About

Dean Eckles is the William F. Pounds Professor of Management and a Professor of Marketing at MIT Sloan. He is also the Associate Director of the Institute for Data, Systems & Society in the Schwarzman College of Computing. His substantive research examines people's interactions with and through communication technologies, especially how these technologies mediate and direct social influence. Eckles works on applied statistics, design of field experiments, and causal inference to support his research. Prior to joining MIT, he was a scientist at Facebook and Nokia. He holds a BA in philosophy, a BS and MS in cognitive science, an MS in statistics, and a PhD in communication, all from Stanford University.

Research topics

• Computer Science
• Political Science
• Business
• World Wide Web
• Internet privacy
• Advertising
• Computer Security
• Cognitive psychology
• Psychology
• Marketing
• Internal medicine
• Virology
• Medicine
• Mathematics
• Physics
• Social psychology

Selected publications

• Promoting engagement with social fact-checks online: Investigating the roles of social connection and shared partisanship

  PLoS ONE · 2025-03-31 · 1 citations

  articleOpen accessCorresponding

  Social corrections - where users correct each other - can help rectify inaccurate beliefs. However, social corrections are often ignored. Here we ask under what conditions social corrections promote engagement from corrected users, allowing for greater insight into how users respond to debunking messages (even if such responses are negative). Prior work suggests two key factors may help promote engagement with corrections - partisan alignment between users, and social connections between users. We investigate these factors here. First, we conducted a field experiment on Twitter (X) using human-looking bots to examine how shared partisanship and prior social connection affect correction engagement. We randomized whether our accounts identified as Democrat or Republican, and whether they followed Twitter users and liked three of their tweets before correcting them (creating a minimal social connection). We found that shared partisanship had no significant effect in the baseline (no social connection) condition. Interestingly, social connection increased engagement with corrections from co-partisans. Effects in the social counter-partisan condition were ambiguous. Follow-up survey experiments largely replicated these results and found evidence for a generalized norm of responding, wherein people feel more obligated to respond to people who follow them - even outside the context of misinformation correction. Our findings have important implications for increasing engagement with social corrections online.

  PublisherOA PDFDOI

• Policy relevance of causal quantities in networks

  arXiv (Cornell University) · 2025-07-18

  preprintOpen accessSenior author

  In settings where units' outcomes are affected by others' treatments, there has been a proliferation of ways to quantify effects of treatments on outcomes, including via indirect exposure to other units' treatments. Here we consider two properties we might want estimands to have: being interpretable as summaries of unit-level effects, and being relevant to choice of a policy governing treatment assignment. We characterize many estimands as involving one of two orders of averaging over units in a population and over treatment assignments under a policy. The more common representation often results in quantities that are insufficient for optimal policy choice. This occurs because these quantities summarize outcomes under homogeneous exposure to treatment, but even homogeneous policies often lead to heterogeneous exposures. The other representation often yields quantities that lack an interpretation as summaries of unit-level effects. We argue that, among various estimands, the expected average outcome, which averages over units and treatment assignments in either order, deserves further attention from researchers. This estimand, or contrasts among these estimands under different policies, is both a summary of unit-level effects and is sufficient for optimal policy choice with utilitarian welfare.

  PublisherOA PDFDOI

• Tendencies toward triadic closure: Field-experimental evidence

  2025-05-28

  preprintOpen access

  Empirical social networks are characterized by a high degree of triadic closure (i.e.,transitivity, clustering), whereby network neighbors of the same individual are also likely tobe directly connected. It is unknown to what degree this results from dispositions to formsuch ties (i.e., to close open triangles) per se or from other processes, such as homophily andmore opportunities for exposure. These are difficult to disentangle in many settings, but insocial media not only can they be decomposed, but platforms frequently make decisions thatdepend on these distinct processes. Here, using a field experiment on social media, werandomize the existing network structure that a user faces when followed by a target accountthat we control, and we examine whether they reciprocate this tie formation. Being randomlyassigned to have an existing tie to an account that follows the target user increases tieformation by 35%. Through the use of multiple control conditions in which the relevant tie isabsent (never existent or removed), we attribute this effect specifically to a minimal cue thatindicates the presence of a potential mutual follower. Theory suggests that triadic closureshould be especially likely in open triads of strong ties, and we find larger effects when thesubject has interacted more with the existing follower. These results indicate a substantialrole for tendencies toward triadic closure, but one that is substantially smaller than whatmight be inferred from prior observational studies. Platforms and others may rely on thesetendencies in encouraging tie formation, with broader implications for network structure andinformation diffusion in online networks.

  PublisherOA PDFDOI

• Promoting engagement with social fact-checks online: Investigating the roles of social connection and shared partisanship

  2025-04-07

  preprintOpen access

  Social corrections – where users correct each other – can help rectify inaccurate beliefs. However,social corrections are often ignored. Here we ask under what conditions social corrections promoteengagement from corrected users, allowing for greater insight into how users respond to debunkingmessages (even if such responses are negative). Prior work suggests two key factors may helppromote engagement with corrections – partisan alignment between users, and social connectionsbetween users. We investigate these factors here. First, we conducted a field experiment on Twitter(X) using human-looking bots to examine how shared partisanship and prior social connectionaffect correction engagement. We randomized whether our accounts identified as Democrat orRepublican, and whether they followed Twitter users and liked three of their tweets beforecorrecting them (creating a minimal social connection). We found that shared partisanship had nosignificant effect in the baseline (no social connection) condition. Interestingly, social connectionincreased engagement with corrections from co-partisans. Effects in the social counter-partisancondition were ambiguous. Follow-up survey experiments largely replicated these results andfound evidence for a generalized norm of responding, wherein people feel more obligated torespond to people who follow them – even outside the context of misinformation correction. Ourfindings have important implications for increasing engagement with social corrections online.

  PublisherOA PDFDOI

• Tendencies toward triadic closure: Field experimental evidence

  Proceedings of the National Academy of Sciences · 2025-06-30 · 3 citations

  articleOpen access

  Empirical social networks are characterized by a high degree of triadic closure (i.e., transitivity, clustering): network neighbors of the same individual are also likely to be directly connected. It is unknown to what degree this results from dispositions to form such ties (i.e., to close open triangles) per se versus other processes such as homophily and more opportunities for exposure. These mechanisms are difficult to disentangle in many settings. On social media, however, they can be decomposed - and platforms frequently make decisions that depend on these distinct processes. Here, using a field experiment on social media, we randomize the existing network structure that a user faces when they are followed by a target account that we control. We then examine whether the user reciprocates this tie formation. Being randomly assigned to have an existing tie to an account that follows the target user increases tie formation by 35%. Through multiple control conditions, we attribute this effect specifically to a minimal cue that indicates the presence of a potential mutual follower. Theory suggests that triadic closure should be especially likely in open triads of strong ties, and accordingly we find larger effects when the subject has interacted more with the existing follower. These results indicate a substantial role for tendencies toward triadic closure, but one that is substantially smaller than what might be inferred from prior observational studies. Platforms and others may rely on these tendencies in encouraging tie formation, with broader implications for network structure and information diffusion in online networks.

  PublisherDOI

• Tendencies toward triadic closure: Field-experimental evidence

  2025-05-10

  preprintOpen access

  Empirical social networks are characterized by a high degree of triadic closure (i.e.,transitivity, clustering), whereby network neighbors of the same individual are also likely tobe directly connected. It is unknown to what degree this results from dispositions to formsuch ties (i.e., to close open triangles) per se or from other processes, such as homophily andmore opportunities for exposure. These are difficult to disentangle in many settings, but insocial media not only can they be decomposed, but platforms frequently make decisions thatdepend on these distinct processes. Here, using a field experiment on social media, werandomize the existing network structure that a user faces when followed by a target accountthat we control, and we examine whether they reciprocate this tie formation. Being randomlyassigned to have an existing tie to an account that follows the target user increases tieformation by 35%. Through the use of multiple control conditions in which the relevant tie isabsent (never existent or removed), we attribute this effect specifically to a minimal cue thatindicates the presence of a potential mutual follower. Theory suggests that triadic closureshould be especially likely in open triads of strong ties, and we find larger effects when thesubject has interacted more with the existing follower. These results indicate a substantialrole for tendencies toward triadic closure, but one that is substantially smaller than whatmight be inferred from prior observational studies. Platforms and others may rely on thesetendencies in encouraging tie formation, with broader implications for network structure andinformation diffusion in online networks.

  PublisherOA PDFDOI

• Promoting engagement with social fact-checks online: Investigating the roles of social connection and shared partisanship

  2025-11-11

  articleOpen access

  Social corrections – where users correct each other – can help rectify inaccurate beliefs. However,social corrections are often ignored. Here we ask under what conditions social corrections promoteengagement from corrected users, allowing for greater insight into how users respond to debunkingmessages (even if such responses are negative). Prior work suggests two key factors may helppromote engagement with corrections – partisan alignment between users, and social connectionsbetween users. We investigate these factors here. First, we conducted a field experiment on Twitter(X) using human-looking bots to examine how shared partisanship and prior social connectionaffect correction engagement. We randomized whether our accounts identified as Democrat orRepublican, and whether they followed Twitter users and liked three of their tweets beforecorrecting them (creating a minimal social connection). We found that shared partisanship had nosignificant effect in the baseline (no social connection) condition. Interestingly, social connectionincreased engagement with corrections from co-partisans. Effects in the social counter-partisancondition were ambiguous. Follow-up survey experiments largely replicated these results andfound evidence for a generalized norm of responding, wherein people feel more obligated torespond to people who follow them – even outside the context of misinformation correction. Ourfindings have important implications for increasing engagement with social corrections online.

  PublisherDOI

• Noise-induced randomization in regression discontinuity designs

  Biometrika · 2025-01-01 · 5 citations

  preprintOpen access1st authorCorresponding

  Summary Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running variable crosses a prespecified threshold. Here, we propose a new approach to identification, estimation and inference in regression discontinuity designs that uses knowledge about exogenous noise (e.g., measurement error) in the running variable. In our strategy, we weight treated and control units to balance a latent variable, of which the running variable is a noisy measure. Our approach is driven by effective randomization provided by the noise in the running variable, and complements standard formal analyses that appeal to continuity arguments while ignoring the stochastic nature of the assignment mechanism.

  PublisherOA PDFDOI

• Tendencies toward triadic closure: Field-experimental evidence

  2024-01-24

  preprint

  Empirical social networks are characterized by a high degree of triadic closure (i.e., transitivity, clustering), whereby network neighbors of the same individual are also likely to be directly connected. It is unknown to what degree this results from dispositions to form such ties (i.e., to close open triangles) per se or from other processes, such as homophily and more opportunities for exposure. These are difficult to disentangle in many settings, but in social media not only can they be decomposed, but platforms frequently make decisions that depend on these distinct processes. Here, using a field experiment on social media, we randomize the existing network structure that a user faces when followed by a target account that we control, and we examine whether they reciprocate this tie formation. Being randomly assigned to have an existing tie to an account that follows the target user increases tie formation by 35%. Through the use of multiple control conditions in which the relevant tie is absent (never existent or removed), we attribute this effect specifically to a minimal cue that indicates the presence of a potential mutual follower. Theory suggests that triadic closure should be especially likely in open triads of strong ties, and we find larger effects when the subject has interacted more with the existing follower. These results indicate a substantial role for tendencies toward triadic closure, but one that is substantially smaller than what might be inferred from prior observational studies. Platforms and others may rely on these tendencies in encouraging tie formation, with broader implications for network structure and information diffusion in online networks.

  PublisherDOI

• Tendencies toward triadic closure: Field-experimental evidence

  2024-01-24 · 3 citations

  preprintOpen access

  Empirical social networks are characterized by a high degree of triadic closure (i.e., transitivity, clustering), whereby network neighbors of the same individual are also likely to be directly connected. It is unknown to what degree this results from dispositions to form such ties (i.e., to close open triangles) per se or from other processes, such as homophily and more opportunities for exposure. These are difficult to disentangle in many settings, but in social media not only can they be decomposed, but platforms frequently make decisions that depend on these distinct processes. Here, using a field experiment on social media, we randomize the existing network structure that a user faces when followed by a target account that we control, and we examine whether they reciprocate this tie formation. Being randomly assigned to have an existing tie to an account that follows the target user increases tie formation by 35%. Through the use of multiple control conditions in which the relevant tie is absent (never existent or removed), we attribute this effect specifically to a minimal cue that indicates the presence of a potential mutual follower. Theory suggests that triadic closure should be especially likely in open triads of strong ties, and we find larger effects when the subject has interacted more with the existing follower. These results indicate a substantial role for tendencies toward triadic closure, which platforms and others can use in encouraging tie formation, with broader implications for network structure and information diffusion in online networks.

  PublisherOA PDFDOI

Frequent coauthors

• Eytan Bakshy

  53 shared

• Kiran Garimella

  Harvard University

  22 shared

• M. Amin Rahimian

  University of Pittsburgh

  21 shared

• Sinan Aral

  Massachusetts Institute of Technology

  20 shared

• David G. Rand

  Massachusetts Institute of Technology

  20 shared

• Mohsen Mosleh

  Massachusetts Institute of Technology

  17 shared

• Itamar Rosenn

  Meta (Israel)

  16 shared

• Avinash Collis

  12 shared

Labs

• MIT Sloan Data, Systems & Society LabPI

Awards & honors

• 2020 INFORMS eBusiness Section Best Paper Award
• 2018 Amazon Research Award