Happiness is trending downward, globally, according to a new study about Twitter using an “hedonometer” to assess tweets.

The paper is interesting material, although a challenge to fully appreciate in places (given that this study could have been book-length).

Here are their opening remarks:

One of the great modern scientific challenges we face lies in understanding macroscale sociotechnical phenomena–i.e., the behavior of decentralized, networked systems inextricably involving people, information, and machine algorithms–such as global economic crashes and the spreading of ideas and beliefs [1]. Accurate description through quantitative measurement is essential to the advancement of any scientific field, and the shift from being data scarce to data rich has revolutionized many areas [2]–[5] ranging from astronomy [6]–[8] to ecology and biology [9] to particle physics [10]. For the social sciences, the now widespread usage of the Internet has led to a collective, open recording of an enormous number of transactions, interactions, and expressions, marking a clear transition in our ability to quantitatively characterize, and thereby potentially understand, previously hidden as well as novel microscale mechanisms underlying sociotechnical systems [11].

What’s the upshot for infrastructure folks? Check out the first line above:

One of the great modern scientific challenges we face lies in understanding macroscale sociotechnical phenomena–i.e., the behavior of decentralized, networked systems inextricably involving people, information, and machine algorithms–such as global economic crashes and the spreading of ideas and beliefs…

This is something of our challenge in research too. However, even this huge, abstract portrayal of the problem — as being about people, info, and algorithms that bind them — happens somewhere. It is the sum of microscale sociotechnical phenomenoa, no doubt, but, of course, that does not make these trends “macro” out of hand (other than in the constructivist’s sense that we literally “make” them macro). So, there is a matter of scale (thanks again, Kathryn, for all your thoughts on scalar issues), and I wonder if the solution, a la Tom Gieryn, is to reduce our concerns over scale and instead focus on topographical depictions/conceptualizations (?).

In closing, and I have no real explanation ready-at-hand, what is are the “algorithms that bind” people and information (or resources) in infrastructure studies? This might unlock some new territory for case study experts and more quantitative types (like the authors cited above) to work together in future research…