June 17, 2018 - June 30, 2018 | Duke University

Sponsored by the Russell Sage Foundation & The Alfred P. Sloan Foundation


From the evening of Sunday, June 17 to the morning of Saturday, June 30, 2018, the Russell Sage Foundation and the Alfred P. Sloan Foundation will sponsor the Summer Institute in Computational Social Science, to be held at Duke University. The purpose of the Summer Institute is to bring together graduate students, postdoctoral researchers, and beginning faculty interested in computational social science. The Summer Institute is for both social scientists (broadly conceived) and data scientists (broadly conceived). The co-organizers and principal faculty of the Summer Institute are Christopher Bail and Matthew Salganik. There will also be seven partner locations run by alumni of the 2017 Summer Institute, which will be hosted at the following universities: Hunter College, New York University, Northwestern University, University of Cape Town, University of Colorado, University of Helsinki, and University of Washington.

The instructional program will involve lectures, group problem sets, and participant-led research projects. There will also be outside speakers who conduct computational social science research in academia, industry, and government. Topics covered include text as data, website scraping, digital field experiments, non-probability sampling, mass collaboration, and ethics. There will be ample opportunities for students to discuss their ideas and research with the organizers, other participants, and visiting speakers. Because we are committed to open and reproducible research, all materials created by faculty and students for the Summer Institute will be released open source.

Participation is restricted to Ph.D. students, postdoctoral researchers, and untenured faculty within 7 years of their Ph.D. Most participant costs during the workshop, including housing and most meals, will be covered, and most travel expenses will be reimbursed up to a set cap. About thirty participants will be invited. Participants with less experience with social science research will be expected to complete additional readings in advance of the Institute, and participants with less experience coding will be expected to complete a set of online learning modules on the R programming language. Students doing this preparatory work will be supported by a teaching assistant who will hold online office hours before the Institute.

Application materials were due Monday, February 19, 2018. We are no longer accepting applications.

Faculty

Matthew Salganik

Matthew Salganik is Professor of Sociology at Princeton University, and he is affiliated with several of Princeton’s interdisciplinary research centers: the Office for Population Research, the Center for Information Technology Policy, the Center for Health and Wellbeing, and the Center for Statistics and Machine Learning. His research interests include social networks and computational social science. He is the author of the forthcoming book Bit by Bit: Social Research in the Digital Age.

Chris Bail

Chris Bail is the Douglas and Ellen Lowey Associate Professor of Sociology and Public Policy at Duke University and a member of the Interdisciplinary Program on Data Science, the Duke Network Analysis Center, and the Duke Population Research Institute. His research examines how non-profit organiations and other political actors shape social media discourse using large text-based datasets and apps for social science research. He is the author of Terrified: How Anti-Muslim Fringe Organizations Became Mainstream.

Speakers

Deen Freelon

Deen Freelon is an Associate Professor in the School of Media and Journalism at the University of North Carolina at Chapel Hill, and directs the Computational Communication Research Lab.

Kieran Healy

Kieran Healy is Associate Professor of Sociology at Duke University. He’s affiliated with the Kenan Institute for Ethics, the Markets and Management Studies program, and the Duke Network Analysis Center.

David Lazer

David Lazer is Professor of Political Science and Computer and Information Science, Northeastern University & Harvard University.

Monica Lee

Monica Lee is a Data Scientist at Facebook who works on Civic Engagement and Election Integrity. Her research leverages social network analysis and machine learning to combat election related social media abuse and to develop tools for civic empowerment.

Kristian Lum

Kristian Lum is the Lead Statistician at the Human Rights Data Analysis Group (HRDAG), where she leads the HRDAG project on criminal justice in the United States.

Sendhil Mullainathan

Sendhil Mullainathan is the Robert C. Waggoner Professor of Economics at Harvard University and the co-founder of the Abdul Latif Jameel Poverty Action Lab.

Duncan Watts

Duncan Watts is a Principal Researcher at Microsoft Research and a founding member of the MSR-NYC lab. He is also an AD White Professor at Large at Cornell University.

Teaching Assistants

Image of Taylor Brown

Taylor Brown

Taylor Brown is a doctoral student in the Duke Sociology department, and is associated with the Duke Network Analysis Center. She has broad interests in computational methods and social media studies. Her dissertation explores gender inequality in creative professions. Taylor holds an MA in sociology from UNC-Chapel Hill and an MSc in evidence-based social intervention from the University of Oxford. Prior to beginning her PhD, Taylor fulfilled an appointment at the National Science Foundation in the division of Social and Economic Sciences.

Image of Haohan Chen

Haohan Chen

Haohan Chen is a doctoral student in the Duke Political Science Department. He studies the formation and expression of political preferences in authoritarian regimes in the social media era. His current research uses computational models to simulate how people in authoritarian regimes strategically falsify their political preferences with different parts of their social network and how authoritarian regimes respond. He applies a combination of machine learning and causal inference methods to text data from social media sites of China to test empirical implications of his computational models. Prior to graduate school, Haohan earned a BA from the University of Hong Kong.

Marcus Mann

Marcus Mann is a doctoral student in the Duke Sociology department. He uses computational methods to examine politically partisan news ecologies on social media and maintains a general interest in the cultural differentiation of epistemic authorities and their corresponding audiences, communities, and social movements.

Image of Friedolin Merhout

Friedolin Merhout

Friedolin Merhout is a doctoral student in the Duke Sociology department. He enjoys exploring how computational methods provide a new lens to view longstanding social science debates, and pondering the potential inherent in the wealth of digital trace data. Before starting the doctoral program at Duke, he earned a BA from Freie Universitaet in his hometown Berlin.

Image of Janet Xu

Janet Xu

Janet Xu is a doctoral student in the Princeton Sociology department, where she is also affiliated with the Office of Population Research. Her current research examines perceptions and portrayals of demographic diversity using experimental and computational methods. She holds a B.A. from the University of Chicago. Prior to graduate school, Janet worked at the National Opinion Research Center (NORC) as a survey researcher.

Participants

Emily Bello-Pardo

Emily D. Bello-Pardo is a doctoral student at American University. Her work examines the attitudinal impacts of online dis- and mis- information, online discursive incivility, and public policy shifts, and uses experimental and computational social science approaches to explore these topics in the US and Latin America. In 2017, Bello-Pardo was a Google NewsLab Fellow at Pew Research Center. Before her PhD, Bello-Pardo obtained a MA in Latin American and Caribbean Studies and dual Bachelor degrees in Political Science and International Relations from Florida International University.

Nicolò Cavalli

Nicolò is a DPhil candidate in Sociology at Nuffield College, University of Oxford. He holds a BA in Politics from University of Bologna and a MSc in Economics from Bocconi University, Milan. Before joining Nuffield College, Nicolò worked as journalist, reporting on social issues and political movements from Italy, Greece, Catalunya, California and Peru. His Doctoral Thesis focuses on how intergroup emotional stratification emerged in Europe in times of economic recession.

Lily Fesler

Lily Fesler is a doctoral student in economics of education at the Stanford Graduate School of Education and an Institute of Education Sciences (IES) fellow. Her research interests include student and teacher bias in higher education and barriers to college access. Methodologically, she is very interested in using text analysis to better understand student’s college experiences (and started the Stanford student-led group Computational Text Analysis in the Social Sciences). Before coming to Stanford, Lily worked as an education analyst at Abt Associates and as an analyst at an economic consulting firm in Boston. She received her bachelor’s degree from Wesleyan University.

Image of Natalie Gallagher

Natalie Gallagher

Natalie Gallagher is currently pursuing a PhD in psychology at Northwestern University. Her work lies at the intersection of social and cognitive research, including network cognition, social categories, and a flexible sense of self. She draws on psychological, sociological, and computational methods to pursue her questions, and is interested in how research can inform social change. Natalie received her BA in psychology and theater from Georgetown University.

Ryan J. Gallagher

Ryan J. Gallagher is a PhD student at Northeastern University. At the Network Science Institute, he researches the dynamics of social networks using tools and theory from natural language processing and communications. He currently studies the affective phenomena of networked counterpublics. Ryan holds an MS in mathematics from the University of Vermont, where he worked with the Computational Story Lab at the Vermont Complex Systems Center, and a BA in math from the University of Connecticut.

Douglas Guilbeault

Douglas Guilbeault is a PhD candidate in the Network Dynamics Group at the University of Pennsylvania. His research uses formal models and online experiments to study political communication and cultural evolution. His recent work focuses on the effects of political polarization on collective intelligence. Doug is funded by a PhD scholarship from the Social Sciences and Humanities Research Council of Canada, as well as by a dissertation fellowship from the Institute for Research on Innovation and Science. He has a background in philosophy, linguistics, and cognitive science.

David Hagmann

David is a PhD candidate in Decision Sciences at Carnegie Mellon University and a visiting scholar at the Wharton School at the University of Pennsylvania. His interests include information avoidance, behavioral interventions (nudges), and decisions from experience. In his dissertation, David studies persuasion in the presence of motivated reasoning. While we might think that changing someone’s mind is all about exposing them to facts that support our views and challenge theirs, such an approach may be more likely to engender defensive information avoidance rather than receptive information processing.

Katherine Hoffmann Pham

Katherine Hoffmann Pham is a PhD candidate in Information Systems at the NYU Stern School of Business. Her research focuses on applications of big data to international development and policy problems; her current projects study transportation mode choice in New York City and migration patterns in the Central Mediterranean. She is also interested in how machine learning can be applied to causal inference. Previously, she worked on randomized controlled trials with Innovations for Poverty Action and completed a co-terminal degree in International Relations, Economics, and International Policy Studies at Stanford.

David Holtz

David Holtz is a PhD student in the Information Technology group at MIT Sloan. His research interests span online marketplace design, causal inference, applied machine learning, and network science. His work thus far has focused on ratings and reviews, as well as the viability of reputation systems that don’t depend on user generated feedback. He holds an MA in Physics & Astronomy from Johns Hopkins University and a BA in Physics from Princeton University. Prior to beginning his PhD, David was a data scientist (most recently at Airbnb).

Image of Eaman Jahani

Eaman Jahani

Eaman Jahani is a graduate research assistant pursuing a PhD degree in Social and Engineering Systems with a minor in Statistics at MIT IDSS. Prior to MIT, he was a software engineer at Google for 4 years. His main training is in statistics and computer science, but recently he has been appreciating econometrics and modeling in applied economics. His past research examined the extent of bubbles vs truth-seeking in cryptocurrency markets and socio-economic prediction in social networks. His current research focuses on structural factors such as networks or institutions that regenerate inequality at a micro scale. Eaman spends too much time reading political commentaries.

Carly Knight

Carly Knight is completing her PhD in Sociology at Harvard University. Her work applies quantitative and computational methods to questions of historical and cultural change. Her primary research interest concerns the evolution of attitudes towards the market and the development of organizational market actors. She is also broadly interested in political sociology, law and regulation, markets and moral classification, and computational analysis. In the Fall, she will begin as an Assistant Professor at New York University.

Elena Labzina

Elena is a graduating Ph.D. student in PolSci at WashU in St Louis. Soon, she is joining the Lab of Law&Economics at ETH Zurich as a postdoc in PolSci, Econ, and Data Analysis. Also, she holds MAs in PolSci, Econ, and Stats. Her BSc is in Applied Math and CompSci from Lomonosov State in Moscow, Russia. Elena’s research concerns the interdisciplinary studies that apply advanced data methods to questions related to information security, media freedom, development, and environmental issues.

Image of Tina Law

Tina Law

Tina Law is a Ph.D. student in sociology at Yale. Her research explores how big data and computational social science can be used to advance the study of racial inequality in U.S. cities and neighborhoods. She is particularly interested in applying and integrating techniques from analyses of social networks, text corpora, and emotions in order to generate new insights into longstanding issues of urban racial inequality. Her ongoing work is supported by the National Science Foundation’s Graduate Research Fellowship Program. She will be continuing her studies at Northwestern in fall 2018.

Yan Leng

Yan is currently pursuing a Ph.D. at Human Dynamics group at MIT. She received dual masters in Computer Science and Transportation Engineering from MIT in 2016. Yan is interested in using a broad range of computational techniques to understand the network effect of social influence. In particular, she works on the inference, identification, and modeling of social influence and social learning with large-scale behavioral data in a networked environment. Besides, she also works on the combining network structure and personal attributes in maximizing cascading payoff.

Jeff Lockhart

Jeff Lockhart is a PhD candidate in sociology at the University of Michigan. Before coming to Michigan, he completed a masters in computer science at Fordham University and a masters in gender studies at the University of Cambridge. His research seeks to integrate computational tools with critical insight from feminist and queer theory.

Julien Migozzi

Julien Migozzi is a PhD Candidate in Urban & Economic Geography at the University of Grenoble Alpes and a Lecturer & Research Assistant at the Ecole Normale Supérieure de Paris. His research investigates how the real estate market reshapes patterns of social stratification and neighborhood change in emerging cities of the Global South, with a specific focus on Cape Town, South Africa. He is particularly interested in the intersection of housing market, financialization and inequalities. His methodology combines in-depth, qualitative fieldwork with spatial analysis, multivariate statistics & mapping. He received his B.A and his M.A in Geography from the Ecole Normale Supérieure de Lyon.

Sanaz Mobasseri

Sanaz Mobasseri is an Assistant Professor of Organizational Behavior at Boston University’s Questrom School of Business. She received her PhD from the Management of Organizations Department at UC Berkeley’s Haas School of Business. Her research examines the role of emotion, cognition, and culture in shaping social networks and labor market outcomes. Much of her work is situated in organizational settings, where she examine the microfoundations of workplace inequality. Although grounded in sociology and organizational theory, her work integrates theoretical insights from social psychology and sociolinguistics. Her research methods are similarly diverse, ranging from experimental studies in the lab to audit studies in the field to computational approaches applied to large archival data sets.

Hussein Mohsen

Hussein is a PhD student in Computational Biology & Bioinformatics at Yale University supported by the Nicholas Jabr and Gruber Science Fellowships. His research interests are at the confluence of deep learning, cancer genomics, and computational social science. He received a BS in Computer Science from the Lebanese American University (LAU) and an MS in Bioinformatics at Indiana University.

Zanele Munyikwa

Zanele Munyikwa is a PhD student in Information Technologies at the MIT Sloan School of Management. She studies the role of networks, platforms, and people in the rapidly changing digital economy. Zanele’s current research focuses on the future of work and the economics of social networks. She holds a Bachelors degree in Computer Science from Duke University, and spent two years as a Research Fellow at Stanford Graduate School of Business.

Image of Trang (Mae) Nguyen

Trang (Mae) Nguyen

Trang (Mae) Nguyen (Nguyễn Thu Trang) is the John N. Hazard Fellow in Comparative Law at New York University School of Law, U.S.-Asia Law Institute, and visiting scholar at University of California Berkeley School of Law. Her research uses mixed methods analysis to study authoritarian legality. Mae earned a J.D. degree from NYU School of Law, where she was a Mitchell Jacobson Law & Leadership Fellow and executive editor of the New York University Law Review.

Stan Oklobdzija

Stan Oklobdzija is a PhD Candidate in the Department of Political Science at UC San Diego. His dissertation project focuses on how changes to US election law allowed networks of interest groups to take over roles previously held by political parties. His research interests revolve around campaign finance, election law and state politics. Prior to graduate school, he worked as a reporter at the Sacramento Bee.

Anne Helby Petersen

Anne Petersen is a research assistant at the Section of Biostatistics, Copenhagen University, Denmark. She has a MSc in statistics, a BSc in mathematics and a keen interest in sociology. She is the primary developer of two R-packages on CRAN, dataMaid and PCADSC. Her research interests are focused on the methodological challenges related to modeling observational data and in particular how this type of information can be used to understand the interplay between social inequality and health.

Image of Iacopo Pozzana

Iacopo Pozzana

Iacopo holds a masters in physics from the University of Pisa and is currently pursuing a PhD in computer science at Birkbeck, University of London. In his research, he uses tools from network science, machine learning and natural language processing to study human behaviour on social media, currently focusing on platforms granting an high degree of anonymity to their users. Previously, he has worked on social bot detection and on temporal network modelling.

Francesco Rampazzo

Francesco is pursuing a PhD in Social Statistics and Demography at the University of Southampton, while being a Doctoral Fellow at the Max Planck Institute of Demographic Research. He holds a Master’s degree in Demography from Stockholm University, and a Bachelor’s degree in Statistics from the University of Padova. Having moved around Europe for his university education, he understands how important it is to complement traditional data sources on migrants, with digital data sources for capturing the actual movements of individuals. His PhD focuses on the use of digital data for describing demographic events, such as European migration, male fertility, and patterns of transition to adulthood.

Leah Rosenzweig

Leah just completed her PhD in political science at MIT, where she was a member of MIT GOV/LAB and the Political Methodology Lab. Her research focuses on citizens’ political behavior in developing countries. Her current book project investigates the puzzle of why citizens vote in elections with foregone conclusions. Using survey and experimental methods, she analyzes the role that social norms play in motivating turnout among citizens in semi-authoritarian states. Leah will be joining the Institute for Advanced Study in Toulouse, France as a postdoctoral research fellow in the fall.

Martijn Schoonvelde

Martijn Schoonvelde is a postdoctoral fellow in Political Science at the Vrije Universiteit in Amsterdam where he uses automated text analysis to study on leader communication in the European Union during times of crisis. Before this, he was a Max Weber Fellow at the European University Institute in Florence, and a research fellow at the University of Exeter in the UK. He received his PhD from Stony Brook University. His interests include comparative political behavior, EU politics and research methods. He tweets under @hjms.

Carsten Schwemmer

Carsten Schwemmer is currently pursuing a PhD in Sociology at Bamberg University, Germany. His research focuses on computational methods for the study of ethnic minorities and social media communication. Carsten is particularly interested in natural language processing, data mining and software development. He also teaches computational social science at Bamberg University and Humboldt University of Berlin.

Image of Ieke de Vries

Ieke de Vries

Ieke de Vries is pursuing a PhD in Criminology and Justice Policy at Northeastern University. Her current research aims to address the legitimate contours of crime by building and analyzing novel, digitized data sets utilizing computational methods. She has collaborated with federal, state and local agencies in the U.S. and gained research and policy experience while researching crime in several other countries including the Netherlands where she worked for the Dutch Rapporteur on Trafficking in Human Beings.

Pre-arrival

As we discussed in our call for applications, we have arranged two types of training prior to the event this summer. Some students have more sophisticated coding skills but little exposure to social science; other students have significant exposure to social science but lack strong coding skills.

Coding

The majority of the coding work presented at the 2018 SICSS will employ R. However, you are welcome to employ a language of your choice, such as Python, Julia, or other languages that are commonly used by computational social scientists. If you would like to work in R, we recommend that you complete the following courses within DataCamp, a website with courses on many topics related to data science. Obviously, you only need to complete the classes with material that you would like to learn.

We thank DataCamp for making these materials avaialble to admitted participants though their DataCamp for the Classroom program.

Reading List

Our institute will bring together people from many fields, and therefore we think that asking you to do some reading before you arrive will help us use our time together more effectively. First, we ask you to read Matt’s book, Bit by Bit: Social Research in the Digital Age, which is a broad introduction to computational social science. Parts of this book will be review for most of you, but if we all read this book ahead of time, then we can use our time together for more advanced topics.

Also, for students with little or no exposure to sociology, economics, or political science, we have assembled a collection of exemplary papers in the core areas addressed by the Russell Sage Foundation. Neither your work nor the work we develop together at the institute need map neatly onto these categories, but if those with less exposure to social science read these, we will increase the chances of interdisciplinary cross-pollination, which we view as critical to the future of computational social science.

Future of Work

Behavioral Economics

Race, Ethnicity, and Immigration

Social Inequality

Schedule and materials

Sunday June 17, 2018

  • Opening Dinner (Not open to public/No livestream)

Monday June 18, 2018 - Introduction and Ethics

  • 9:00 - 9:15 Logistics (Not open to public/No livestream)

  • 9:15 - 9:30 Introductions (Not open to public/No livestream)

  • 9:30 - 10:00 Introduction to computational social science

  • 10:00 - 10:30 Why SICSS?

  • 10:30 - 10:45 Coffee Break

  • 10:45 - 11:30 Ethics: Principles-based approach

  • 11:30 - 12:15 Four areas of difficulty: informed consent, informational risk, privacy, and making decisions in the face of uncertainty

  • 12:15 - 12:30 Introduction to the group exercise

  • 12:30 - 1:30 Lunch (Not open to public/No livestream)

  • 1:30 - 3:45 Group exercise (Not open to public/No livestream)

  • 3:45 - 4:00 Break

  • 4:00 - 5:30 Guest speaker: Duncan Watts

  • 6:00 - 7:30 Dinner & discussion (Not open to public/No livestream)

Tuesday June 19, 2018 - Collecting Digital Trace Data

  • 9:00 - 9:15 Logistics (Not open to public/No livestream)

  • 9:15 - 9:30 What is digital trace data?

  • 9:30 - 9:45 Strengths and weakness of digital trace data

  • 9:45 - 10:15 Screen-Scraping

  • 10:15 - 10:30 Coffee Break

  • 10:30 - 11:00 Application Programming Interfaces

  • 11:00 - 12:30 Building Apps and Bots for Social Science Research

    • Video, slides, [annotated code](https://compsocialscience.github.io/summer-institute/2018/materials/day2-digital-trace-data/building-apps-bots/rmarkdown/Building%20Apps%20and%20Bots%20for%20Social%20Science%20Research.nb.html
  • 12:30 - 1:30 Lunch (Not open to public/No livestream)

  • 1:30 - 3:45 Group Exercise (Not open to public/No livestream)

  • 3:45 - 4:00 Break

  • 4:00 - 5:30 Guest speaker: Jim Wilson (Russell Sage Foundation)

  • 6:00 - 7:30 Dinner & Discussion (Not open to public/No livestream)

Wednesday June 20, 2018 - Automated Text Analysis

  • 9:00 - 9:15 Logistics (Not open to public/No livestream)

  • 9:15 - 9:30 History of quantitative text analysis

  • 9:30 - 9:45 Basic Text Analysis/GREP

  • 9:45 - 10:00 Dictionary-Based Text Analysis

  • 10:00 - 10:15 Coffee Break

  • 10:15 - 11:15 Topic models/Structural Topic Models

  • 11:15 - 11:20 Break

  • 11:20 - 12:30 Text Networks

  • 12:30 - 1:30 Lunch (Not open to public/No livestream)

  • 1:30 - 4:00 Group Exercise (Not open to public/No livestream)

  • No Guest Speaker Tonight

  • 6:00 - 7:30 Dinner & Discussion (Not open to public/No livestream)

Thursday June 21, 2018 - Surveys in the Digital Age

  • 9:00 - 9:15 Logistics (Not open to public/No livestream)

  • 9:15 - 9:45 Survey research in the digital age

  • 9:45 - 10:15 Probability and non-probability sampling

  • 10:15 - 10:30 Coffee break

  • 10:30 - 11:00 Computer-administered interviews and wiki surveys

  • 11:00 - 11:30 Combining surveys and big data

  • 11:30 - 12:00 Group exercise introduction

  • 12:00 - 12:30 Begin group exercise

  • 12:30 - 1:30 Lunch

  • 1:30 - 3:15 Continue group exercise (Not open to public/No livestream)

  • 3:15 - 3:45 Discuss activity and open-source data

    • Slides (Not open to public/No livestream)
  • 3:45 - 4:00 Break

  • 4:00 - 5:30 Guest speaker: David Lazer

  • 6:00 - 7:30 Dinner & Discussion (Not open to public/No livestream)

Friday June 22, 2018 - Mass Collaboration

  • 9:00 - 9:15 Logistics (Not open to public/No livestream)

  • 9:15 - 9:30 Mass collaboration

  • 9:30 - 9:45 Human computation

  • 9:45 - 10:00 Open call

  • 10:00 - 10:15 Distributed data collection

  • 10:15 - 10:30 Coffee break

  • 10:30 - 11:30 Introduction to the Fragile Families Challenge

  • 11:30 - 12:30 Working on the Fragile Families Challenge (Not open to public/No livestream)

  • 12:30 - 1:30 Lunch

  • 1:30 - 3:30 Fragile Families Challenge

  • 3:30 - 3:45 Discussion of the Fragile Families Challenge (Not open to public/No livestream)

  • 3:45 - 4:00 Break

  • 4:00 - 5:30 Guest speaker: Sendhil Mullainathan

  • 6:00 - 7:30 Dinner & Discussion (Not open to public/No livestream)

Saturday June 23, 2018 - Experiments

  • 9:00 - 9:15 Logistics (Not open to public/No livestream)

  • 9:15 - 9:45 What, why, and which experiments?

  • 9:45 - 10:15 Moving beyond simple experiments

  • 10:15 - 10:30 Coffee break

  • 10:30 - 11:15 Four strategies for experiments

  • 11:15 - 11:45 Zero variable cost data and musiclab

  • 11:45 - 12:15 3 Rs

  • 12:15 - 12:30 Logistics (Not open to public/No livestream)

  • 12:30 - 1:30 Lunch (Not open to public/No livestream)

  • Afternoon off

Sunday June 24, 2018 - Day off

Monday June 25, 2018 - Work on projects (Not open to public/No livestream)

  • 11:00 - 12:00 Gary King (not in person)

  • 12:30 - 12:45 Flash Talk: Cleaning up the data cleaning process: Reproducible data cleaning in R (Anne Helby Petersen)

  • 12:45 - 1:00 Flash Talk: Entropy and information-theoretic methods for text analysis (Ryan J. Gallagher)

  • 4:00 - 5:30 Guest speaker: Deen Freelon

Tuesday June 26, 2018 - Work on projects (Not open to public/No livestream)

  • 9:00 - 9:15 Flash Talk: Running R Studio in your web browser/in the cloud with AWS (Chris Bail)

  • 9:15 - 9:30 Flash Talk: Making .Rpres and .rmarkdown files (Chris Bail)

  • 12:30 - 12:45 Flash Talk: Text interpretation & the Constitution (Trang (Mae) Nguyen)

  • 12:45 - 1:00 Flash Talk: Open Review Toolkit (Matthew Salganik)

  • 1:00 - 1:15 Flash Talk: Utility from beliefs and information - and an experiment on persuasion (David Hagmann)

  • 1:15 - 1:30 Flash Talk: Parallelism Basics for Data Analysis (plus: How to Get 5,000x Speedup without Really Trying) (Jeff Lockhart)

  • 4:00 - 5:30 Guest speaker: Kristian Lum (not in person)

Wednesday June 27, 2018 - Work on projects (Not open to public/No livestream)

  • 9:00 - 9:15 Flash Talk: Facebook’s Advertising Platform data for Demographic Research (Francesco Rampazzo)

  • 9:15 - 9:30 Flash Talk: Machine translation and bag of words models (Martijn Schoonvelde)

  • 12:30 - 2:00 Guest speaker: Monica Lee

Thursday June 28, 2018 - Work on projects (Not open to public/No livestream)

  • 12:30 - 12:45 A new dataset on nonprofits from the IRS (Stan Oklobdzija)

  • 12:45 - 1:00 Flash Talk: Deep Learning: Primer and (Cool) Applications (Hussein Mohsen)

  • 1:00 - 1:15 Flash Talk: Urban big data: opportunities and challenges (Tina Law)

  • 1:15 - 1:30 Flash Talk: Using Github/Git to manage code and collaborate with others (David Holtz)

  • 1:30 - 1:45 Flash Talk: Network effects on Inequality (Eaman Jahani)

  • 1:45 - 2:00 Guidelines for group project prensentations

  • 5:00 - 6:00 Guest speaker: Kieran Healy

Friday June 29, 2018 - Present final projects

  • 2:30 - 2:50 Fun Clustering of SICSS participants (Tina Law, Jeff Lockhart)

  • 2:50 - 3:20 Facebook for demographics and surveys in developing countries (Anne Helby Petersen, Francesco Rampazzo, Leah Rosenzweig, Katherine Hoffmann Pham, Tina Law, Julien Migozzi)

  • 3:20 - 3:50 Cracking the Coding Interview (Dave Holtz, Janet Xu, Sanaz Mobasseri, Zanele Munyikwa, and Lily Fesler)

  • 3:50 - 4:00 Coffee Break

  • 4:00 - 4:20 Polarization and Exposure to Outgroup (Douglas Guilbeault, Yan Leng, David Hagmann, Ryan Gallagher, Nicolò Cavalli, Natalie Gallagher, Elena Labzina, Eaman Jahani)

  • 4:20 - 4:40 SketchNets. Combining Text, Network, and Spatial Analysis to evaluate perceptions of neighborhoods (Hussein Mohsen, Ieke de Vries, Julien Migozzi, Tina Law, Mae Trang, Marcus Mann, Friedolin Merhout)

  • 4:40 - 5:10 Political Twitter Images (Jeff Lockhart, Stan Oklobdzija, Martijn Schoonvelde, Carly Knight, Carsten Schwemmer, Emily Bello-Pardo, Iacopo Pozzana)

  • 5:30 Closing dinner (Not open to public/No livestream)

Saturday June 30, 2018

  • Participants depart