happiness, and show the influence of individuals on team happiness. Our premise is that measuring and giving feedback about happiness will increase individual and team happiness.

When I describe our work to potential sponsors and other interested people, at some point in the discussion invariably the question comes up: WHY measuring happiness?

When I then answer that our goal is to better understand what the determinants of happiness are to increase individual and team happiness, I get one of two reactions:

Particularly in the US, most of the time, people will say "ah, I understand,  if you have happier people working with you, their productivity will go up, and the company will make more money." In other words, the capitalist's reaction is: "make people happier, so my company makes more money".

The second reaction, which I experience more seldom is that people will say "ah, that's cool, let's understand how happiness works so we can make our people happier". In other words, being happy is the main goal in itself.

I know which reaction I like better. What do you think?

Thursday, February 09, 2017

Finding Fatherlanders, Nerds, and Treehuggers on Social Media

In my previous blog post I introduced the three big virtual tribes of our Western world, which I called the Jingoists, Progressives, and Treehuggers. In more popular terms, the Jingoists can also be called the “Fatherlanders”, as they believe in God and the Fatherland, while “Nerds” is another term to describe the Progressives, whose religion is science and technology.  Each of the three tribes has their distinctive leaders and role models.
The Fatherlanders look up to Donald Trump, who promises to makeAmericaGreat again, cutting back on almost everything inside the fatherland except on a strong army and building a wall around the fatherland. The Nerds identify with science geeks like Elon Musk whose goal in life it is to bring humanity to the Mars. The treehuggers admire Pope Francis who is organizing conferences about global warming and climate change in the Vatican.

In this post I will illustrate how our Coolhunting approach makes it possible to find the key topics and news of the days for each of the three tribes.  The goal will be to identify the language that each of the three tribes speak, defining a vocabulary and networking behavior for each of the tribes indicative of its value system. My core hypothesis is that each of the three tribes will have its own sphere of collective consciousness.
Towards that goal I collected 4000 tweets each about Donald Trump, Elon Musk, and Pope Francis on February 4, 2017. I used Condor’s Twitter EgoFetcher, which also factors in the popularity of the people retweeting about these three people.

Technically speaking, Condor's EgoFetcher takes the first 4000 results for the search string, for example “Donald Trump”, as well as the first 100 retweets for each of these tweets. It constructs a link between two tweeters if one is retweeting or mentioning the other in her tweet. It also adds all the tweets of the 480 most influential of these tweeters, measured as their popularity (degree centrality).
I collected the following tweets in total:
“Donald Trump”
      71,521 tweets from Mar 15, 2007 to Feb 4, 2017
      71,520 actors, 349,539 ties
“Pope Francis”
      61,617 tweets from Apr 24, 2009 to Feb 4, 2017
      61,616 actors, 344,188 ties
“Elon Musk”
      24,813 tweets from Mar 7, 2009 to Feb 4, 2017
      24,812 actors, 312,728 ties

The resulting network for each of the three tribal leaders looks very similar, below is the picture for Elon Musk as an example.
We get a big connected component in the center of the network, this is the active tweeters retweeting and mentioning each other. In the periphery, we have the “asteroid” belt”, made up of the “nobodies”, tweeting their lungs out about “Elon Musk”, while nobody is listening. The coloring tells us that the largest groups of tweeters are from the US Pacific, Eastern and Central Time zones.

Zooming in on the connected component leads to the following picture. Coloring of the actors (the people tweeting) is by using Condor’s cluster detection algorithm to find the largest subgroups.
The most surprising finding in this picture is that although I have been collecting tweets about “Elon Musk”, the towering presence of realDonaldTrump is overbearing: he is more central (size of the nodes is by betweenness centrality) than even elonMusk.
Showing the same picture for Pope Frances leads to the same conclusion:

The realDonaldTrump is more central in the Twitter network about the Pontifex, than then Pontifex himself. And the community about the realDonaldTrump is the largest one in the different subcommunities about the Pope.
Finally the network about Donald Trump:
The realDonaldTrump dominates his own network, surrounded by his tribe (in yellow). The only actor who can keep up with the realDonaldTrump is “YouTube”. Or in other words: Donald Trump’s entertainment value is unsurpassed these days!

Looking at the contents of the Tweets in each of the three Twitter networks confirms the picture. Here is the most frequent words in the Tweets collected about “Donald Trump”. Not surprisingly, Trump (in green, this means he is seen quite positive) dominates as the “top story”.

Looking at the word cloud about the Pope, Trump (in green) is far more important than the “pope”, “jesus”, and “god”.
Looking at the word cloud of Elon Musk, Trump “trumps them all…” again, with “Elon Musk” being rather small even in his own word cloud, technology words such as “app”, “techcrunch”, snap”, “google”, “apple” show up quite prominently instead.

Honest Signals of Communication
Let’s now have a look at the hidden “honest signals” of each of the three tribes. See for example my Youtube video for an explanation of these.

Looking at the density of the network, we see that Trump’s retweet network is the most connected, while the Musk network is quite unconnected. This means that whatever is said about Trump triggers a lot of responses, with tweets about the Pope being a close second, while tweets about technology do not raise much traffic.

Looking at the other social network metrics, this is confirmed by the analysis of emotionality and sentiment. All three tribes are over the happiness threshold of 0.5 (which is neutral sentiment), with the Nerd tribe of Musk being the happiest, but also the least emotional one. AWVCI (Average Weighted Variance in Contribution Index) is a metric for the level of group flow, the lower AWVCI is, the more the group is in synch. We find that the Trump tribe has the highest internal turmoil (highest AWVCI). The Fatherlanders also show the strongest central leadership by degree and betweenness centrality by its two leaders realDonaldTrump and YouTube.

The last comparative chart shows the passion and respect measured as speed of response in retweeting, the Fatherlander tribe has by far the fastest response time at about 12 hours on average, which makes it the most passionate. How quickly others retweet is a proxy for respect, here the Treehuggers lead by a small margin over the Fatherlanders. On the other hand, Nerds need the least nudging, on average just a bit more than one tweet, until somebody responds, while the Fatherlanders and Treehuggers who respond (there is a large group that does not respond at all) only respond on every second tweet. 

Finally, we also find that the Nerds are using more complex language than the Fatherlanders and the Treehuggers, as can be expected when talking about complex technology topics.
To resume, we find that the Fatherlanders, led by Donald Trump, are on a roll, but show a lot of internal turmoil. The Treehuggers are very much dominated by the Fatherlanders, as they share a similar vocabulary and communication structure. In comparison, the Nerds seem to live on a more peaceful planet, with less internal turmoil, dominated by high-tech and stock market discussion, but also visibly influenced by Donald Trump as shown in their network picture and word cloud. In conclusion, we find two spheres of collective consciousness, one occupied by Fatherlanders and Treehuggers looked in an mutual struggle, and a second one, the Nerds, also strongly influenced by Donald Trump, but running on its own rhythm.