Computational Social Science: Exciting Progress And Future Challenges
KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining San Francisco California USA August, 2016(2016)
摘要
The past 15 years have witnessed a remarkable increase in both the scale and scope of social and behavioral data available to researchers, leading some to herald the emergence of a new field: "computational social science." Against these exciting developments stands a stubborn fact: that in spite of many thousands of published papers, there has been surprisingly little progress on the "big" questions that motivated the field in the first place-questions concerning systemic risk in financial systems, problem solving in complex organizations, and the dynamics of epidemics or social movements, among others. In this talk I highlight some examples of research that would not have been possible just a handful of years ago and that illustrate the promise of CSS. At the same time, they illustrate its limitations. I then conclude with some thoughts on how CSS can bridge the gap between its current state and its potential.
更多查看译文
关键词
Social data,Computational Social Science
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络