Overview

Details

The Relationship Between This Sharing and the Computational Social Science Reading Group

Text analysis is a very important research method in social sciences; however, the traditional manual coding method severely limits the sample size that can be researched, making it difficult to apply to large-scale data analysis. In this session, I will introduce the entire process of text analysis based on natural language processing technology and use two articles as examples to illustrate the application of NLP methods in computational social science research.

Content Introduction

This sharing will introduce the basic process of natural language processing and will focus on how to expand traditional manual coding to large-scale automated text analysis. Based on this, we will discuss the combination and application of NLP methods in fields such as communication and psychology with two research examples.

Outline

• Basic Process of Natural Language Processing

• Data-Driven Manual Coding

• Research Case One: Quantifying Language Intimacy

a. How to quantify language intimacy b. How gender, social distance, and anonymity affect language intimacy

• Research Case Two: Changes in Certainty in Scientific Communication

a. How to quantify the certainty of scientific discoveries b. Has the certainty of scientific discoveries changed in scientific communication c. How team size and journal impact factor influence the certainty of scientific discoveries

Instructor Introduction

Pei Jiaxin

PhD candidate in Information Science at the University of Michigan, focusing on computational social science and natural language processing, dedicated to building new NLP models to analyze large-scale human behavior, currently focusing on interpersonal communication and scientific communication, with related results published in conferences such as ACL, EMNLP, WWW.

Course Learning

Learning Address:

https://campus.swarma.org/course/4537

Recruitment for the Second Season of the Computational Social Science Reading Group

The second season of the Computational Social Science Reading Group, led by Professor Luo Jiade from Tsinghua University, was jointly initiated by several PhD students from Carnegie Mellon University, the University of Michigan, Tsinghua University, and the University of Pittsburgh, starting from June 18, 2022, lasting 10-12 weeks. This season’s reading will focus on discussing methods such as Graph, Embedding, NLP, Modeling, Data collection, and their combination with social science issues, and will specifically discuss topics such as predictive vs. explanatory and COVID-19 research. For details on the reading group and participation methods, please see the end of the article. Friends engaged in related research or interested in computational social science are welcome to participate.

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