About

Michael Macy is a Distinguished Professor of Arts and Sciences in Sociology at Cornell University and serves as the Director of the Social Dynamics Laboratory. His academic role involves exploring social science through various courses such as 'Six Pretty Good Books: Explorations in Social Science,' 'The Science of Social Behavior,' and 'Social Dynamics and Computational Methods.' Macy's work focuses on understanding social behavior and social dynamics, utilizing computational methods to analyze complex social phenomena. His contributions include leading research initiatives at the Social Dynamics Laboratory and engaging in teaching that bridges social science and computational techniques, fostering insights into cultural wars, political polarization, and social inequality.

Research topics

• Computer Science
• Psychology
• Artificial Intelligence
• Data Mining
• Natural Language Processing
• Statistics
• World Wide Web
• Mathematics
• Econometrics
• Social psychology

Selected publications

• Prosocial motives underlie scientific censorship by scientists: A perspective and research agenda

  Proceedings of the National Academy of Sciences · 2023 · 96 citations

  • Political Science
  • Sociology
  • Political Science

  Science is among humanity's greatest achievements, yet scientific censorship is rarely studied empirically. We explore the social, psychological, and institutional causes and consequences of scientific censorship (defined as actions aimed at obstructing particular scientific ideas from reaching an audience for reasons other than low scientific quality). Popular narratives suggest that scientific censorship is driven by authoritarian officials with dark motives, such as dogmatism and intolerance. Our analysis suggests that scientific censorship is often driven by scientists, who are primarily motivated by self-protection, benevolence toward peer scholars, and prosocial concerns for the well-being of human social groups. This perspective helps explain both recent findings on scientific censorship and recent changes to scientific institutions, such as the use of harm-based criteria to evaluate research. We discuss unknowns surrounding the consequences of censorship and provide recommendations for improving transparency and accountability in scientific decision-making to enable the exploration of these unknowns. The benefits of censorship may sometimes outweigh costs. However, until costs and benefits are examined empirically, scholars on opposing sides of ongoing debates are left to quarrel based on competing values, assumptions, and intuitions.

  PublisherDOI

• Does Bad News Go Away Faster?

  Proceedings of the International AAAI Conference on Web and Social Media · 2021 · 52 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  We study the relationship between content and temporal dynamics of information on Twitter, focusing on the persistence of information. We compare two extreme temporal patterns in the decay rate of URLs embedded in tweets, defining a prediction task to distinguish between URLs that fade rapidly following their peak of popularity and those that fade more slowly. Our experiments show a strong association between the content and the temporal dynamics of information: given unigram features extracted from corresponding HTML webpages, a linear SVM classifier can predict the temporal pattern of URLs with high accuracy. We further explore the content of URLs in the two temporal classes using various textual analysis techniques (via LIWC and trend detection). We find that the rapidly-fading information contains significantly more words related to negative emotion, actions, and more complicated cognitive processes, whereas the persistent information contains more words related to positive emotion, leisure, and lifestyle.

  DOI

• Going beyond accuracy: estimating homophily in social networks using predictions

  2020 · 2 citations

  Senior authorCorresponding
  • Computer Science
  • Data Mining
  • Computer Science

  In online social networks, it is common to use predictions of node categories to estimate measures of homophily and other relational properties. However, online social network data often lacks basic demographic information about the nodes. Researchers must rely on predicted node attributes to estimate measures of homophily, but little is known about the validity of these measures. We show that estimating homophily in a network can be viewed as a dyadic prediction problem, and that homophily estimates are unbiased when dyad-level residuals sum to zero in the network. Node-level prediction models, such as the use of names to classify ethnicity or gender, do not generally have this property and can introduce large biases into homophily estimates. Bias occurs due to error autocorrelation along dyads. Importantly, node-level classification performance is not a reliable indicator of estimation accuracy for homophily. We compare estimation strategies that make predictions at the node and dyad levels, evaluating performance in different settings. We propose a novel “ego-alter” modeling approach that outperforms standard node and dyad classification strategies. While this paper focuses on homophily, results generalize to other relational measures which aggregate predictions along the dyads in a network. We conclude with suggestions for research designs to study homophily in online networks. Code for this paper is available at https://github.com/georgeberry/autocorr.

  DOI

Recent grants

• Comparative Network Analysis: Mapping Global Social Interactions

  NSF · $435k · 2012–2016

• Dissertation Research: Generalized Reciprocity: Understanding the Social Contagion of Altruistic Behavior

  NSF · $12k · 2013–2015

• Very Large Semi-Structured Datasets for Social Science Research

  NSF · $2.0M · 2006–2010

Frequent coauthors

• Rob Claxton

  BT Research

  4 shared

• Nathan Eagle

  Tallaght University Hospital

  4 shared

• Patrick Park

  3 shared

• Ingmar Weber

  Saarland University

  3 shared

• Juan David Robalino

  2 shared

• Bogdan State

  Social Care Institute for Excellence

  2 shared

• Milena Tsvetkova

  1 shared

• Jon Kleinberg

  Cornell University

  1 shared

Similar researchers at Cornell University

• Edward Bucklerh-182
• Robert Sternbergh-168
• John P. Mooreh-153
• Olivier Elementoh-150
• Andy Clarkh-142