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[chengjun]

计算社会科学单元

1 计算社会科学简介

• Matthew J. Salganik Bit by Bit_ social research in the digital age-princeton university press (2017).pdf
• Should social science be more solution-oriented?.pdf
• Watts (2007) A twenty-first century science. Nature 445. Feb.pdf
• Watts 2016 Common sense.pdf
• Lazer et al (2009) Compuational social science. Science.pdf
• Lessons and Challenges in Computational Social Science.pdf
• King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science.pdf
• homework day 1 mobirise + republic

2 大数据

• Chetty The Fading American Dream- Trends in Absolute Income Mobility Since 1940.pdf
• Wu 2019 Small Team Disrupt.pdf
• Ginsberg (2009) Detecting influenza epidemics using search engine query data. Nature.pdf
• Blumenstock 2015 Predicting poverty and wealth from mobile phone metadata.pdf
• homework day 2 big data

3 休息

规模：复杂世界的简单法则 .epub

4 调查&实验

• Bond et al (2012) A 61-million-person experiment in social influence and political mobilization. Nature.pdf
• Salganik (2006) Experimental study of inequality and unpredictability in an artificial cultural market. Science.pdf
• Phan & Airoldi (2015) A natural experiment of social network formation and dynamics. PNAS.pdf
• Centola (2010) The spread of behavior in an online social network experiment. Science.pdf
• Kramer et al. (2014) Emotional Contagion.pdf
• homework day 4 survey + experiment

5 网络科学

• Barabasi Network Science book
• The spread of true and false news online.pdf
• Watts 1998 Collective_Dynamics_of_“Small-world”_Networks.pdf
• Barabasi (1999) Emergence of scaling in random networks.Science-509-12.pdf
• Schich 2014 A network framework of cultural history. Science.pdf
• Watts (2002) A simple model of global cascade on random networks. PNAS.pdf
• Granovetter (1978) Threshold models of collective behavior. AJS.pdf
• Granovetter (1973)The strength of weakties. AJS.pdf
• Uzzi, B., & Spiro, J. (2005). Collaboration and Creativity: The Small World Problem. American Journal of Sociology, 111(2), 447–504. https://doi.org/10.1086/432782
• homework day 5 network true-and-false-news

6 机器学习入门

• Python Data Science Handbook Essential Tools for Working with Data by Jake VanderPlas (z-lib.org).pdf
• Lazer et al.. 2014. The parable of Google Flu Traps in big data analysis.pdf
• Ginsberg (2009) Detecting influenza epidemics using search engine query data. Nature. v457. 19 Feb.07634.pdf.pdf
• Risi. 2019 Predicting history.pdf
• Salganik 2020 PNAS Measuring the predictability of life outcomes with a scientific mass collaboration.pdf
• homework day 6 machine learning googleflu

7 机器学习算法

• Blumenstock 2015 Predicting poverty and wealth from mobile phone metadata.pdf
• Frey, C. B., & Osborne, M. A. (2017). The future of employment How susceptible are jobs to computerisation. Technological forecasting and social change.pdf
• homework day 7 machine learning future work & fake news detection & poverty prediction

8 神经网络模型

• Dive into Deep Leaning-en-pytorch.pdf
• TemporalFusionTransformersforinterpretablemulti-horizontimeseriesforecasting.pdf
• homework day 8 deep learning fake news detection & imagenet vgg16 pretrained model

9 词向量嵌入模型

• Mikolov 2013 Efficient Estimation of Word Representations in Vector Space.pdf
• Mikolov 2013 Distributed Representations of Words and Phrases and their Compositionality.pdf
• Kozlowski, A. C., Taddy, M., & Evans, J. A. (2019). The Geometry of Culture- Analyzing the Meanings of Class through Word Embeddings. American Sociological Review.pdf
• Word embeddings quantify 100 years of gender and ethnic stereotypes.pdf
• how quantifying the shapes of the stories predict their sucess full.pdf
• The Cinderella Complex.pdf
• node2vec-kdd16.pdf
• Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). DeepWalk. Proceedings of the 20th ACM SIGKDD .pdf
• homework day 9 embeddings

10 自然语言处理

• Michel (2011) Quantitative analysis of culture using millions of digitized books. Science.pdf
• Storywrangler- A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter.pdf

[chengjun]

• https://socratesacademy.github.io/css/readings/RNN.html
• https://socratesacademy.github.io/css/readings/Attention.html
• https://socratesacademy.github.io/css/readings/Backpropagation.html
• https://socratesacademy.github.io/css/readings/Backpropagation.mhtml
• https://socratesacademy.github.io/css/readings/ACourseinMachineLearning.html
• https://socratesacademy.github.io/css/readings/BeyondWordEmbeddingsPart2.html
• https://socratesacademy.github.io/css/readings/IllustratedTransformer.html
• https://socratesacademy.github.io/css/readings/IllustratedWord2vec.html
• https://socratesacademy.github.io/css/readings/Illustrated_BERT_ELMo.html
• https://socratesacademy.github.io/css/readings/MachineLearningforEveryone.mhtml
• https://socratesacademy.github.io/css/readings/NeuralNetworkwithPyTorch.html
• https://socratesacademy.github.io/css/readings/Pandas.html
• https://socratesacademy.github.io/css/readings/Translation_with_a_Seq2Seq&Attention.html
• https://socratesacademy.github.io/css/readings/UnderstandingPyTorch.html
• https://socratesacademy.github.io/css/readings/gradient_descent.html
• https://socratesacademy.github.io/css/readings/Variants_of_Gradient_Descent.mhtml