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| 1 | The Contestation of Tech Ethics: A Sociotechnical Approach to Technology Ethics in Practice显示文摘This article introduces the special issue“Technology Ethics in Action:Critical and Interdisciplinary Perspectives”.In response to recent controversies about the harms of digital technology,discourses and practices of“tech ethics”have proliferated across the tech industry,academia,civil society,and government.Yet despite the seeming promise of ethics,tech ethics in practice suffers from several significant limitations:tech ethics is vague and toothless,has a myopic focus on individual engineers and technology design,and is subsumed into corporate logics and incentives.These limitations suggest that tech ethics enables corporate“ethics-washing”:embracing the language of ethics to defuse criticism and resist government regulation,without committing to ethical behavior.Given these dynamics,I describe tech ethics as a terrain of contestation where the central debate is not whether ethics is desirable,but what“ethics”entails and who gets to define it.Current approaches to tech ethics are poised to enable technologists and technology companies to label themselves as“ethical”without substantively altering their practices.Thus,those striving for structural improvements in digital technologies must be mindful of the gap between ethics as a mode of normative inquiry and ethics as a practical endeavor.In order to better evaluate the opportunities and limits of tech ethics,I propose a sociotechnical approach that analyzes tech ethics in light of who defines it and what impacts it generates in practice. | Ben Green | 2021 | Journal of Social Computing2021，2，3： | 7 |
| 2 | From Symbols to Embeddings:A Tale of Two Representations in Computational Social Science显示文摘Computational Social Science(CSS),aiming at utilizing computational methods to address social science problems,is a recent emerging and fast-developing field.The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks,which contain rich text and network data for investigation.However,these large-scale and multi-modal data also present researchers with a great challenge:how to represent data effectively to mine the meanings we want in CSS?To explore the answer,we give a thorough review of data representations in CSS for both text and network.Specifically,we summarize existing representations into two schemes,namely symbol-based and embeddingbased representations,and introduce a series of typical methods for each scheme.Afterwards,we present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS.From the statistics of these applications,we unearth the strength of each kind of representations and discover the tendency that embedding-based representations are emerging and obtaining increasing attention over the last decade.Finally,we discuss several key challenges and open issues for future directions.This survey aims to provide a deeper understanding and more advisable applications of data representations for CSS researchers. | Huimin Chen Cheng Yang Xuanming Zhang Zhiyuan Liu Maosong Sun Jianbin Jin | 2021 | Journal of Social Computing2021，2，2： | 4 |
| 3 | Social Computing Unhinged显示文摘Social computing is ubiquitous and intensifying in the 21st Century.Originally used to reference computational augmentation of social interaction through collaborative filtering,social media,wikis,and crowdsourcing,here I propose to expand the concept to cover the complete dynamic interface between social interaction and computation,including computationally enhanced sociality and social science,socially enhanced computing and computer science,and their increasingly complex combination for mutual enhancement.This recommends that we reimagine Computational Social Science as Social Computing,not merely using computational tools to make sense of the contemporary explosion of social data,but also recognizing societies as emergent computers of more or less collective intelligence,innovation and flourishing.It further proposes we imagine a socially inspired computer science that takes these insights into account as we build machines not merely to substitute for human cognition,but radically complement it.This leads to a vision of social computing as an extreme form of human computer interaction,whereby machines and persons recursively combine to augment one another in generating collective intelligence,enhanced knowledge,and other social goods unattainable without each other.Using the example of science and technology,I illustrate how progress in each of these areas unleash advances in the others and the beneficial relationship between the technology and science of social computing,which reveals limits of sociality and computation,and stimulates our imagination about how they can reach past those limits together. | James Evans | 2020 | Journal of Social Computing2020，1，1： | 2 |
| 4 | Inaugural Message from Editors-in-Chief显示文摘On behalf of the Editorial Board,it is our privilege to present the first issue of the Journal of Social Computing,affectionately shortened JoSoCo.Social computing concerns the intersection of social behavior and computational systems.Historically focused on recreating human social conventions and contexts through software and technology,we propose its expansion to the full interface between social interaction and computation. | James Evans Xiaoming Fu Jar-Der Luo | 2020 | Journal of Social Computing2020，1，1： | 1 |
| 5 | Estimating the size of online social networks显示文摘 | Ye S Wu S F | 2011 | International Journal of Social Computing and Cyber-Physieal Systems2011，1，2： | 1 |
| 6 | Predicting Tie Strength of Chinese Guanxi by Using Big Data of Social Networks显示文摘This paper poses a question:How many types of social relations can be categorized in the Chinese context?In social networks,the calculation of tie strength can better represent the degree of intimacy of the relationship between nodes,rather than just indicating whether the link exists or not.Previou research suggests that Granovetter measures tie strength so as to distinguish strong ties from weak ties,and the Dunbar circle theory may offer a plausible approach to calculating 5 types of relations according to interaction frequency via unsupervised learning(e.g.,clustering interactive data between users in Facebook and Twitter).In this paper,we differentiate the layers of an ego-centered network by measuring the different dimensions of user's online interaction data based on the Dunbar circle theory.To label the types of Chinese guanxi,we conduct a survey to collect the ground truth from the real world and link this survey data to big data collected from a widely used social network platform in China.After repeating the Dunbar experiments,we modify our computing methods and indicators computed from big data in order to have a model best fit for the ground truth.At the same time,a comprehensive set of effective predictors are selected to have a dialogue with existing theories of tie strength.Eventually,by combining Guanxi theory with Dunbar circle studies,four types of guanxi are found to represent a four-layer model of a Chinese ego-centered network. | Xin Gao Jar-Der Luo Kunhao Yang Xiaoming Fu Loring Liu Weiwei Gu | 2020 | Journal of Social Computing2020，1，1： | 1 |
| 7 | Hybrid Predictive Ensembles:Synergies Between Human and Computational Forecasts显示文摘An increasing proportion of decisions,design choices,and predictions are being made by hybrid groups consisting of humans and artificial intelligence(AI).In this paper,we provide analytic foundations that explain the potential benefits of hybrid groups on predictive tasks,the primary use of AI.Our analysis relies on interpretive and generative signal frameworks as well as a distinction between the big data used by AI and the thick,often narrative data used by humans.We derive several conditions on accuracy and correlation necessary for humans to remain in the loop.We conclude that human adaptability along with the potential for atypical cases that mislead AI will likely mean that humans always add value on predictive tasks. | Lu Hong PJ Lamberson Scott E Page | 2021 | Journal of Social Computing2021，2，2： | 1 |
| 8 | Uncovering the Online Social Structure Surrounding COVID-19显示文摘How do people talk about COVID-19 online?To address this question,we offer an unsupervised framework that allows us to examine Twitter framings of the pandemic.Our approach employs a network-based exploration of social media data to identify,categorize,and understand communication patterns about the novel coronavirus on Twitter.The simplest structure that emerges from our analysis is the distinction between the internal/personal,external/global,and generic threat framings of the pandemic.This structure replicates in different Twitter samples and is validated using the variation of information measure,reflecting the significance and stability of our findings.Such an exploratory study is useful for understanding the contours of the natural,non-random structure in this online space.We contend that this understanding of structure is necessary to address a host of causal,supervised,and related questions downstream. | Philip D.Waggoner Robert Y.Shapiro Samuel Frederick Ming Gong | 2021 | Journal of Social Computing2021，2，2： | 1 |
| 9 | An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks显示文摘Social media have dramatically changed the mode of information dissemination.Various models and algorithms have been developed to model information diffusion and address the influence maximization problem in complex social networks.However,it appears difficult for state-of-the-art models to interpret complex and reversible real interactive networks.In this paper,we propose a novel influence diffusion model,i.e.,the Operator-Based Model(OBM),by leveraging the advantages offered from the heat diffusion based model and the agent-based model.The OBM improves the performance of simulated dissemination by considering the complex user context in the operator of the heat diffusion based model.The experiment obtains a high similarity of the OBM simulated trend to the real-world diffusion process by use of the dynamic time warping method.Furthermore,a novel influence maximization algorithm,i.e.,the Global Topical Support Greedy algorithm(GTS-Greedy algorithm),is proposed corresponding to the OBM.The experimental results demonstrate its promising performance by comparing it against other classic algorithms. | Chenting Jiang Anthony D’Arienzo Weihua Li Shiqing Wu Quan Bai | 2021 | Journal of Social Computing2021，2，2： | 1 |
| 10 | How to Better Identify Venture Capital Network Communities:Exploration of A Semi-Supervised Community Detection Method显示文摘In the field of Venture Capital(VC),researchers have found that VC companies are more likely to jointly invest with other VC companies.This paper attempts to realize a semi-supervised community detection of the VC network based on the data of VC networking and the list of industry leaders.The main research method is to design the initial label of community detection according to the evolution of components of the VC industry leaders.The results show that the community structure of the VC network has obvious distinguishing characteristics,and the aggregation of these communities is affected by the type of institution,the source of capital,the background of personnel,and the field of investment and the geographical position.Meanwhile,by comparing the results of the semi-supervised community detection algorithm with the results of community detection using extremal optimization,it can be shown to some extent that the semi-supervised community detection results in the VC network are more accurate and reasonable. | Hong Xiong Ying Fan | 2021 | Journal of Social Computing2021，2，1： | 1 |
| 11 | Learning Universal Network Representation via Link Prediction by Graph Convolutional Neural Network显示文摘Network representation learning algorithms,which aim at automatically encoding graphs into low-dimensional vector representations with a variety of node similarity definitions,have a wide range of downstream applications.Most existing methods either have low accuracies in downstream tasks or a very limited application field,such as article classification in citation networks.In this paper,we propose a novel network representation method,named Link Prediction based Network Representation(LPNR),which generalizes the latest graph neural network and optimizes a carefully designed objective function that preserves linkage structures.LPNR can not only learn meaningful node representations that achieve competitive accuracy in node centrality measurement and community detection but also achieve high accuracy in the link prediction task.Experiments prove the effectiveness of LPNR on three real-world networks.With the mini-batch and fixed sampling strategy,LPNR can learn the embedding of large graphs in a few hours. | Weiwei Gu Fei Gao Ruiqi Li Jiang Zhang | 2021 | Journal of Social Computing2021，2，1： | 1 |
| 12 | Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis显示文摘During the past decades,the term“social computing”has become a promising interdisciplinary area in the intersection of computer science and social science.In this work,we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project(DBLP),a representative computer science bibliography website.We have observed a series of trends in the development of social computing,including the evolution of the number of publications,popular keywords,top venues,international collaborations,and research topics.Our findings will be helpful for researchers and practitioners working in relevant fields. | Jiaqi Wu Bodian Ye Qingyuan Gong Atte Oksanen Cong Li Jingjing Qu Felicia F.Tian Xiang Li Yang Chen | 2022 | Journal of Social Computing2022，3，4： | 1 |
| 13 | Estimating Multiple Socioeconomic Attributes via Home Location-A Case Study in China显示文摘Inferring people’s Socioeconomic Attributes(SEAs),including income,occupation,and education level,is an important problem for both social sciences and many networked applications like targeted advertising and personalized recommendation.Previous works mainly focus on estimating SEAs from peoples’cyberspace behaviors and relationships,such as the content of tweets or the social networks between online users.Besides cyberspace data,alternative data sources about users’physical behavior,like their home location,may offer new insights.More specifically,in this paper,we study how to predict a person’s income level,family income level,occupation type,and education level from his/her home location.As a case study,we collect people’s home locations and socioeconomic attributes through a survey involving 9 provinces and 85 cities in China.We further enrich home location with the knowledge from real estate websites,government statistics websites,online map services,etc.To learn a shared representation from input features as well as attribute-specific representations for different SEAs,we propose H2SEA,a factorization machine-based multi-task learning method with attention mechanism.Extensive experiment results show that:(1)Home location can clearly improve the estimation accuracy for all SEA prediction tasks(e.g.,80.2%improvement in terms of F1-score in estimating personal income level);(2)The proposed H2SEA model outperforms alternative models for SEA inference in terms of various evaluation metrics,such as Area Under Curve(AUC),F-measure,and specificity;(3)The performance of specific SEA prediction tasks(e.g.,personal income)can be further improved if H2SEA only focuses on cities or villages due to urban-rural gap in China;(4)Compared with online crawled housing price data,the area-level average income and Points Of Interest(POI)are more important features for SEA inferences in China. | Shichang Ding Xin Gao Yufan Dong Yiwei Tong Xiaoming Fu | 2021 | Journal of Social Computing2021，2，1： | 1 |
| 14 | The Hidden Sexual Minorities:Machine Learning Approaches to Estimate the Sexual Minority Orientation Among Beijing College Students显示文摘Based on the fourth-wave Beijing College Students Panel Survey(BCSPS),this study aims to provide accurate estimation of the percentage of the potential sexual minorities among the Beijing college students by using machine learning methods.Specifically,we employ random forest(RF),an ensemble learning approach for classification and regression,to predict the sexual orientation of those who were not willing to disclose his/her inherent sexual identity.To overcome the imbalance problem arising from far different numerical proportion of sexual minority and majority members,we adopt the repeated random sub-sampling for training set by partitioning those who expressed heterosexual orientation into different number of splits and further combining each split with those who expressed sexual minority orientation.The prediction from 24-split random forest suggests that youths in Beijing with sexual minority orientation amount to 5.71%,almost two times that of the original estimation 3.03%.The results are robust to alternative learning methods and covariate sets.Besides,it is also suggested that random forest outperforms other learning algorithms,including AdaBoost,Naïve Bayes,support vector machine(SVM),and logistic regression,in dealing with missing data,by showing higher accuracy,F1 score,and area under curve(AUC)value. | Yunsong Chen Guangye He Guodong Ju | 2022 | Journal of Social Computing2022，3，2： | 1 |
| 15 | Data Science as Political Action:Grounding Data Science in a Politics of Justice显示文摘In response to public scrutiny of data-driven algorithms,the field of data science has adopted ethics training and principles.Although ethics can help data scientists reflect on certain normative aspects of their work,such efforts are ill-equipped to generate a data science that avoids social harms and promotes social justice.In this article,I argue that data science must embrace a political orientation.Data scientists must recognize themselves as political actors engaged in normative constructions of society and evaluate their work according to its downstream impacts on people’s lives.I first articulate why data scientists must recognize themselves as political actors.In this section,I respond to three arguments that data scientists commonly invoke when challenged to take political positions regarding their work.In confronting these arguments,I describe why attempting to remain apolitical is itself a political stance-a fundamentally conservative one-and why data science’s attempts to promote“social good”dangerously rely on unarticulated and incrementalist political assumptions.I then propose a framework for how data science can evolve toward a deliberative and rigorous politics of social justice.I conceptualize the process of developing a politically engaged data science as a sequence of four stages.Pursuing these new approaches will empower data scientists with new methods for thoughtfully and rigorously contributing to social justice. | Ben Green | 2021 | Journal of Social Computing2021，2，3： | 1 |
| 16 | Social Scale and Collective Computation:Does Information Processing Limit Rate of Growth in Scale?显示文摘Collective computation is the process by which groups store and share information to arrive at decisions for collective behavior.How societies engage in effective collective computation depends partly on their scale.Social arrangements and technologies that work for small-and mid-scale societies are inadequate for dealing effectively with the much larger communication loads that societies face during the growth in scale that is a hallmark of the Holocene.An important bottleneck for growth may be the development of systems for persistent recording of information(writing),and perhaps also the abstraction of money for generalizing exchange mechanisms.Building on Shin et al.,we identify a Scale Threshold to be crossed before societies can develop such systems,and an Information Threshold which,once crossed,allows more or less unlimited growth in scale.We introduce several additional articles in this special issue that elaborate or evaluate this Thresholds Model for particular types of societies or times and places in the world. | Timothy A.Kohler Darcy Bird David H.Wolpert | 2022 | Journal of Social Computing2022，3，1： | 1 |
| 17 | From Ethics Washing to Ethics Bashing:A Moral Philosophy View on Tech Ethics显示文摘Weaponized in support of deregulation and self-regulation,“ethics”is increasingly identified with technology companies’self-regulatory efforts and with shallow appearances of ethical behavior.So-called“ethics washing”by tech companies is on the rise,prompting criticism and scrutiny from scholars and the tech community.The author defines“ethics bashing”as the parallel tendency to trivialize ethics and moral philosophy.Underlying these two attitudes are a few misunderstandings:(1)philosophy is understood in opposition and as alternative to law,political representation,and social organizing;(2)philosophy and“ethics”are perceived as formalistic,vulnerable to instrumentalization,and ontologically flawed;and(3)moral reasoning is portrayed as mere“ivory tower”intellectualization of complex problems that need to be dealt with through other methodologies.This article argues that the rhetoric of ethics and morality should not be reductively instrumentalized,either by the industry in the form of“ethics washing”,or by scholars and policy-makers in the form of“ethics bashing”.Grappling with the role of philosophy and ethics requires moving beyond simplification and seeing ethics as a mode of inquiry that facilitates the evaluation of competing tech policy strategies.We must resist reducing moral philosophy’s role and instead must celebrate its special worth as a mode of knowledge-seeking and inquiry.Far from mandating self-regulation,moral philosophy facilitates the scrutiny of various modes of regulation,situating them in legal,political,and economic contexts.Moral philosophy indeed can explainin the relationship between technology and other worthy goals and can situate technology within the human,the social,and the political. | Elettra Bietti | 2021 | Journal of Social Computing2021，2，3： | 1 |
| 18 | The Promise and Limits of Lawfulness:Inequality,Law,and the Techlash显示文摘In response to widespread skepticism about the recent rise of“tech ethics”,many critics have called for legal reform instead.In contrast with the“ethics response”,critics consider the“lawfulness response”more capable of disciplining the excesses of the technology industry.In fact,both are simultaneously vulnerable to industry capture and capable of advancing a more democratic egalitarian agenda for the information economy.Both ethics and law offer a terrain of contestation,rather than a predetermined set of commitments by which to achieve more democratic and egalitarian technological production.In advancing this argument,the essay focuses on two misunderstandings common among proponents of the lawfulness response.First,they misdiagnose the harms of the techlash as arising from law’s absence.In fact,law mediates the institutions that it enacts,the productive activities it encases,and the modes and myths of production it upholds and legitimates.Second,this distinction between law’s absence and presence implies that once law’s presence is secured,the problems of the techlash will be addressed.This concedes the legitimacy of the very regimes currently at issue in law’s own legitimacy crisis,and those that have presided over the techlash.The twin moment of reckoning in tech and law thus poses a challenge to those looking to address discontent with technology with promises of future lawfulness. | SaloméViljoen | 2021 | Journal of Social Computing2021，2，3： | 1 |
| 19 | Misalignment Between Skills Discovered, Disseminated, and Deployed in the Knowledge Economy显示文摘The knowledge economy is a complex and dynamical system,where knowledge and skills are discovered through research,diffused via education,and deployed by industry.Dynamically aligning the supply of new knowledge with the demand for practical skills through education is critical for developing national innovation systems that maximize human flourishing.In this paper,we evaluate the complex alignment of skills across the knowledge economy by creating an integrated semantic model that neurally encodes invented,instructed,and instituted skills across three major datasets:research abstracts from the Web of Science,teaching syllabi from the Open Syllabus Project,and job advertisements from Burning Glass.Analyzing the high dimensional knowledge and skills space inscribed by these data,we draw critical insight about systemic misalignment between the diversity of skills supplied and demanded in the knowledge economy.Consistent with insights from economic geography,demand for skills from industry exhibits high entropy(diversity)at local,regional,and national levels,demonstrating dense complementarities between them at all levels of the economy.Consistent with the economics and sociology of innovation,we find low entropy in the invention of new knowledge and skills through research,as specialist researchers cluster within universities.We provide new evidence,however,for the low entropy of skills taught at local,regional,and national levels,illustrating a massive mismatch between diversity in skills supplied versus demanded.This misalignment is sustained by the spatial and institutional mismatch in the organization of education by researchers at the site of skill invention over use.Our findings suggestively trace the societal costs of tethering education to researchers with narrow knowledge rather than students with broad skill needs. | Bhargav Srinivasa Desikan James Evans | 2022 | Journal of Social Computing2022，3，3： | 0 |
| 20 | A Novel Hybrid Model for Gasoline Prices Forecasting Based on Lasso and CNN显示文摘Gasoline is the lifeblood of the national economy.The forecasting of gasoline prices is difficult because of frequent price fluctuations,its complex nature,diverse influencing factors,and low accuracy of prediction results.Previous studies mainly focus on forecasting gasoline prices in a single region by single time series analysis which ignores the daily price co-movement of different series from multiple regions.Because price co-movement may contain useful information for price forecasting,this paper proposes the LassoCNN ensemble model that combines statistical models and deep neural networks to forecast gasoline prices.In this model,the Least Absolute Shrinkage and Selection Operator(Lasso)screens and chooses the correlated time series to enhance the performance of forecasting and avoid overfitting,while Convolutional Neural Network(CNN)takes the selected multiple series as its input and then forecasts the gasoline prices in a certain region.Forecasting results of gasoline prices at the national level and regional levels by using the new method demonstrate that the new approach provides more accurate results for the predictions of gasoline prices than those results generated by alternative methods.Thus,the relevant series can enhance the performance of forecasting and help to gain better results. | Hu Yang Xinlu Tian Xin Jin Haijun Wang | 2022 | Journal of Social Computing2022，3，3： | 0 |
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