Dean Eckles
Cambridge
As seen in:
MSN,
Nature,
Los Angeles Times,
PLOS ONE,
PNAS,
Science Magazine,
Finger Lakes Times,
Manistee News Advocate
networks, contagion, causality. @MIT professor
not often on X
deaneckles, eckles & eckles.bsky.social elsewhere
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Dean Eckles
Promoting engagement with social fact-checks online: Investigating the roles of social connection and shared partisanship
Field experiment To examine what corrector account features promote engagement with social corrections, we conducted a field experiment on Twitter (X) in which we identified users who shared links to fact-checked false political articles and replied to their tweets with public corrective messages from human-appearing bot accounts. Our corrective messages contained short messages questioning the veracity of the false story claim and provided a link to a fact-checking article from Snopes.com.
Long ties accelerate noisy threshold-based contagions - Nature Human Behaviour
Abstract In widely used models of biological contagion, interventions that randomly rewire edges (generally making them ‘longer’) accelerate spread. However, recent work has argued that highly clustered, rather than random, networks facilitate the spread of threshold-based contagions, such as those motivated by myopic best response for adoption of new innovations, norms and products in games of strategic complement.
Relating t-statistics and the relative width of confidence intervals
How much does a statistically significant estimate tell us quantitatively? If you have an estimate that’s statistically distinguishable from zero with some t-statistic, what does that say about your confidence interval? Perhaps most simply, with a t-statistic of 2, your 95% confidence intervals will nearly touch 0. That is, they’re just about 100% wide in each direction. So they cover everything from nothing (0%) to around double your estimate (200%).
Statistical Modeling, Causal Inference, and Social Science
Sometimes when you conduct (or read) a study you learn you’re wrong in interesting ways. Other times, maybe you’re wrong for less interesting reasons. Being wrong about the “first stage” can be an example of the latter. Maybe you thought you had a neat natural experiment. Or you tried a randomized encouragement to an endogenous behavior of interest, but things didn’t go as you expected.
Statistical Modeling, Causal Inference, and Social Science
How much do people consume news media that is mainly consumed by their co-partisans? And how do new media, including social media and their “dangerous algorithms“, contribute to this? One way of measuring the degree of this partisan co-consumption of news is a measure used in the literature on segregation and, more recently, in media economics.
thefacebook and mental health trends: Harvard and Suffolk County Community College
Multiple available measures indicate worsening mental health among US teenagers. Prominent researchers, commentators, and news sources have attributed this to effects of information and communication technologies (while not always being consistent on exactly which technologies or uses thereof).
Confusions about inference, prediction, and “probability of superiority”
People sometimes confuse certainty about summary statistics with certainty about draws from the distributions they summarize. Saying that we are quite confident that the average outcome for one group is higher than the average for the other can be taken as a claim about the full distributions of the outcomes.
Statistical Modeling, Causal Inference, and Social Science
Post navigation The connection between junk science and sloppy data handling: Why do they go together? We have this tenure-track Assistant Professor position open at MIT. It is an unusual opportunity in being a shared position between the Department of Political Science and the College of Computing.
Statistical Modeling, Causal Inference, and Social Science
There is a good deal of coverage of the more shocking reasons that papers on the psychology of dishonesty by Dan Ariely and Francesca Gino need to be corrected or retracted. I thought I’d share a more mundane example — in this same literature, and in fact in the very series of papers. There is no allegation of further fraud here, the errors are mundane, but maybe this is relevant to challenges in correcting the scientific record, etc.
New research on social media during the 2020 election, and my predictions
Back in 2020, leading academics and researchers at the company now known as Meta put together a large project to study social media and the 2020 US elections — particularly the roles of Instagram and Facebook. As Sinan Aral and I had written about how many paths for understanding effects of social media in elections could require new interventions and/or platform cooperation, this seemed like an important development.
Statistical Modeling, Causal Inference, and Social Science
I think researchers in the social sciences often have unrealistic expectations about effect sizes. This has many causes, including publication bias (and selection bias more generally) and forking paths. Old news here. Will Hobbs pointed me to his (PPNAS!) paper with Anthony Ong that highlights and examines another cause: common method bias.
Postdoctoral position at MIT: privacy, synthetic data, fairness & causal inference
Post navigation How large is the underlying coefficient? An application of the Edlin factor to that claim that “Cash Aid to Poor Mothers Increases Brain Activity in Babies” I have appreciated Jessica’s recent coverage of differential privacy and related topics on this blog — especially as I’ve also started working in this general area. So I thought I’d share this new postdoc position that Manish Raghavan and I have here at MIT where it is an important focus.
When plotting all the data can help avoid overinterpretation
Patrick Ruffini and David Weakliem both looked into this plot that’s been making the rounds, which seems to suggest a sudden drop in some traditional values: But the survey format changed between 2019 and 2023, both moving online and randomizing the order of response options. Perhaps one clue that you shouldn’t draw sweeping conclusions specific to these values is that there is a drop in the importance of “self-fulfillment” and “tolerance” too.
Successful randomization and covariate “imbalance” in a survey experiment in Nature
Last year I wrote about the value of testing observable consequences of a randomized experiment having occurred as planned. For example, if the randomization was supposedly Bernoulli(1/2), you can check that the number of units in treatment and control in the analytical sample isn’t so inconsistent with that; such tests are quite common in the tech industry.
Statistical Modeling, Causal Inference, and Social Science
In some versions of a “design-based” perspective on causal inference, the idea is to focus on how units are assigned to different treatments (i.e. exposures, actions), rather than focusing on a model for the outcomes. We may even want to prohibit loading, looking at, etc., anything about the outcome (Y) until we have settled on an estimator, which is often something simple like a difference-in-means or a weighted difference-in-means.
I had big plans for that four-fifths of a penny: False precision and fraud
Andrew likes to discourage false precision through reporting too many digits for estimates. I think this is good advice, especially for abstracts, summaries, and the primary outputs of much research.
Statistical Modeling, Causal Inference, and Social Science
Decision theory plays a prominent role in many texts and courses on theoretical statistics. However, the “decisions” being made are often as simple as using a particular estimator and then producing a point estimate — the point estimate, say, of an average treatment effect (ATE) of a some intervention is the “decision”. That is, these decisions are often substantially removed from the kinds of actions that policymakers, managers, doctors, and others actually have to make.
Does the “Table 1 fallacy” apply if it is Table S1 instead?
In a randomized experiment (i.e. RCT, A/B test, etc.) units are randomly assigned to treatments (i.e. conditions, variants, etc.). Let’s focus on Bernoulli randomized experiments for now, where each unit is independently assigned to treatment with probability q and to control otherwise. Thomas Aquinas argued that God’s knowledge of the world upon creation of it is a kind of practical knowledge: knowing something is the case because you made it so.
Shifting attention to accuracy can reduce misinformation online - Nature
Abstract In recent years, there has been a great deal of concern about the proliferation of false and misleading news on social media1,2,3,4. Academics and practitioners alike have asked why people share such misinformation, and sought solutions to reduce the sharing of misinformation5,6,7. Here, we attempt to address both of these questions. First, we find that the veracity of headlines has little effect on sharing intentions, despite having a large effect on judgments of accuracy.
Commentary: Why public health messaging should emphasize vaccine acceptance, not hesitancy
Human beings are deeply social, and that's not surprising. But this fact has taken on new urgency as we struggle to reach herd immunity in the face of new, more transmissible forms of COVID-19. One fear that public health officials have repeatedly articulated is vaccine hesitancy — that despite the availability of safe vaccines, not enough people will accept them, creating insufficient protection for us all.
Why public health messaging should emphasize vaccine acceptance, not hesitancy
Sinan Aral and Dean Eckles, Published 4:05 pm EST, Thursday, February 25, 2021 (TNS) Human beings are deeply social, and that's not surprising. But this fact has taken on new urgency as we struggle to reach herd immunity in the face of new, more transmissible forms of COVID-19. One fear that public health officials have repeatedly articulated is vaccine hesitancy — that despite the availability of safe vaccines, not enough people will accept them, creating insufficient protection for us all.
Op-Ed: Why public health messaging should emphasize vaccine acceptance, not hesitancy
© Provided by The LA Times People line up in their cars at Dodger Stadium for COVID-19 vaccinations on Jan. 15. (Los Angeles Times) Human beings are deeply social, and that’s not surprising. But this fact has taken on new urgency as we struggle to reach herd immunity in the face of new, more transmissible forms of COVID-19.
Shared partisanship dramatically increases social tie formation in a Twitter field experiment
Abstract Americans are much more likely to be socially connected to copartisans, both in daily life and on social media. However, this observation does not necessarily mean that shared partisanship per se drives social tie formation, because partisanship is confounded with many other factors. Here, we test the causal effect of shared partisanship on the formation of social ties in a field experiment on Twitter.
Images and Misinformation in Political Groups: Evidence from WhatsApp in India
WhatsApp is a key medium for the spread of news and rumors, often shared as images. We study a large collection of politically-oriented WhatsApp groups in India, focusing on the period leading up to the 2019 Indian national elections. By labeling samples of random and popular images, we find that around 10% of shared images are known misinformation and most fall into three types of images.
Protecting elections from social media manipulation
Policy ForumScience and Democracy Sinan Aral1,2,3, Dean Eckles1,2 1Sloan School of Management, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. 2Institute for Data, Systems, and Society, MIT, Cambridge, MA, USA. 3Manifest Investment Partners, Tiburon, CA, USA. Email: sinan{at}mit.edu See allHide authors and affiliations Science 30 Aug 2019: Vol. 365, Issue 6456, pp.
Protecting elections from social media manipulation
Policy ForumScience and Democracy Sinan Aral1,2,3, Dean Eckles1,2 1Sloan School of Management, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. 2Institute for Data, Systems, and Society, MIT, Cambridge, MA, USA. 3Manifest Investment Partners, Tiburon, CA, USA. Email: sinan{at}mit.edu See allHide authors and affiliations Science 30 Aug 2019: Vol. 365, Issue 6456, pp.
Rumor Cascades
Online social networks provide a rich substrate for rumor propagation. Information received via friends tends to be trusted, and online social networks allow individuals to transmit information to many friends at once. By referencing known rumors from Snopes.com, a popular website documenting memes and urban legends, we track the propagation of thousands of rumors appearing on Facebook.
Field studies of psychologically targeted ads face threats to internal validity
As some cities struggle with population declines, vacant lots are proliferating. Where residents often see blight, researchers see the potential for ecological oases. Can these derelict spaces contribute to conservation?
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Dean
Contact Dean, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack