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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/css-capstone
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课程名称：计算社会科学顶点项目 课程概述：祝贺你！你不仅成功完成了我们的知识旅程，而且现在已经具备实施全面的多方法计算社会科学工作流程所需的所有技能。在这个最终整合实验室中，我们将把这些技能付诸实践。我们从社交媒体网站收集数据（运用在专业化第一个课程中获得的技能）。接着，我们通过可视化结果网络来分析收集到的数据（基于第三课程的技能）。我们深入分析其中的一些关键方面，运用机器学习驱动的自然语言处理技术（发挥第二课程获得的见解）。最后，我们使用计算机模拟模型探索可能的生成机制，并仔细审视在我们的经验现实中未发现的方面，但这些方面有助于改善社会（借助于专业化第四课程获得的技能）。经过这一切，你已初步成为了一名计算社会科学家！ 课程大纲： 1. 起步与里程碑1：在此阶段，你将再次从两个YouTube频道抓取视频，但此次不再抓取特定新闻频道的推荐视频，而是根据新闻频道的名称与您的名字的组合进行搜索结果抓取。 2. 里程碑2：社交网络分析：使用Gephi软件分析一个社交网络。 3. 里程碑3：自然语言处理：在这一整合实验室的里程碑中，选定两段借助我们的社交网络分析（SNA）识别出的关键视频，分析评论区的情感和情绪，我们使用IBM Watson的NLP技术。 4. 里程碑4：基于代理的计算机模拟：在这一阶段，你将利用前面里程碑创建的数据，采用双向流动模型，探索思想如何在社会中传播，从而自下而上地构建自己的人工社会。 完成以上全部内容后，你可以自信地称自己为一名初学者计算社会科学家！
Name:Getting Started and Milestone 1
Description:For this milestone, you will again web scrape videos from two YouTube channels. You will be assigned two channels to scrape. In contrast to the previous version of this exercise, you will NOT scrape the featured videos of the specified news channel, but the search results of the name of the news channel in combination with your name.
Name:Milestone 2: Social Network Analysis
Description:In this milestone, you will analyze a social network with help of the software Gephi.
Name:Milestone 3: Natural Language Processing
Description:In this milestone of our Integrative Lab, you will select two of the key videos identified with help of our SNA, and analyze the sentiment and emotions contained in the comment sections of the videos. We use NLP from IBM Watson for this.
Name:Milestone 4: Agent-Based Computer Simulations
Description:In this milestone, you will take all the data you created in the previous milestones and use a two-step flow model and discover how ideas can diffuse into society. Through this exercise you will grow your own artificial society from the bottom-up.
CONGRATULATIONS! Not only did you accomplish to finish our intellectual tour de force, but, by now, you also already have all required skills to execute a comprehensive multi-method workflow of computational social science. We will put these skills to work in this final integrative lab, where we are bringing it all together. We scrape data from a social media site (drawing on the skills obtained in the 1st course of this specialization). We then analyze the collected data by visualizing the resulting networks (building on the skills obtained in the 3rd course). We analyze some key aspects of it in depth, using machine learning powered natural language processing (putting to work the insights obtained during the 2nd course). Finally, we use a computer simulation model to explore possible generative mechanism and scrutinize aspects that we did not find in our empirical reality, but that help us to improve this aspect of society (drawing on the skills obtained during the 4th course of this specialization). The result is the first glimpse at a new way of doing social science in a digital age: computational social science. Congratulations! Having done all of this yourself, you can consider yourself a fledgling computational social scientist!
