Associate Professor Jana Diesner and her students have organized two tutorials for The Web Conference 2021. The conference, which will be held virtually from April 12-23, will address the evolution and current state of the Web through the lens of computer science, computational social science, economics, public policy, and Web-based applications.
The first tutorial, "Information Extraction from Social Media: Tasks, Data, and Open-Source," was organized by doctoral candidate Rezvaneh (Shadi) Rezapour, Shubhanshu Mishra (PhD '19), and Diesner. In this hands-on tutorial, participants will learn about digital social trace data abstraction, which allows researchers to model social media data with rich information associated with social media text, such as authors, topics, and time stamps. Participants will be introduced to several Python-based, open-source tools for performing information extraction on social media data and become familiar with a catalogue of publicly available social media corpora for extraction tasks.
The second tutorial, "Hands-on Tutorial on Analyzing Social Network Data in Jupyter Python: The Essentials, Signed Networks, and Network Optimization," was organized by Rezapour, Diesner, doctoral candidate Ly Dinh, and Samin Aref (Max Planck Institute for Demographic Research). According to the organizers, while several open-source tools for social network analysis are available, there is a need for a pipeline that guides scholars through a multilevel analysis of networks. This tutorial will educate participants on how to use network libraries in Jupyter for analyzing the structure of social networks.
Diesner leads the Social Computing Lab at the iSchool. Her research in human-centered data science and responsible computing combines the benefits of machine learning, AI, network analysis and natural language processing with the consideration of social science theories, social contexts, and ethical concerns.