2nd Annual Conference on Politics and Computational Social Science (PaCSS) 

DATE: Wednesday, August 28
LOCATION: Georgetown University
Georgetown University Hotel & Conference Center, Washington, D.C.

 

CONFERENCE DESCRIPTION

The data and methodologies available to social scientists have exploded with the emergence of vast archives of passive data collection, large scale online experimentation, and innovative uses of simulation.  These data are of a larger magnitude and methods are of a greater computational complexity than approaches that have dominated political science for the last 50 years.

This offers the potential for rich insights into society at scale, while simultaneously introducing new ethical and infrastructural challenges. In parallel, the information and communication technologies that have driven these changes are also driving changes in politics, around the world, that require study.

In order to understand the political world, it is increasingly important to gain access to the political communication and behavior occurring online. This preconference gathering will offer a forum for political science research in this emerging space. Example relevant topics/approaches include:  analysis of social media; text analysis; use of finely granular geographic data; large scale online experimentation.

Scholars from all subfields of political science are welcome, as are researchers in other relevant disciplines, such as computer science, sociology, and physics. The meeting will take place 9am-5pm on Wednesday, August 28th at Georgetown University.

Everyone who wishes to attend should apply. We welcome applications to present, discuss or simply participate. We recognize that strength comes through diversity, and actively seek, welcome, and encourage people with diverse backgrounds, experiences, and identities to apply and attend.

This will be a one day conference with a keynote by Sandra González-Bailón of the University of Pennsylvania. A wide range of subjects for presentations are welcome, including purely methodologically oriented presentations or applications thereof; synthesis/overviews of research; discussions of ethics and/or data infrastructures; and so on. Papers are not required.

Applications to present should be submitted by June 10 at our proposal submission page

Register for the conference at our registration page.  Registration is $50 before August 1, 2019 and $100 after that.  The conference includes breakfast, lunch, coffee and snacks.

Pew Research Center is pleased to host two parallel training workshops (see tab above, if link does not work in your browser) at their office in downtown D.C. These workshops will focus on two key areas: 1) natural language (NLP) processing techniques and their application to social science questions; and 2) deep learning techniques with applications to image analysis for social scientists. Participants will be given hands-on experience building and training models in these subject areas and will also be able to meet members of Pew Research Center’s Data Labs team. Workshops will run from 2pm to 5pm Tuesday August 27. Light snacks and coffee will be provided. Cost per participant is $25.

PACSS 2019 is sponsored by the McCourt School of Public Policy Data Science for Public Policy Program, the Office of Provost Robert Groves, Georgetown University and Sage Publishing and the Pew Research Center.


Previous Conference: 2018 PaCSS at Northeastern 

Georgetown Programs: Massive Data Institute  |  Master of Science in Data Science for Public Policy

2nd Annual Conference on Politics and Computational Social Science (PaCSS)
Wednesday, August 28, 2019
Georgetown University
Georgetown University Hotel and Conference Center, Washington, DC


Objective

The data and methodologies available to social scientists have exploded with the emergence of vast archives of passive data collection, large scale online experimentation, and innovative uses of simulation. These data are of a larger magnitude and methods are of a greater computational complexity than approaches that have dominated political science for the last 50 years. This offers the potential for rich insights into society at scale, while simultaneously introducing new ethical and infrastructural challenges. In parallel, the information and communication technologies that have driven these changes are also driving changes in politics, around the world, that require study. In order to understand the political world, it is increasingly important to gain access to the political communication and behavior occurring online.

 

Conference Agenda

Wednesday, August 28, 2019
Location: Georgetown Hotel and Conference Center

 
Agenda will be posted soon.

2nd Annual Preconference on Politics and Computational Social Science (PaCSS) at the McCourt School of Public Policy, in conjunction with the Data Science for Public Policy Program<.

Wednesday, August 28, 2019, Georgetown University, Washington DC

Workshops at Pew

Pew Research Center is pleased to host two parallel training workshops on Tuesday August 27 at their office in downtown D.C. These workshops will focus on two key areas: 1) natural language (NLP) processing techniques and their application to social science questions; and 2) deep learning techniques with applications to image analysis for social scientists. Participants will be given hands-on experience building and training models in these subject areas and will also be able to meet members of Pew Research Center’s Data Labs team. Workshops will run from 2pm to 5pm Tuesday August 27. Light snacks and coffee will be provided. Cost per participant is $25.

Natural Language Processing (NLP) 

This workshop will offer a soup-to-nuts overview of text-as-data methods for political, social, and policy applications using R, split into two sections. The first section will provide both a practical and theoretical introduction to text-as-data approaches including: how to acquire text data, how to clean and organize text data, and challenges and considerations for working with text sources in combination with other types of data. In particular, the first section will introduce dictionary-based techniques (and discuss their pitfalls and alternatives), as well as visualization strategies for summarizing corpora. The second section will focus on more advanced approaches to evaluating text data, including clustering, topic modeling, and word embeddings. 

Sarah BouchatThis workshop will be led by Sarah Bouchat. Bouchat is an Assistant Professor at Northwestern University, and is core faculty in the Political Science Department as well as the Institute for Complex Systems (NICO). At Northwestern, Bouchat teaches courses in machine learning, text-as-data, Bayesian statistics, and research design for social science. They completed their PhD in the Department of Political Science at the University of Wisconsin–Madison in 2017. With research interests in political methodology, comparative political economy, and authoritarian politics with a regional focus on Southeast Asia, Bouchat's research has focused on elicited priors, as well as machine learning and Bayesian statistical applications for the study of low information, authoritarian regimes like Myanmar.

Image Processing

As the volume and accessibility of media content has increased in recent years, computational methods for content analysis at scale have gained popularity. Until recently, these studies limited their use of computational methods to text analysis. As a result, current innovations and developments in the field of deep learning for computer vision, or automated image content analysis, remain unfamiliar and inaccessible for most social science researchers. This workshop aims to bridge this gap, by providing a brief theoretical background along with hands-on experience with deep learning for image analysis. The focus of the workshop is on feature extraction and image classification. It will be of particular interest to scholars studying social media or any domain where large numbers of images (e.g. tens of thousands or more) need to be labeled. It also serves as a useful introduction for scholars interested in studying video data. The first part of the workshop introduces deep learning and Convolutional Neural Networks (CNNs). The second part is a hands-on tutorial using Python. It will cover how to 1) use pre-trained CNNs to extract features of interest from an image (e.g. identifying particular objects), and 2) how to train CNNs to classify images into user-determined categories using labeled examples. Experience with Python will be helpful but is not required.

Nora Webb Williams

This workshop will be led by Nora Webb Williams.  Williams is a PhD Candidate in the University of Washington Department of Political Science studying comparative politics, methods, and political economy. Her dissertation research addresses economic resilience and the long-term impacts of colonialism on social trust, with a regional focus on the former Soviet Union. She also writes about the impact of social media and images on protest mobilization, examining diverse cases such as the 2010 revolution in Kyrgyzstan and the Black Lives Matter movement in the United States. Her primary methodological interest is in images as data for social science research, with related interests in machine learning, text as data, and causal inference. Notable experiences outside of the university setting include serving as a Peace Corps volunteer in Kazakhstan and Liberia. Her work has been published or is forthcoming in Political Research Quarterly, Central Asian Survey and Nationalities Papers.

Organizing Committee Members:

Information to be posted soon.

Hotel Information

Conference participants are welcome to stay at any hotels in the area, however there will be a block reservation at the Georgetown University Hotel and Conference Center. The Hotel is located on the Georgetown University campus.

Getting to Campus

If you are driving to campus, you should plan to park at the Southwest Garage located at the bottom of Kennedy Hall. You should use 3611 Canal Road NW as the address when mapping directions in GPS. It is important to note that this garage is cash only. You can find additional directions about driving to campus here and additional information about parking here. We will share additional information about transportation reimbursements at the conference. 

If you are taking a taxi or ride share vehicle, it is easiest to get dropped off at the Main Gates of the University, located at the intersection of 37th and O Streets NW. The University does not allow taxis, Ubers, Lyfts, etc. on campus, so you should not use the parking garage address or any other campus location as a drop off spot. 

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