Social Science Projects

Computational Analysis of the Social Sciences

Heather Haveman, Sociology and Haas School of Business 

Fall 2018

Studying trends in the use of theories of organizations in the social sciences (e.g., sociology, economics) and professional fields (e.g., business, public health) using data from~400,000 articles published 1970-2015 from JSTOR, one of the largest online respositories of academic articles. The data consist of metadata on each article plus 1-grams, 2-grams, & 3-grams from JSTOR's data for research arm. Students will help clean the data (e.g., by eliminating front matter, book reviews, and errata), and analyze these data to trace engagement with specific theories over time using coded dictionaries (of concepts) based readings of foundational articles. Students to trace engagement using dictionary methods, helping me refine the dictionaries by using word embeddings (e.g., word2vec) and produce data visualizations.   

Motivations to Attend Coding Bootcamps

Krista Schnell, Sociology

Fall 2018

In the past five years, there has been a surge of coding bootcamps. Their purpose is to prepare graduates for jobs as programmers in the tech industry. Surprisingly, the percentage of women graduating from coding bootcamps is much higher than would be expected, compared to those graduating with bachelor’s degrees in computer science or working in the tech industry. Why do women choose to enter computing now, often after having received a degree in another non-computing field? This research will use online blogs to investigate the motivating factors and reasons behind why people choose to attend coding bootcamps.

Liberating Textual Data: Opening up Public Archives for Open Research

Nick Adams, BIDS

Spring 2018

Students will learn how to gather, parse, and publicly share digital archives that are currently inaccessible for research purposes. Using the Government Publishing Office’s Congressional Hearing Transcripts as an example, the project leads will guide participants through their own data liberation project, in which they will: scrape the web for document files while retaining document metadata; programmatically find and extract meaningful data objects within the documents; link those objects to external databases; prepare all this compiled textual data for computational analysis in R and Python; and host their newly formed database so that the public and other researchers can launch their own studies of the data. Participants will work together on group assignments built around a textual source of their own choosing, each culminating in a new, final database, a research paper, and its presentation. This project is sponsored by Berkeley's Social Science Matrix.

PublicEditor: The Citizen Science Solution to Media Misinformation

Nick Adams, BIDS

Spring 2018

Students on this project will help develop and test a collaborative web app guiding thousands of internet volunteers to read through the most shared news articles and find evidence of misinformation in the content. Working with a national coalition of social science researchers and journalists, a Nobel Laureate, cognitive scientists, and software designers/developers students will see first hand how the social good technologies of the future get built.

Text Analysis of Townhall Meetings

Alexander Sahn, Department of Political Science

Spring 2018

The town-hall meeting where citizens and politicians interact face-to-face is often viewed as the idyllic form of participatory democracy. While rare in national politics, this form of participation is widespread in cities across the country. Yet, the content and consequences of these interactions remain unexamined. This project will investigate the inequalities in representation that arise from politicians using meeting participation to gather information about their constituents’ views. Policymakers and practitioners often point to public meetings as the primary obstacle to housing development in California cities, since a vocal minority can sway elected officials. We will examine records of city council and zoning board meetings in California cities to determine who participates, why they do so, and whether their actions affect political outcomes such as the approval of housing developments.  

Effects of State and Local minimum wage Policies on Health

Sylvia Allegretto, Center on Wage and Employment Dynamics (CWED)

Spring 2018

CWED seeks to understand the effects of state and local minimum wage policies on health and other outcomes. Research would involve processing large micro datasets (review codebooks, import raw data, clean and reshape for analysis), compute, tabulate, and graph descriptive statistics, run simple regression models. 

Quantifying the Role of Partisanship in Rooftop Solar PV Adoption in the U.S.

John Dees, Energy Resources Group

Support or opposition for renewable energy policies often take on a partisan character in U.S. politics. However, solar PV and supporting state and federal policies benefit consumers across the political spectrum. In turn, the benefits of rooftop solar PV policies create interest groups apart from ideological leanings. This has important implications for the politics of solar policy, going forward. Hence, this project seeks to quantify role of political voting behavior on adoption of solar PV.

We are looking for a student to compile, clean, and merge geospatial (GIS) data for each state’s voting data, Solar PV installation data, U.S. Census Bureau income

Effects of State and Local minimum wage Policies on Health

Sylvia Allegretto, Center on Wage and Employment Dynamics (CWED)

Spring 2018

CWED seeks to understand the effects of state and local minimum wage policies on health and other outcomes. Research would involve processing large micro datasets (review codebooks, import raw data, clean and reshape for analysis), compute, tabulate, and graph descriptive statistics, run simple regression models.

DecidingForce: Discovering Patterns of Peace and Violence between Police and Protesters

Nick Adams, BIDS

Summer 2016

Students on this project will use a collaborative web app to process the information in over 8,000 news articles describing all the interactions between police and protesters during the Occupy movement. Data processed by students will be used to find patterns of peace and violence, which can be used to scaffold broad public conversations, and shift the behavior of police and protest strategists. Students work will be used to create artificial intelligence able to understand the dynamics between cities and movements and recommend policies more likely to result in peaceful and effect political expression.