Dean Eckles

I am a social scientist and statistician. Much of my research is on social influence that is mediated by interactive technologies. The design of systems, interventions, and methods is sometimes both a means and an end.

I am a member of the MIT Sloan School of Management faculty, in the Marketing group. I am affiliated with the MIT Schwarzman College of Computing via the Institute for Data, Systems & Society, including its Statistics and Data Science Center. I lead the analytics research area at the Initiative on the Digital Economy, via which we also organize the annual Conference on Digital Experimentation (CODE@MIT).

The principal subjects of my research are

social interactions, contagion, and interventions in networks
experimental design and inference in networks
methods for causal inference

Multiple of these often end up appearing together in a single project.

I try to use Twitter (@deaneckles) and, less often, my blog and the Gelman et al. blog to share ideas and report on research in a more immediate and informal way than scholarly publications. I’m even on Bluesky, Mastodon, and Threads.

Featured work

Long ties, disruptive life events and economic prosperity. PNAS.
50 years after Granovetter’s “The Strength of Weak Ties”, we provide new evidence of the connection between network structure and economic prosperity. We also show how life events, like switching high schools, can result in more long ties, even decades later. This paper is accompanied by new public data on the Facebook networks of people in US and Mexican zip codes .

Algorithmic transparency and assessing effects of algorithmic ranking. Testimony before the US Senate Subcommittee on Communications, Media, and Broadband.
This addresses the questions: How does algorithmic ranking in social media work? How can we assess algorithmic impact & amplification? What can policy-makers do here?

Seeding with costly network information. Operations Research.
Because of their position in a social network, some people can be much more influential than others, and so marketers and others often want to target them as “seeds”. But, outside of specific cases, one doesn’t fully observe the social network, but must probe it (with costly surveys or crawling). We develop techniques for selecting good seeds in arbitrary networks while limiting how much we need to probe the network.

Providing normative information increases intentions to accept a COVID-19 vaccine. Nature Communications.
When does sharing information about others’ behaviors cause more of that same behavior? This is both a central question in the social sciences and also important for effecting preventative health behaviors, including vaccination. We use a randomized experiment embedded in the international survey on COVID-19 we conducted with Meta and JHU to address this, providing evidence that these descriptive norms increase intentions to get vaccinated.