# 参考文献

```
白朔天, 郝碧波, 李昂, 等, 2014. 微博用户的抑郁和焦虑预测[J]. 中国科学院大学学报, 31(6): 814-820.
陈云松, 吴晓刚, 胡安宁, 等, 2020. 社会预测:基于机器学习的研究新范式[J]. 社会学研究, 35(3): 94-117, 244.
范如国, 2018. 公共管理研究基于大数据与社会计算的方法论革命[J]. 中国社会科学(9): 74-91+205.
高见, 周涛, 2016. 大数据揭示经济发展状况[J]. 电子科技大学学报, 45(4): 625-633.
韩军徽, 2020. 计算社会科学中“守旧”与“维新”的方法论探讨[J]. 理论探索(4): 11-17.
黄萃, 杨超, 2020. “计算社会科学”与“社会计算”概念辨析与研究热点比较分析[J]. 信息资源管理学报, 10(6): 4-19.
郦全民, 2019. 计算社会科学的哲学透视[J]. 河北学刊, 39(5): 98-104.
凌昀, 李伦, 2020. 计算社会科学研究：范式转换与伦理问题[J]. 江汉论坛(9): 26-31.
吕鹏, 2024. 计算社会科学：学科体系与领域演进[J]. 求索(4): 84-94.
孟小峰, 张祎, 2019. 计算社会科学促进社会科学研究转型[J]. 社会科学(7): 3-10.
苏毓淞, 刘江锐, 2021. 计算社会科学与研究范式之争：理论的终结?[J]. 复旦学报（社会科学版）, 63(2): 189-196.
俞立平, 冉嘉睿, 罗宇舟, 等, 2023. 计算社会科学发展演变及学科框架与学科结构[J]. 重庆大学学报（社会科学版）, 29(2): 124-139.
汪静莹, 甘硕秋, 赵楠, 等, 2016. 基于微博用户的情绪变化分析[J]. 中国科学院大学学报, 33(6): 815-824.
王芳, 王宣艺, 陈硕, 2020. 经济学研究中的机器学习：回顾与展望[J]. 数量经济技术经济研究, 37(4): 146-164.
周涛, 高馨, 罗家德, 2022. 社会计算驱动的社会科学研究方法[J]. 社会学研究, 37(5): 130-155, 228-229.
Anon, 1978. Centrality in social networks conceptual clarification[J]. Social Networks, 1(3): 215-239.
Anon, 2011. Twitter mood predicts the stock market[J]. Journal of Computational Science, 2(1): 1-8.
Anon, 2021. Benthic fauna contribute to microplastic sequestration in coastal sediments[J]. Journal of Hazardous Materials, 415: 125583.
Abadie A, 2021. Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects[J]. Journal of Economic Literature, 59(2): 391-425.
Angrist J D, Pischke J S, 2009. Mostly Harmless Econometrics: An Empiricist’s Companion[M]. Princeton University Press.
Anselin L, 1995. Local Indicators of Spatial Association—LISA[J]. Geographical Analysis, 27(2): 93-115.
Armbrust M, View Profile, Fox A, et al., 2010. A view of cloud computing[J]. Communications of the ACM, 53(4): 50-58.
Aronow P M, Samii C, 2017. Estimating average causal effects under general interference, with application to a social network experiment[J]. The Annals of Applied Statistics, 11(4): 1912-1947.
Athey S, Imbens G, 2016. Recursive partitioning for heterogeneous causal effects[J]. Proceedings of the National Academy of Sciences, 113(27): 7353-7360.
Athey S, Imbens G W, 2017. The State of Applied Econometrics: Causality and Policy Evaluation[J]. Journal of Economic Perspectives, 31(2): 3-32.
Axelrod R, 1997. The Dissemination of Culture: A Model with Local Convergence and Global Polarization[J]. The Journal of Conflict Resolution, 41(2): 203-226.
Barabási A L, Albert R, 1999. Emergence of Scaling in Random Networks[J]. Science.
Barabási A L, Pósfai M, 2016. Network Science[M]. 1st edition. Cambridge, United Kingdom: Cambridge University Press.
Barocas S, Hardt M, Narayanan A, 2018. Fairness and machine learning limitations and opportunities[C].
Barocas S, Hardt M, Narayanan A, 2023. Fairness and Machine Learning: Limitations and Opportunities[M]. Cambridge, Massachusetts: The MIT Press.
Beauchamp N, 2017. Predicting and Interpolating State-Level Polls Using Twitter Textual Data[J]. American Journal of Political Science, 61(2): 490-503.
Belloni A, Chernozhukov V, Hansen C, 2014. High-Dimensional Methods and Inference on Structural and Treatment Effects[J]. Journal of Economic Perspectives, 28(2): 29-50.
Blumenstock J, Cadamuro G, On R, 2015. Predicting poverty and wealth from mobile phone metadata[J]. Science.
Bond R M, Fariss C J, Jones J J, et al., 2012. A 61-million-person experiment in social influence and political mobilization[J]. Nature, 489(7415): 295-298.
Borgatti S P, Mehra A, Brass D J, et al., 2009. Network Analysis in the Social Sciences[J]. Science.
Boyd D, Crawford K, 2012. Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon[J]. Information, Communication & Society, 15(5): 662-679.
Braghieri L, Levy R, Makarin A, 2022. Social Media and Mental Health[J]. American Economic Review, 112(11): 3660-3693.
Castells M, 2000. Toward a Sociology of the Network Society[J]. Contemporary Sociology, 29(5): 693-699.
Centola D, 2010. The Spread of Behavior in an Online Social Network Experiment[J]. Science.
Centola D, Macy M, 2007. Complex Contagions and the Weakness of Long Ties[J]. American Journal of Sociology, 113(3): 702-734.
Chadefaux T, 2014. Early warning signals for war in the news[J]. Journal of Peace Research.
Chernozhukov V, Chetverikov D, Demirer M, et al., 2018. Double/debiased machine learning for treatment and structural parameters[J]. The Econometrics Journal, 21(1): C1-C68.
Choudhury M D, De S, 2014. Mental Health Discourse on reddit: Self-Disclosure, Social Support, and Anonymity[J]. Proceedings of the International AAAI Conference on Web and Social Media, 8(1): 71-80.
Cioffi-Revilla C, 2017. Introduction to Computational Social Science: Principles and Applications[M]. 2nd ed. 2017 edition. New York, NY: Springer.
Cioffi-Revilla C, 2017. Computation and Social Science[M]//Cioffi-Revilla C. Introduction to Computational Social Science: Principles and Applications. Cham: Springer International Publishing: 35-102.
Conte G L, Arnegard M E, Peichel C L, et al., 2012. The probability of genetic parallelism and convergence in natural populations[J]. Proceedings of the Royal Society B: Biological Sciences, 279(1749): 5039-5047.
Conte R, Gilbert N, Bonelli G, et al., 2012. Manifesto of computational social science[J]. The European Physical Journal Special Topics, 214(1): 325-346.
Davis J M V, Heller S B, 2017. Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs[J]. American Economic Review, 107(5): 546-550.
Deaton A, Cartwright N, 2018. Understanding and misunderstanding randomized controlled trials[J]. Social Science & Medicine (1982), 210: 2-21.
Edelmann A, Wolff T, Montagne D, et al., 2020. Computational Social Science and Sociology[J]. Annual Review of Sociology, 46(1): 61-81.
Epstein J M, Axtell R, 1996. Growing artificial societies:  Social science from the bottom up[M]. Cambridge, MA, US: The MIT Press: xv, 208.
Fagiolo G, Moneta A, Windrum P, 2007. A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems[J]. Computational Economics, 30(3): 195-226.
Fararo T J, 1989. The Meaning of General Theoretical Sociology: Tradition and Formalization[M]. Cambridge ; New York: Cambridge University Press.
FerraraEmilio, VarolOnur, DavisClayton, et al., 2016. The rise of social bots[J]. Communications of the ACM.
Fisher R A, 1935. The design of experiments[M]. Oxford, England: Oliver & Boyd: xi, 251.
Foster I, Ghani R, Jarmin R S, et al., 2016. Big Data and Social Science: A Practical Guide to Methods and Tools[M]. 1st edition. Boca Raton, FL: Chapman and Hall/CRC.
Gilbert N, 2008. Researching social life[M]//Researching social life. Sage Publications Ltd.
Ginsberg J, Mohebbi M H, Patel R S, et al., 2009. Detecting influenza epidemics using search engine query data[J]. Nature, 457(7232): 1012-1014.
Glaeser E L, Sacerdote B, Scheinkman J, 1996. Crime and Social Interactions[J]. The Quarterly Journal of Economics, 111(2): 507-548.
González-Bailón S, Borge-Holthoefer J, Rivero A, et al., 2011. The Dynamics of Protest Recruitment through an Online Network[J]. Scientific Reports, 1(1): 197.
Goodchild M F, 2007. Citizens as sensors: the world of volunteered geography[J]. GeoJournal, 69(4): 211-221.
Granovetter M S, 1973. The Strength of Weak Ties[J]. American Journal of Sociology, 78(6): 1360-1380.
Grimmer J, Roberts M E, Stewart B M, 2021. Machine Learning for Social Science: An Agnostic Approach[J]. Annual Review of Political Science, 24(Volume 24, 2021): 395-419.
Grimmer J, Stewart B M, 2013. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts[J]. Political Analysis, 21(3): 267-297.
Groves R M, Jr F J F, Couper M P, et al., 2009. Survey Methodology[M]. 2nd edition. Somerset: Wiley.
Hastie T, Tibshirani R, Friedman J, 2009. Overview of Supervised Learning[M]//Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer: 9-41.
Hilbert M, 2016. Big Data for Development: A Review of Promises and Challenges[J]. Development Policy Review, 34(1): 135-174.
Hofman J M, Watts D J, Athey S, et al., 2021. Integrating explanation and prediction in computational social science[J]. Nature, 595(7866): 181-188.
Holland J H, 1996. Hidden Order: How Adaptation Builds Complexity[M]. Cambridge, Mass.: Basic Books.
Howison J, Wiggins A, Crowston K, 2011. Validity Issues in the Use of Social Network Analysis with Digital Trace Data[J]. Journal of the Association for Information Systems, 12(12).
IMAI K, KEELE L, TINGLEY D, et al., 2011. Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies[J]. American Political Science Review, 105(4): 765-789.
J. A. V. Dijk, 2005. The Deepening Divide: Inequality in the Information Society[M]. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc.
Jean N, Burke M, Xie M, et al., 2016. Combining satellite imagery and machine learning to predict poverty[J]. Science.
Jurafsky D, Martin J H, 2025. Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition with language models[M]. 3rd version.
Kitchin R, 2014. Big Data, new epistemologies and paradigm shifts[J]. Big Data & Society, 1(1): 2053951714528481.
Kohavi R, Tang D, Xu Y, 2020. Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing[M].
Kramer A D I, Guillory J E, Hancock J T, 2014. Experimental evidence of massive-scale emotional contagion through social networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 111(24): 8788-8790.
L. Manzo, P. Devine‐Wright, 2020. Place Attachment : Advances in Theory, Methods and Applications[M]. Routledge.
Lazer D, Kennedy R, King G, et al., 2014. Big data. The parable of Google Flu: traps in big data analysis[J]. Science (New York, N.Y.), 343(6176): 1203-1205.
Lazer D, Pentland A, Adamic L, et al., 2009. Computational Social Science[J]. Science, 323(5915): 721-723.
LeCun Y, Bengio Y, Hinton G, 2015. Deep learning[J]. Nature, 521(7553): 436-444.
Liu D M, Salganik M J, 2019. Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge[J]. Socius, 5: 2378023119849803.
Lux T, Marchesi M, 1999. Scaling and criticality in a stochastic multi-agent model of a financial market[J]. Nature, 397(6719): 498-500.
M. Newman, 2018. Network search[M]. Oxford University Press.
Macy M W, Willer R, 2002. From Factors to Actors: Computational Sociology and Agent-Based Modeling[J]. Annual Review of Sociology, 28(Volume 28, 2002): 143-166.
Manzo L, Devine-Wright P, 2013. Place Attachment: Advances in Theory, Methods and Applications[M]. London: Routledge.
Mayer-Schönberger V, Cukier K, 2013. Big data: A revolution that will transform how we live, work, and think[M]. Boston, MA: Houghton Mifflin Harcourt: 242.
Miller J H, Page S E, 2007. Complex adaptive systems: An introduction to computational models of social life[M]. Princeton, NJ, US: Princeton University Press: xix, 263.
Mitchell J M, Bogenschutz M, Lilienstein A, et al., 2021. MDMA-assisted therapy for severe PTSD: a randomized, double-blind, placebo-controlled phase 3 study[J]. Nature Medicine, 27(6): 1025-1033.
Mitchell M, 2011. Complexity: A Guided Tour[M]. 1st edition. New York, NY: Oxford University Press.
Mitchell R K, Agle B R, Chrisman J J, et al., 2011. Toward a Theory of Stakeholder Salience in Family Firms[J]. Business Ethics Quarterly, 21(2): 235-255.
Molina M, Garip F, 2019. Machine Learning for Sociology[A]. Rochester, NY: Social Science Research Network.
Molnar C, 2022. Interpretable Machine Learning: A Guide For Making Black Box Models Explainable[M]. Munich, Germany: Independently published.
Moreno J L, 1934. Who shall survive?: A new approach to the problem of human interrelations[M]. Washington, DC, US: Nervous and Mental Disease Publishing Co: xvi, 441.
Mullainathan S, Spiess J, 2017. Machine Learning: An Applied Econometric Approach[J]. Journal of Economic Perspectives, 31(2): 87-106.
Murphy K P, 2022. Probabilistic Machine Learning: An Introduction[M]. Cambridge, Massachusetts London, England: The MIT Press.
Neal B, 2020. Introduction to Causal Inference[J]. Course Lecture Notes (draft), 132.
Newman M E J, Girvan M, 2004. Finding and evaluating community structure in networks[J]. Physical Review E, 69(2): 026113.
Oliver N, Lepri B, Sterly H, et al., 2020. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle[J]. Science Advances.
Orben A, Przybylski A K, 2019. The association between adolescent well-being and digital technology use[J]. Nature Human Behaviour, 3(2): 173-182.
Orcutt G H, 1957. A New Type of Socio-Economic System[J]. The Review of Economics and Statistics, 39(2): 116-123.
Pang B, Lee L, 2008. Opinion mining and sentiment analysis[M].
Pearl J, 2009. Causality: Models, Reasoning and Inference[M]. 2nd edition. Cambridge, U.K. ; New York: Cambridge University Press.
Perry G, 2013. Behind the shock machine: The untold story of the notorious Milgram psychology experiments[M]. New York, NY, US: New Press: x, 340.
Preis T, Moat H S, Stanley H E, 2013. Quantifying Trading Behavior in Financial Markets Using Google Trends[J]. Scientific Reports, 3(1): 1684.
Provost F, Fawcett T, 2013. Data Science and its Relationship to Big Data and Data-Driven Decision Making[J]. Big Data, 1(1): 51-59.
Radicchi F, Fortunato S, Markines B, et al., 2009. Diffusion of scientific credits and the ranking of scientists[J]. Physical Review E, 80(5): 056103.
Rubin D B, 1974. Estimating causal effects of treatments in randomized and nonrandomized studies[J]. Journal of Educational Psychology, 66(5): 688-701.
Rudin C, 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead[J]. Nature Machine Intelligence, 1(5): 206-215.
Salganik M J, 2017. Bit by Bit: Social Research in the Digital Age[M]. Illustrated edition. Princeton Oxford: Princeton University Press.
Savage M, Burrows R, 2007. The Coming Crisis of Empirical Sociology[J]. Sociology, 41(5): 885-899.
Sawyer R K, 2005. Social Emergence[M]. Cambridge University Press.
Schelling T C, 1971. Dynamic models of segregation†[J]. Journal of Mathematical Sociology.
Shalizi C R, Thomas A C, 2011. Homophily and contagion are generically confounded in observational social network studies[J]. Sociological Methods & Research, 40(2): 211-239.
Shelton T, Zook M, Wiig A, 2015. The “Actually Existing Smart City”[A]. Rochester, NY: Social Science Research Network.
Shmueli G, 2010. To Explain or to Predict?[J]. Statistical Science, 25(3): 289-310.
Simon H A, 1955. A Behavioral Model of Rational Choice[J]. The Quarterly Journal of Economics, 69(1): 99-118.
Simon H A, 1996. The Sciences of the Artificial - 3rd Edition[M]. 3rd edition. Cambridge, Mass.: MIT Press.
Tesfatsion L, Judd K L, 2006. Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics[M]. Amsterdam, The Netherlands: Elsevier.
Tufekci Z, 2014. Social Movements and Governments in the Digital Age: Evaluating a Complex Landscape[J]. Journal of International Affairs, 68(1): 1-18.
Ugander J, Backstrom L, Marlow C, et al., 2012. Structural diversity in social contagion[J]. Proceedings of the National Academy of Sciences, 109(16): 5962-5966.
Uzzi B, Mukherjee S, Stringer M, et al., 2013. Atypical Combinations and Scientific Impact[J]. Science.
Vosoughi S, Roy D, Aral S, 2018. The spread of true and false news online[J]. Science.
Wager S, Athey S, 2018. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests[J]. Journal of the American Statistical Association.
Wasserman S, Faust K, 1994. Social network analysis:  Methods and applications[M]. New York, NY, US: Cambridge University Press: xxxi, 825.
Watts D J, Strogatz S H, 1998. Collective dynamics of ‘small-world’ networks[J]. Nature, 393(6684): 440-442.
Watts S, 2014. User Skills for Qualitative Analysis: Perspective, Interpretation and the Delivery of Impact[J]. Qualitative Research in Psychology, 11(1): 1-14.
Zimmer M, 2010. “But the data is already public”: on the ethics of research in Facebook[J]. Ethics and Information Technology, 12(4): 313-325.
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