Themes: – Methodologies for working with open-source data – Research workflow for robust and meaningful analyses – Career and curriculum suggestions for graduate students – Roles of NGOs and civil society actors in combating misinformation
PRESENTER: Aric Toler, Training and Research Director at Bellingcat
Aric Toler is the training and research director at Bellingcat, an online publication specialized in open-source intelligence. Aric’s research including Russian intelligence operations, the war in the Donbas, and the 2014 downing of Malaysian Airlines Flight 17 over eastern Ukraine. Aric received his Master’s degree in Slavic Languages & Literatures from the University of Kansas in 2013 and has since been working with Bellingcat.
I have a 10-hour Graduate Research Assistant position available in 2021 spring (for UT students only). The project will examine bias and social stereotypes in the nonprofit sector using computational methods. Publication and authorship are possible depending on contribution. The successful applicant is expected to have the following qualifications:
Use Python as a primary coding language.
Familiar with web crawling and markup languages (e.g., XML and HTML).
Proficient in natural language processing, contextual word embedding (e.g., BERT) in particular.
Needless to introduce the background of the 2019 Hong Kong anti-extradition movement which started in March 2019. It has been a year since its inception. Although people are still talking about the protests, the movement gradually steps out the mass media’s front page. An important question to ask is, has this movement ended?
Social scientists also run heavy computational jobs. In one of my projects, I need to analyze the psychological state of a few billion Telegram messages. ChameleonCloud provides hosts with up to 64 cores (or “threads”, sometimes “workers”, yes these terms are confusing but CS folks to blame). But even with parallel computing on the best server, the job will run for years, and I need this project for tenure.
Abstract: This work has two goals: explore the research strategy of combining incentivized game behavior with large area probability surveys, and use the research strategy to explore how the network structure around a person predicts trust and cooperation beyond the network. Reasoning from research within networks, we hypothesize that network closure has a negative effect on trust and cooperation beyond the network. We find empirical support for the hypothesis in game play and network data on a large area probability sample of Chinese CEOs. More, success is the tonic that animates the hypothesis. Trust and cooperation from CEOs running less successful businesses is independent of their network. In contrast, successful CEOs with closed networks are particularly likely to defect against people beyond their network, and successful CEOs with open networks are particularly likely to cooperate beyond their network. We demonstrate the robustness of our empirical evidence, and discuss future use of incentivized games to obtain behavioral data from respondents in large area probability surveys.
“Ronald Stuart Burt is an American sociologist and the Hobart W. Williams Professor of Sociology and Strategy at the University of Chicago Booth School of Business. He is most notable for his research and writing on social networks and social capital, particularly the concept of structural holes in a social network.” (Wikipedia introduction)
I primarily use Chameleon Cloud (CC) for my research projects. It provides great flexibility because I can run bare-metal servers (e.g., 44 threads/cores, 128G+ RAM) for a seven-day lease which is also renewable if the hosts I’m using are not booked by others. Its supporting team is also amazing.
But everything becomes slow if you are working with a really big dataset. For example, I’m working on a Telegram project and have 1TB+ data. This really gets me a headache. Well, the CC machines are able to handle this but need extra configurations.
In 2019 August, we finished our fieldwork in two rural villages in southeast China. The graph below shows the self-governance organizations weave together through local elites (xiangxian). I wrote a non-academic article introducing our work, which was featured in the Nonprofit Academic Centers Council’s monthly newsletter and IC2’s website. You can read the full article here.
We will solve real-world civic issues by analyzing open government data and building computer programs or models. You can assemble a team with students from or outside of the class. Your team can choose from the following problems: