计算社会学Computational Social Science/2016/Princeton

Computational Social Science: Social Research in the Digital Age

Sociology 596

Princeton University

Fall 2016

链接： http://www.princeton.edu/~mjs3/soc596_f2016/

普林斯顿大学社会学教授Matthew J. Salganik2016年的计算社会科学的课程，其中的内容十分经典且在当下仍旧适用。同时推荐他的书籍《Bit by Bit》，中译本为《计算社会学》。英译本在大纲中基本都有开放阅读的链接。

Bit by Bit

9.4

Matthew J. Salganik / 2017 / Princeton University Press

总览：技术的发展让我们从模拟时代走向数字时代，这意味着我们可以通过新方式去收集和分析社会中的数据。这份课程介绍了如何通过这些新方式开展社会研究。不同于以往众多关于计算社会科学的课程，这份课程大纲更加强调“社会科学”而不是“计算”。课程将会聚焦于研究设计中的传统概念，如何引领我们理解新的数据资源，以及这些新的数据资源又如何更新我们对于研究设计的思考。课程对想从事数据科学研究的社会科学学者、以及想从事社会科学研究的数据科学学者都会有所助益。

（以下仅为不同节次的主题预览，具体材料请点击大纲链接查看）

Introduction and Ethics (September 20, 2016)

In this first class we will cover a broad overview of computational social science, focusing on blending ideas from social science and data science. A theme that runs throughout the course is ethics so we will cover it in the first week.

Big data (September 27, 2016)

Human behavior in the digital age often leaves behind traces, and these traces are being aggregated by companies and governments on a massive scale. This week we will discuss the strengths and weaknesses of using these big data sources for social research. Then, I'll describe three approaches that can help you learn from these big data sources: counting things, forecasting, and approximating experiments.

Surveys (October 4, 2016)

This week I'll begin by explaining that big data sources will not replace surveys. In fact, the abundance of big data sources increases---not decreases---the value of surveys. Given that motivation, I’ll summarize the total survey error framework that was developed during the first two eras of survey research. This framework enables us to understand new approaches to representation (e.g., non-probability samples) and new approaches to measurement (e.g., new ways of asking questions to respondents). Finally, I’ll describe two research templates for linking survey data to big data sources.

Running experiments (October 11, 2016)

Randomized controlled experiments have proven to be a powerful way to learn about the social world, and this week we will see how you can use them in your research. We will describe the difference between lab experiments and field experiments and the differences between analog experiments and digital experiments. Further, I’ll argue that digital field experiments can offer the best features of analog lab experiments (tight control) and analog field experiments (realism), all at a scale that was not possible previously. Next, I’ll describe three concepts---validity, heterogeneity of treatment effects, and mechanisms---that are critical for designing rich experiments. With that background, I’ll describe the trade-offs involved in the two main strategies for conducting digital experiments: doing it yourself or partnering with the powerful. Finally, I’ll conclude with some design advice about how you can take advantage of the real power of digital experiments and describe some of responsibility that comes with that power.

Mass collaborations (October 18, 2016)

Wikipedia is amazing. A mass collaboration of volunteers created a fantastic encyclopedia that is available to everyone. The key to Wikipedia’s success was not new knowledge; rather, it was a new form of collaboration. The digital age, fortunately, enables many new forms of collaboration. Thus, we should now ask: what massive scientific problems---problems that we could not solve individually---can we now tackle together? Mass collaboration has a long, rich history in fields such as astronomy and ecology, but it is not yet common in social research. However, by describing successful projects from other fields and providing a few key organizing principles, I hope to convince you of two things. First, mass collaboration can be harnessed for social research. And, second, researchers who use mass collaboration will be able to solve problems that had previously seemed impossible. Although mass collaboration is often promoted as a way to save money, it is much more than that. As I will show, mass collaboration doesn’t just allow us to do research cheaper, it allows us to do research better.