Words Matter In Building Intelligent Communities

The Intelligent Community Forum (ICF) is an international group of city, town and regional leaders as well as scholars and other experts who are focused on quality of life for residents and intelligently responding to the challenges and opportunities provided by a world and an economy that is increasingly based on broadband and technology.

To quote from their website: “The Intelligent Community Forum is a global network of cities and regions with a think tank at its center.  Its mission is to help communities in the digital age find a new path to economic development and community growth – one that creates inclusive prosperity, tackles social challenges, and enriches quality of life.”

Since 1999, ICF has held an annual contest and announced an award to intelligent communities that go through an extensive investigation and comparison to see how well they are achieving these goals.  Of hundreds of applications, some are selected for an initial, more in-depth assessment and become semi-finalists in a group called the Smart21.

Then the Smart21 are culled to a smaller list of the Top7 most intelligent communities in the world each year.  There are rigorous quantitative evaluations conducted by an outside consultancy, field trips, a review by an independent panel of leading experts/academic researchers and a vote by a larger group of experts.

An especially important part of the selection of the Top7 from the Smart21 is an independent panel’s assessment of the projects and initiatives that justify a community’s claim to being intelligent.

It may not always be clear to communities what separates these seven most intelligent communities from the rest.  After all, these descriptions are just words.  We understand that words matter in political campaigns.  But words matter outside of politics in initiatives, big and small, that are part of governing.

Could the words that leaders use be part of what separates successful intelligent initiatives from those of others who are less successful in building intelligent communities?

In an attempt to answer that question, I obtained and analyzed the applications submitted over the last ten years.  Then, using the methods of analytics and machine learning that I teach at Columbia University, I sought to determine if there was a difference in how the leaders of the Top7 described what they were doing in comparison with those who did not make the cut.

Although at a superficial level, the descriptions seem somewhat similar, it turns out that the leaders of more successful intelligent community initiatives did, indeed, describe those initiatives differently from the leaders of less successful initiatives.

The first significant difference was that the descriptions of the Top7 had more to say about their initiatives, since apparently they had more accomplishments to discuss.  Their descriptions had less talk about future plans and more about past successes.

In describing the results of their initiatives so far, they used numbers more often, providing greater evidence of those results.  Even though they were discussing technology-based or otherwise sometimes complex projects, they used more informal, less dense and less bureaucratic language.

Among the topics they emphasized, engagement and leadership as well as the technology infrastructure primarily stood out.  Less important, but also a differentiation, the more successful leaders emphasized the smart city, innovation and economic growth benefits.

For those leaders who wish to know what will gain them recognition for real successes in transforming their jurisdictions into intelligent communities, the results would indicate these simple rules:

  • Have and highlight a solid technology infrastructure.
  • True success, however, comes from extensive civic engagement and frequently mentioning that engagement and the role of civic leadership in moving the community forward.
  • Less bureaucratic formality and more stress on results (quantitative measures of outcomes) in their public statements is also associated with greater success in these initiatives.

On the other hand, a laundry list of projects that are not tied to civic engagement and necessary technology, particularly if those projects have no real track record, is not the path to outstanding success – even if they check off the six wide-ranging factors that the ICF expects of intelligent communities.

While words do matter, it is also true that other factors can impact the success or failure of major public initiatives.  However, these too can be added into the models of success or failure, along with the results of the textual analytics.

Overall, the results of this analysis can help public officials understand a little better how they need to think about what they are doing and then properly describe it to their citizens and others outside of their community.  This will help them to be more successful, most importantly for their communities and, if they wish, as well in the ICF awards process.

© 2020 Norman Jacknis, All Rights Reserved

Trump And Cuomo COVID-19 Press Conferences

Like many other people who have been watching the COVID-19 press conferences held by Trump and Cuomo, I came away with a very different feeling from each.  Beyond the obvious policy and partisan differences, I felt there is something more going on.

Coincidentally, I’ve been doing some research on text analytics/natural language processing on a different topic.  So, I decided to use these same research tools on the transcripts of their press conferences from April 9 through April 16, 2020.  (Thank you to the folks at Rev.com for making available these transcripts.)

One of the best approaches is known by its initials, LIWC, and was created some time ago by Pennebaker and colleagues to assess especially the psycho-social dimensions of texts.   It’s worth noting that this assessment is based purely on the text – their words – and doesn’t include non-verbal communications, like body language.

While there were some unsurprising results to people familiar with both Trump and Cuomo, there are also some interesting nuances in the words they used.

Here are the most significant contrasts:

  • The most dramatic distinction between the two had to do with emotional tone. Trump’s words had almost twice the emotional content of Cuomo’s, including words like “nice”, although maybe the use of that word maybe should not be taken at face value.
  • Trump also spoke of rewards/benefits and money about 50% more often than Cuomo.
  • Trump emphasized allies and friends about twenty percent more often than Cuomo.
  • Cuomo used words that evoked health, anxiety/pain, home and family two to three times more often than Trump.
  • Cuomo asked more than twice as many questions, although some of these could be sort of rhetorical – like “what do you think?”
  • However, Trump was 50% more tentative in his declarations than Cuomo, whereas Cuomo had greater expressions of certainty than Trump.
  • While both men spoke about the present tense much more than the future, Cuomo’s use of the present was greater than Trump’s. On the other hand, Trump’s use of the future tense and the past tense was greater than Cuomo’s.
  • Trump used “we” a little more often than Cuomo and much more than he used “you”. Cuomo used “you” between two and three times more often than Trump.  Trump’s use of “they” even surpassed his use of you.

Distinctions of this kind are never crystal clear, even with sophisticated text analytics and machine learning algorithms.  The ambiguity of human speech is not just a problem for machines, but also for people communicating with each other.

But these comparisons from text analytics do provide some semantic evidence for the comments by non-partisan observers that Cuomo seems more in command.  This may be because the features of his talks would seem to better fit the movie portrayal and the average American’s idea of leadership in a crisis – calm, compassionate, focused on the task at hand.

© 2020 Norman Jacknis, All Rights Reserved