周四直播：社会计算——探索新的研究范式 | 社会计算读书会

导语

过去十几年，社会科学与计算科学、复杂科学的交叉研究取得了迅猛发展，研究人员利用观察数据、实验设计和大规模仿真发表了海量论文，同时也涌现出了许多研究方向。集智俱乐部主办的“社会计算”主题的系列读书会（5月27日正式开始），通过从不同角度对其进行解读、分析和探讨，希望能给相关的从业者、研究人员、科研爱好者一些启发和灵感。

5月27日（周四）20:00-22:00将进行本读书会第一期分享，由读书会发起人、河北大学的王硕老师给大家介绍社会计算的产生、研究现状、研究工具及研究案例，题目《社会计算：探索新的研究范式》。

社会计算系列读书会的部分主题会在集智俱乐部B站公开直播，欢迎扫描文中二维码或点击文末“阅读原文”，点击「我要听」预约直播，可在开播前获得提醒！

内容简介

在这个大数据的时代里，数字足迹毫无保留地记录下了日益增多的人类社会中的各种互动信息，而对这些信息的研究可以使我们对社会的本质及其运作规律获得前所未有的认知和理解。计算能力的改变让社会计算领域的研究取得了长足进步，诸如机器学习和自然语言处理等技术的进步，使得科学家可以检测和分析那些隐含的模式。计算仿真将使我们拥有模拟已知现实甚至探索未知条件的能力，这些变化彻底改变了传统的社会科学研究范式。我们相信，通过数据与计算的结合来帮助解决社会问题已成必然趋势，社会科学的黄金时代已经到来。

主讲人

王硕，中国人民大学网络与移动数据管理管理实验室博士，现为河北大学数学与科学学院数据科学系讲师。目前研究领域为自然语言处理、机器学习、数据科学、知识融合等，参与完成多项国家重大研究重点支持计划或重点研发项目，发表关于情绪心理学与知识融合方面的论文多篇。

直播信息

直播时间：5月27日（周四）20:00-22:00

参与方式 1：腾讯会议（需报名参加系列读书会。读书会成员可在腾讯会议中开麦交流，向主讲人提问，参与互动）

参与方式 2：集智俱乐部B站直播

扫描下方二维码，点击「我要听」即可预约直播，预约可在开播前获得提醒：

社会计算系列读书会招募中

为了推动交叉学科间的合作，促进社会计算的发展和研究，集智俱乐部主办的社会计算系列读书会，于5月27日（周四）正式启动，预计为期10-12周，每周四晚上20:00-22:00进行在线的论文分享与讨论，欢迎对社会计算、社会智能、复杂科学、社会科学等领域感兴趣的朋友报名参加。（报名长期有效）

时间：5月27日起，每周四晚20:00-22:00

模式：线上闭门读书会；（收费-退款的保证金模式、读书会成员认领解读论文）
费用：399元/人（可看录播视频）

链接：https://campus.swarma.org/form/activity/32

第一步：扫码填写报名信息。

第二步：信息填写之后，进入对应的付费信息，提交保证金399元。（购课相当于提交保证金，符合退费条件后可退费）。

第三步：添加负责人微信，拉入读书会讨论群。

读书会详情及退费规则请参考：

融合计算科学、社会科学与复杂科学：社会计算系列读书会启动招募

关于“社会计算”和“”计算社会科学

最近有很多朋友报名了社会计算读书会，在和大家沟通的过程中我们发现，对“社会计算” 和 “计算社会科学”这两个词语的理解，不同的人会有很大的差异。这两个领域在相当程度上是交融重合的，本读书会以社会计算为主题词，同时也适合从事计算社会科学等交叉研究的朋友参与。

这里摘录近期文章《“计算社会科学”与 “社会计算”概念辨析与研究热点比较分析》中的对比分析供大家参考。原文见：

http://jirm.whu.edu.cn/jwk3/xxzyglxb/CN/10.13365/j.jirm.2020.06.004

概念的辨析：

期刊的分类：

基于关键词聚类结果的比较：

图1. 国内社会计算文献关键词共现网络聚类图

图2. 国内计算社会科学文献关键词共现网络聚类图

图3. 国际计算社会科学文献关键词共现网络聚类图

图4. 国际计算社会科学文献关键词共现网络聚类图

从以上数据可以看出，“社会计算” 和 “计算社会科学”两个词语，无论在期刊的选择还是关键词的使用上，是存在非常高的重合的，两者不是非此即彼，而是相互交融且各具特色。

本次集智俱乐部的社会计算读书会，欢迎有相关研究背景的朋友的加入，无论是“社会计算” 、 “计算社会科学”以及任何专业，只要你感兴趣，有一定读论文能力，都非常欢迎！！也期待多元化的讨论碰撞出更多的火花！！

论文阅读清单

计算科学之于社会计算

可解释的智能模型/Interpretable Intelligent Models in CSS

Wang, Jingyuan, et al. “Alphastock: A buying-winners-and-selling-losers investment strategy using interpretable deep reinforcement attention networks.” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019.

Wang, Jingyuan, et al. “Deep Fuzzy Cognitive Maps for Interpretable Multivariate Time Series Prediction.” IEEE Transactions on Fuzzy Systems (2020).

Wang, Jingyuan, et al. “Multilevel wavelet decomposition network for interpretable time series analysis.” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018.

Wang, Jingyuan, et al. “Empowering A* search algorithms with neural networks for personalized route recommendation.” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019.

Wu, Ning, et al. “Learning Effective Road Network Representation with Hierarchical Graph Neural Networks.” Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020.

Lazer, David, and Jason Radford. “Data ex machina: introduction to big data.” Annual Review of Sociology 43 (2017): 19-39.

Gao, Jian, Yi-Cheng Zhang, and Tao Zhou. “Computational socioeconomics.” Physics Reports 817 (2019): 1-104.

Kou, ：, et al. “Deep learning based inverse model for building fire source location and intensity estimation.” Fire Safety Journal 121 (2021): 103310.

隐私保护与数据伦理/ Privacy and Data Ethics in CSS

Nov, Oded, and Sunil Wattal. “Social computing privacy concerns: antecedents and effects.” Proceedings of the SIGCHI conference on human factors in computing systems. 2009.

Akiti, Chandan, Anna Squicciarini, and Sarah Rajtmajer. “A Semantics-based Approach to Disclosure Classification in User-Generated Online Content.” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings. 2020.

孟小峰, 朱敏杰, and 刘俊旭. “大规模用户隐私风险量化研究.” 信息安全研究 5.9 (2019): 778-788.

Debatin, Bernhard. “Ethics, privacy, and self-restraint in social networking.” Privacy online. Springer, Berlin, Heidelberg, 2011. 47-60.

Post, Robert C. “Data privacy and dignitary privacy: Google Spain, the right to be forgotten, and the construction of the public sphere.” Duke LJ 67 (2017): 981.

孟小峰, et al. “数据垄断与其治理模式研究.” 信息安全研究 9 (2019).

孟小峰, 王雷霞, and 刘俊旭. “人工智能时代的数据隐私, 垄断与公平.” 大数据 6.1 (2020): 0.

基于代理的社会现象建模/Agent-based Modeling of Social Phenomena

Fioretti, Guido. “Agent-based simulation models in organization science.” Organizational Research Methods 16.2 (2013): 227-242.

Gómez-Cruz, Nelson Alfonso, Isabella Loaiza Saa, and Francisco Fernando Ortega Hurtado. “Agent-based simulation in management and organizational studies: a survey.” European Journal of Management and Business Economics (2017).

Secchi, Davide, and Martin Neumann. “Agent-based simulation of organizational behavior.” Springer, Cham doi 10 (2016): 978-3.

Zhang, Jun Jason, et al. “Cyber-physical-social systems: The state of the art and perspectives.” IEEE Transactions on Computational Social Systems 5.3 (2018): 829-840.

图像分析与文本挖掘/Image Analysis and Text Mining for CSS

Wu, Chuhan, et al. “Neural news recommendation with heterogeneous user behavior.” Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Wang, Hongwei, et al. “DKN: Deep knowledge-aware network for news recommendation.” Proceedings of the 2018 world wide web conference. 2018.

He, Li, Antonio Páez, and Desheng Liu. “Built environment and violent crime: An environmental audit approach using Google Street View.” Computers, Environment and Urban Systems 66 (2017): 83-95.

复杂科学之于社会计算

复杂系统动力学/Complex System Dynamics in CSS

Zhou, Fang, Linyuan Lü, and Manuel Sebastian Mariani. “Fast influencers in complex networks.” Communications in Nonlinear Science and Numerical Simulation 74 (2019): 69-83.

Vosoughi, Soroush, Deb Roy, and Sinan Aral. “The spread of true and false news online.” Science 359.6380 (2018): 1146-1151.

Ni, Shunjiang, Wenguo Weng, and Hui Zhang. “Modeling the effects of social impact on epidemic spreading in complex networks.” Physica A: Statistical Mechanics and its Applications 390.23-24 (2011): 4528-4534.

Lu, Peng, et al. “The sandpile model and empire dynamics.” Chaos, Solitons & Fractals 143 (2021): 110615.

Liu, Lu, et al. “Hot streaks in artistic, cultural, and scientific careers.” Nature 559.7714 (2018): 396-399.

社会系统的网络分析/Network analysis of social systems

Mariani, Manuel S., and Linyuan Lü. “Network-based ranking in social systems: three challenges.” Journal of Physics: Complexity 1.1 (2020): 011001.

Xu, Jia, et al. “Incentive mechanism for multiple cooperative tasks with compatible users in mobile crowd sensing via online communities.” IEEE Transactions on Mobile Computing 19.7 (2019): 1618-1633.

Wang, Jiangtao, et al. “Social-network-assisted worker recruitment in mobile crowd sensing.” IEEE Transactions on Mobile Computing 18.7 (2018): 1661-1673.

Varga, Attila. “Shorter distances between papers over time are due to more cross-field references and increased citation rate to higher-impact papers.” Proceedings of the National Academy of Sciences 116.44 (2019): 22094-22099.

Xie, Jiarong, et al. “Detecting and modelling real percolation and phase transitions of information on social media.” Nature Human Behaviour (2021): 1-8.

Li, Sijia, et al. “The impact of family violence incidents on personality changes: An examination of social media users’ messages in C hina.” PsyCh Journal (2021).

Lin, Yu-Ru, et al. “Facetnet: a framework for analyzing communities and their evolutions in dynamic networks.” Proceedings of the 17th international conference on World Wide Web. 2008.

Huang, Jianbin, et al. “Density-based shrinkage for revealing hierarchical and overlapping community structure in networks.” Physica A: Statistical Mechanics and its Applications 390.11 (2011): 2160-2171.

智慧城市/Smart City

Bettencourt, Luís MA. “The origins of scaling in cities.” science 340.6139 (2013): 1438-1441.

Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. “Predicting poverty and wealth from mobile phone metadata.” Science 350.6264 (2015): 1073-1076.

Ju, Jingrui, Luning Liu, and Yuqiang Feng. “Citizen-centered big data analysis-driven governance intelligence framework for smart cities.” Telecommunications Policy 42.10 (2018): 881-896.

He, Li, et al. “Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data.” ISPRS International Journal of Geo-Information 9.6 (2020): 342.

Li, Hui-min, et al. “Analyzing the relationship between developed land area and nighttime light emissions of 36 Chinese cities.” Remote Sensing 11.1 (2019): 10.

Wang, Lingli, et al. “Voice-Based AI in Call Center Customer Service: Evidence from a Field Experiment.” Call Center Customer Service: Evidence from a Field Experiment (June 22, 2020) (2020).

Wang, Chenyang, Yang Gao, and Hui Zhang. “Typical patterns of government response measures and trends for COVID-19 pandemic.” Proceedings of the 6th ACM SIGSPATIAL International Workshop on Emergency Management using GIS. 2020.

阅读论文清单链接：

https://pattern.swarma.org/path?id=126

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