Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements includ...Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.展开更多
With the convergence of mobile communication network and Internet in depth, mobile Internet is penetrating into every field of people＇s life. Smart phone bring us great convenience, but it also becomes the breeding g...With the convergence of mobile communication network and Internet in depth, mobile Internet is penetrating into every field of people＇s life. Smart phone bring us great convenience, but it also becomes the breeding ground for the spread of malicious codes. In this paper, we propose a trust transfer algorithm based on the ant colony optimization algorithm to calculate the trust degree between any two nodes in the social network. Afterwards, a defense model based on social computing is presented for mobile phone malware. The simulation results show that our trust transfer algorithm improves the computation accuracy of indirect trust value by 14.65% compared with the TidalTrust algorithm, and the patch transmission speed of our model is faster than that of others.展开更多
Social computing and online groups have accompanied in a new age of the network, where information, networking and communication technologies are enabling systematized human efforts in primarily innovative ways. The s...Social computing and online groups have accompanied in a new age of the network, where information, networking and communication technologies are enabling systematized human efforts in primarily innovative ways. The social network communities working on various social network domains face different hurdles, including various new research studies and challenges in social computing. The researcher should try to expand the scope and establish new ideas and methods even from other disciplines to address the various challenges. This idea has diverse academic association, social links and technical characteristics. Thus it offers an ultimate opportunity for researchers to find out the issues in social computing and provide innovative solutions for conveying the information between social online groups on network computing. In this research paper we investigate the different issues in social media like users’ privacy and security, network reliabilities, and desire data availability on these social media, users’ awareness about the social networks and problems faced by academic domains. A huge number of users operated the social networks for retrieving and disseminating their real time and offline information to various places. The information may be transmitted on local networks or may be on global networks. The main concerns of users on social media are secure and fast communication channels. Facebook and YouTube both claimed for efficient security mechanism and fast communication channels for multimedia data. In this research a survey has been conducted in the most populated cities where a large number of Facebook and YouTube users have been found. During the survey several regular users indicate the certain potential issues continuously occurred on these social web sites interfaces, for example unwanted advertisement, fake IDS, uncensored videos and unknown friend request which cause the poor speed of channel communication, poor uploading and downloading data speed, channel interferences, security of data, privacy of users, integrity and reliability of user communication on these social sites. The major issues faced by active users of Facebook and YouTube have been highlighted in this research.展开更多
Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems(CPESs),but such systems have also become popular targets of cyberattacks.Currently,...Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems(CPESs),but such systems have also become popular targets of cyberattacks.Currently,available methods for evaluating the impacts of cyberattacks suffer from limited resilience,efficacy,and practical value.To mitigate their potentially disastrous consequences,this study suggests a two-stage,discrepancy-based optimization approach that considers both preparatory actions and response measures,integrating concepts from social computing.The proposed Kullback-Leibler divergence-based,distributionally robust optimization(KDR)method has a hierarchical,two-stage objective function that incorporates the operating costs of both system infrastructures(e.g.,energy resources,reserve capacity)and real-time response measures(e.g.,load shedding,demand-side management,electric vehicle charging station management).By incorporating social computing principles,the optimization framework can also capture the social behavior and interactions of energy consumers in response to cyberattacks.The preparatory stage entails day-ahead operational decisions,leveraging insights from social computing to model and predict the behaviors of individuals and communities affected by potential cyberattacks.The mitigation stage generates responses designed to contain the consequences of the attack by directing and optimizing energy use from the demand side,taking into account the social context and preferences of energy consumers,to ensure resilient,economically efficient CPES operations.Our method can determine optimal schemes in both stages,accounting for the social dimensions of the problem.An original disaster mitigation model uses an abstract formulation to develop a risk-neutral model that characterizes cyberattacks through KDR,incorporating social computing techniques to enhance the understanding and response to cyber threats.This approach can mitigate the impacts more effectively than several existing methods,even with limited data availability.To extend this risk-neutral model,we incorporate conditional value at risk as an essential risk measure,capturing the uncertainty and diverse impact scenarios arising from social computing factors.The empirical results affirm that the KDR method,which is enriched with social computing considerations,produces resilient,economically efficient solutions for managing the impacts of cyberattacks on a CPES.By integrating social computing principles into the optimization framework,it becomes possible to better anticipate and address the social and behavioral aspects associated with cyberattacks on CPESs,ultimately improving the overall resilience and effectiveness of the system’s response measures.展开更多
The field of social computing emerged more than ten years ago. During the last decade, researchers from a vari- ety of disciplines have been closely collaborating to boost the growth of social computing research. This...The field of social computing emerged more than ten years ago. During the last decade, researchers from a vari- ety of disciplines have been closely collaborating to boost the growth of social computing research. This paper aims at iden- tifying key researchers and institutions, and examining the collaboration patterns in the field. We employ co-authorship network analysis at different levels to study the bibliographic information of 6 543 publications in social computing from 1998 to 2011. This paper gives a snapshot of the current re- search in social computing and can provide an initial guid- ance to new researchers in social computing.展开更多
Social media analytics have played an important role in disaster identification.Recent advances in deep learning(DL)technologies have been applied to design disaster classification models.However,the DL-based models a...Social media analytics have played an important role in disaster identification.Recent advances in deep learning(DL)technologies have been applied to design disaster classification models.However,the DL-based models are hindered by insufficient training samples,because data collection and labeling are very expensive and time-consuming.To solve this issue,a privacy-preserving federated transfer learning approach for disaster classification(FedTL)is proposed,which can allow distributed social computing nodes to collaboratively train a comprehensive model.In the FedTL,Paillier homomorphic encryption method is used to protect the social computing nodes’data privacy.In particular,the transfer learning technology is adopted as a novel application to reduce the computation and communication costs in the federated learning system.The FedTL is verified by a real disaster image dataset collected from social networks.Theoretical analyses and experiment results show that the FedTL is effective,secure,efficient.In addition,the FedTL is highly extensible and can be easily applied in other transfer learning models.展开更多
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,he...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.展开更多
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 th...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.展开更多
Over the past few years,many efforts have been dedicated to studying cyberbullying in social edge computing devices,and most of them focus on three roles:victims,perpetrators,and bystanders.If we want to obtain a deep...Over the past few years,many efforts have been dedicated to studying cyberbullying in social edge computing devices,and most of them focus on three roles:victims,perpetrators,and bystanders.If we want to obtain a deep insight into the formation,evolution,and intervention of cyberbullying in devices at the edge of the Internet,it is necessary to explore more fine-grained roles.This paper presents a multi-level method for role feature modeling and proposes a differential evolution-assisted K-means(DEK)method to identify diverse roles.Our work aims to provide a role identification scheme for cyberbullying scenarios for social edge computing environments to alleviate the general safety issues that cyberbullying brings.The experiments on ten real-world datasets obtained from Weibo and five public datasets show that the proposed DEK outperforms the existing approaches on the method level.After clustering,we obtain nine roles and analyze the characteristics of each role and their evolution trends under different cyberbullying scenarios.Our work in this paper can be placed in devices at the edge of the Internet,leading to better real-time identification performance and adapting to the broad geographic location and high mobility of mobile devices.展开更多
The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade do...The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade documentation,customs clearance,and regulatory compliance.However,traditional SWS implementations face challenges such as data fragmentation,inefficient processing,and limited real-time intelligence.This study proposes a computational social science framework that integrates artificial intelligence(AI),machine learning,network analytics,and blockchain to optimize SWS operations.By employing predictive modeling,agentbased simulations,and algorithmic governance,this research demonstrates how computational methodologies improve trade efficiency,enhance regulatory compliance,and reduce transaction costs.Empirical case studies on AI-driven customs clearance,blockchain-enabled trade transparency,and network-based trade policy simulation illustrate the practical applications of these techniques.The study concludes that interdisciplinary collaboration and algorithmic governance are essential for advancing digital trade facilitation,ensuring resilience,transparency,and adaptability in global trade ecosystems.展开更多
Objective:To propose and test a new approach based on community detection in the field of social computing for uncovering consensuses and treatment principles in traditional Chinese medicine(TCM).Methods:Three Chinese...Objective:To propose and test a new approach based on community detection in the field of social computing for uncovering consensuses and treatment principles in traditional Chinese medicine(TCM).Methods:Three Chinese databases(CNKI,VIP,andWan Fang Data)were searched for published articles on TCM treatment of diabetic nephropathy(DN)from their inception until September 31,2014.Zheng classification and herbdatawereextractedfromincluded articlesand usedto construct a Zheng classification and treatment of diabetic nephropathy(DNZCT)network with nodes denoting Zhengs and herbs and edges denoting corresponding treating relationshipsamong them.Community detection was applied to the DNZCT and detected community structures were analyzed.Results:A network of 201 nodes and 743 edges were constructed and six communities were detected.Nodes clustered in the samecommunity captured the samesemantic topic;different communities had unique characteristics,and indicated different treatment principles.Large communities usually represented similar points of view or consensuses on common Zheng diagnoses and herb prescriptions;small communities might help to indicate unusual Zhengs and herbs.Conclusion:The results suggest that the community detection-based approach is useful and feasible for uncovering consensuses and treatment principles of DN treatment in TCM,and could be used to address other similar problems in TCM.展开更多
How can we foster and grow artificial societies so as to cause social properties to emerge that are logical, consistent with real societies, and are expected by design- ers？ We propose a framework for fostering artif...How can we foster and grow artificial societies so as to cause social properties to emerge that are logical, consistent with real societies, and are expected by design- ers？ We propose a framework for fostering artificial soci- eties using social learning mechanisms and social control ap- proaches. We present the application of fostering artificial so- cieties in parallel emergency management systems. Then we discuss social learning mechanisms in artificial societies, in- cluding observational learning, reinforcement learning, imi- tation learning, and advice-based learning. Furthermore, we discuss social control approaches, including social norms, social policies, social reputations, social commitments, and sanctions.展开更多
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 ...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.展开更多
Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/metho...Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/methodology/approach:Using publication and affiliation data from OpenAlex,we identified 2,896 scientists who relocated between cities in China from 2014 to 2017.We applied propensity score matching(PSM)to compare their academic outcomes post-mobility with a matched group of non-mobile peers.Multiple performance metrics were examined,including publication count,citation impact,number of collaborators,and university prestige.Ordered logistic regression was used to analyze factors influencing moves to higher-level institutions.Findings:Mobility enhances collaboration by increasing the number of coauthors but is associated with a short-term decline in citation impact.Scientists were more likely to move to lower-prestige universities.However,prior collaboration breadth and citation count positively predicted transitions to more prestigious institutions,while the number of publications did not.Research limitations:This study focuses on intra-national mobility within China from 2014 to 2017 and relies on quantitative data,lacking personal or qualitative variables such as gender,discipline-specific norms,or institutional culture.Data coverage for Chinese-language publications may also be limited.Practical implications:This research provides insights into academic hiring patterns and the trade-offs involved in scientist mobility.It offers valuable guidance for institutions aiming to enhance faculty recruitment and retention,as well as for researchers considering career transitions.Originality/value:This is a quantitative analysis of domestic scientist mobility in China using matched comparison and multi-dimensional academic indicators.The integration of university prestige metrics(Double First-Class and citation-based rankings)offers a nuanced view of career dynamics within the Chinese higher education system.展开更多
Due to the pandemic’s widespread effects on daily life in the past few years,evaluating and analyzing the impact of COVID-19 on large cities in China accurately and timely has become increasingly significant.The exis...Due to the pandemic’s widespread effects on daily life in the past few years,evaluating and analyzing the impact of COVID-19 on large cities in China accurately and timely has become increasingly significant.The existing research lacks studies on the electricity consumption of residents and non-residents during the pandemic.So based on the four-year electricity consumption data of a large city in China,this paper examines COVID-19’s impact from the perspective of residential and nonresidential electricity consumption through the COVID-19-Power framework and its extended version that predicts electricity consumption in 2020 without the outbreak of COVID-19 for comparison with the actual values.For residential electricity consumption,through comparative analysis of different administrative districts on geographic location and historical electricity consumption habits,the impact of the pandemic on residential electricity consumption is found to be relatively small.For nonresidential electricity consumption,we infer that power-related factors including real estate development and investment,etc.contribute to perception of the special impacts of the pandemic.We conduct predictive analysis of non-residential electricity consumption for different administrative districts by geographic location,different electricity consumption categories.We verify the significant negative impact of the pandemic on the real estate sector by the analysis of electricity consumption across different sectors.Through these research results,details about the impact of the pandemic on people’s lives and electrical load in China’s large cities can be captured by power enterprises to adjust power supply strategies more effectively and pertinently.展开更多
Conducting thematic analysis in qualitative research can be laborious and time-consuming.We propose and evaluate the feasibility of using Generative Pre-trained Transformer(GPT)models to assist public health researche...Conducting thematic analysis in qualitative research can be laborious and time-consuming.We propose and evaluate the feasibility of using Generative Pre-trained Transformer(GPT)models to assist public health researchers in extracting themes from interview transcripts.Carefully engineered prompts were used to sequentially extract and synthesize transcripts into a concise set of study-level themes relevant to the study’s goals.An evaluation using a 5-point Likert scale(0−4)assessed GPTgenerated themes across 11 published studies based on four criteria:succinctness,alignment with researcher-identified themes,quality of explanations,and relevance of quotes.Across all four criteria,the scores averaged 3.05(95%Confidence Interval(CI):[2.93,3.16]).Our findings indicate that at least half of the GPT-generated themes align with those in published studies,exhibiting succinctness with minimal repetition,substantial depth of explanations,and relevant quotations.Despite these promising results,practices such as complementing outputs with field-specific knowledge are recommended.展开更多
Code review is intended to find bugs in early development phases,improving code quality for later integration and testing.However,due to the lack of experience with algorithm design,or software development,individual ...Code review is intended to find bugs in early development phases,improving code quality for later integration and testing.However,due to the lack of experience with algorithm design,or software development,individual novice programmers face challenges while reviewing code.In this paper,we utilize collaborative eye tracking to record the gaze data from multiple reviewers,and share the gaze visualization among them during the code review process.The visualizations,such as borders highlighting current reviewed code lines,transition lines connecting related reviewed code lines,reveal the visual attention about program functions that can facilitate understanding and bug tracing.This can help novice reviewers to make sense to confirm the potential bugs or avoid repeated reviewing of code,and potentially even help to improve reviewing skills.We built a prototype system,and conducted a user study with paired reviewers.The results showed that the shared real-time visualization allowed the reviewers to find bugs more efficiently.展开更多
The COVID-19 pandemic has severely harmed every aspect of our daily lives,resulting in a slew of social problems.Therefore,it is critical to accurately assess the current state of community functionality and resilienc...The COVID-19 pandemic has severely harmed every aspect of our daily lives,resulting in a slew of social problems.Therefore,it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery.To this end,various types of social sensing tools,such as tweeting and publicly released news,have been employed to understand individuals’and communities’thoughts,behaviors,and attitudes during the COVID-19 pandemic.However,some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19.This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience.We use fact-checking organizations to classify news as real,mixed,or fake,and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience(CR).Based on the news articles and tweets collected,we quantify CR based on two key factors,community wellbeing and resource distribution,where resource distribution is assessed by the level of economic resilience and community capital.Based on the estimates of these two factors,we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets.To improve the operationalization and sociological significance of this work,we use dimension reduction techniques to integrate the dimensions.展开更多
The co-evolution of social relationships and individual behavior in time and space has important implications, but is poorly understood because of the difficulty of closely tracking the everyday life of a com- plete c...The co-evolution of social relationships and individual behavior in time and space has important implications, but is poorly understood because of the difficulty of closely tracking the everyday life of a com- plete community. We offer evidence that relationships and behavior co-evolve in a student dormitory, based on monthly surveys and location tracking through resident cellular phones over a period of nine months. We demonstrate that a Markov jump process could capture the co-evolution in terms of the rates at which resi- dents visit places and friends. Our co-evolution model will be useful in bridging sensor networks data and organizational dynamics theories, simulating different ways to shape behavior and relationships, and turning mobile phone data into data products.展开更多
Artificial Intelligence is revolutionising our communication practices and the ways in which we interact with each other. This revolution does not only impact how we communicate, but it affects the nature of the partn...Artificial Intelligence is revolutionising our communication practices and the ways in which we interact with each other. This revolution does not only impact how we communicate, but it affects the nature of the partners with whom we communicate. Online discussion platforms now allow humans to communicate with artificial agents in the form of socialbots. Such agents have the potential to moderate online discussions and even manipulate and alter public opinions. In this paper, we propose to study this phenomenon using a constructed large-scale agent platform. At the heart of the platform lies an artificial agent that can moderate online discussions using argumentative messages. We investigate the influence of the agent on the evolution of an online debate involving human participants. The agent will dynamically react to their messages by moderating, supporting, or attacking their stances. We conducted two experiments to evaluate the platform while looking at the effects of the conversational agent. The first experiment is a large-scale discussion with 1076 citizens from Afghanistan discussing urban policy-making in the city of Kabul. The goal of the experiment was to increase the citizen involvement in implementing Sustainable Development Goals. The second experiment is a small-scale debate between a group of 16 students about globalisation and taxation in Myanmar. In the first experiment, we found that the agent improved the responsiveness of the participants and increased the number of identified ideas and issues. In the second experiment, we found that the agent polarised the debate by reinforcing the initial stances of the participant.展开更多
文摘Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.
基金Supported by the National Natural Science Foundation of China(91438117)the Funding of Shanghai Key Laboratory of Financial Information Technology(2015)
文摘With the convergence of mobile communication network and Internet in depth, mobile Internet is penetrating into every field of people＇s life. Smart phone bring us great convenience, but it also becomes the breeding ground for the spread of malicious codes. In this paper, we propose a trust transfer algorithm based on the ant colony optimization algorithm to calculate the trust degree between any two nodes in the social network. Afterwards, a defense model based on social computing is presented for mobile phone malware. The simulation results show that our trust transfer algorithm improves the computation accuracy of indirect trust value by 14.65% compared with the TidalTrust algorithm, and the patch transmission speed of our model is faster than that of others.
文摘Social computing and online groups have accompanied in a new age of the network, where information, networking and communication technologies are enabling systematized human efforts in primarily innovative ways. The social network communities working on various social network domains face different hurdles, including various new research studies and challenges in social computing. The researcher should try to expand the scope and establish new ideas and methods even from other disciplines to address the various challenges. This idea has diverse academic association, social links and technical characteristics. Thus it offers an ultimate opportunity for researchers to find out the issues in social computing and provide innovative solutions for conveying the information between social online groups on network computing. In this research paper we investigate the different issues in social media like users’ privacy and security, network reliabilities, and desire data availability on these social media, users’ awareness about the social networks and problems faced by academic domains. A huge number of users operated the social networks for retrieving and disseminating their real time and offline information to various places. The information may be transmitted on local networks or may be on global networks. The main concerns of users on social media are secure and fast communication channels. Facebook and YouTube both claimed for efficient security mechanism and fast communication channels for multimedia data. In this research a survey has been conducted in the most populated cities where a large number of Facebook and YouTube users have been found. During the survey several regular users indicate the certain potential issues continuously occurred on these social web sites interfaces, for example unwanted advertisement, fake IDS, uncensored videos and unknown friend request which cause the poor speed of channel communication, poor uploading and downloading data speed, channel interferences, security of data, privacy of users, integrity and reliability of user communication on these social sites. The major issues faced by active users of Facebook and YouTube have been highlighted in this research.
基金supported in part by the New Generation Artificial Intelligence Development Plan of China(2015–2030)(Grants No.2021ZD0111205)the National Natural Science Foundation of China(Grants No.72025404,No.71621002,No.71974187)+1 种基金Beijing Natural Science Foundation(L192012)Beijing Nova Program(Z201100006820085).
文摘Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems(CPESs),but such systems have also become popular targets of cyberattacks.Currently,available methods for evaluating the impacts of cyberattacks suffer from limited resilience,efficacy,and practical value.To mitigate their potentially disastrous consequences,this study suggests a two-stage,discrepancy-based optimization approach that considers both preparatory actions and response measures,integrating concepts from social computing.The proposed Kullback-Leibler divergence-based,distributionally robust optimization(KDR)method has a hierarchical,two-stage objective function that incorporates the operating costs of both system infrastructures(e.g.,energy resources,reserve capacity)and real-time response measures(e.g.,load shedding,demand-side management,electric vehicle charging station management).By incorporating social computing principles,the optimization framework can also capture the social behavior and interactions of energy consumers in response to cyberattacks.The preparatory stage entails day-ahead operational decisions,leveraging insights from social computing to model and predict the behaviors of individuals and communities affected by potential cyberattacks.The mitigation stage generates responses designed to contain the consequences of the attack by directing and optimizing energy use from the demand side,taking into account the social context and preferences of energy consumers,to ensure resilient,economically efficient CPES operations.Our method can determine optimal schemes in both stages,accounting for the social dimensions of the problem.An original disaster mitigation model uses an abstract formulation to develop a risk-neutral model that characterizes cyberattacks through KDR,incorporating social computing techniques to enhance the understanding and response to cyber threats.This approach can mitigate the impacts more effectively than several existing methods,even with limited data availability.To extend this risk-neutral model,we incorporate conditional value at risk as an essential risk measure,capturing the uncertainty and diverse impact scenarios arising from social computing factors.The empirical results affirm that the KDR method,which is enriched with social computing considerations,produces resilient,economically efficient solutions for managing the impacts of cyberattacks on a CPES.By integrating social computing principles into the optimization framework,it becomes possible to better anticipate and address the social and behavioral aspects associated with cyberattacks on CPESs,ultimately improving the overall resilience and effectiveness of the system’s response measures.
文摘The field of social computing emerged more than ten years ago. During the last decade, researchers from a vari- ety of disciplines have been closely collaborating to boost the growth of social computing research. This paper aims at iden- tifying key researchers and institutions, and examining the collaboration patterns in the field. We employ co-authorship network analysis at different levels to study the bibliographic information of 6 543 publications in social computing from 1998 to 2011. This paper gives a snapshot of the current re- search in social computing and can provide an initial guid- ance to new researchers in social computing.
基金The authors gratefully acknowledge the financial support provided by National Science and Technology Major Project of China(No.2018YFB0204304)National Natural Science Foundation of China(No.51909200)Tianjin Research Innovation Project for Postgraduate Stu-dents(No.2019YJSB067).
文摘Social media analytics have played an important role in disaster identification.Recent advances in deep learning(DL)technologies have been applied to design disaster classification models.However,the DL-based models are hindered by insufficient training samples,because data collection and labeling are very expensive and time-consuming.To solve this issue,a privacy-preserving federated transfer learning approach for disaster classification(FedTL)is proposed,which can allow distributed social computing nodes to collaboratively train a comprehensive model.In the FedTL,Paillier homomorphic encryption method is used to protect the social computing nodes’data privacy.In particular,the transfer learning technology is adopted as a novel application to reduce the computation and communication costs in the federated learning system.The FedTL is verified by a real disaster image dataset collected from social networks.Theoretical analyses and experiment results show that the FedTL is effective,secure,efficient.In addition,the FedTL is highly extensible and can be easily applied in other transfer learning models.
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.
基金supported by the National Natural Science Foundation of China(Nos.71731004,62072115,62102094,62173095,and 61602122)Shanghai Science and Technology Innovation Action Plan Project(No.22510713600)Natural Science Foundation of Shanghai(No.21ZR1404700).
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.
基金supported by the National Key Research and Development Program of China(No.2021YFC3300202)faculty of Shanghai Key Laboratory of Data Science,Fudan Institute on Aging,MOE Laboratory for National Development and Intelligent GovernanceShanghai Institute of Intelligent Electronics&Systems,Fudan University.
文摘Over the past few years,many efforts have been dedicated to studying cyberbullying in social edge computing devices,and most of them focus on three roles:victims,perpetrators,and bystanders.If we want to obtain a deep insight into the formation,evolution,and intervention of cyberbullying in devices at the edge of the Internet,it is necessary to explore more fine-grained roles.This paper presents a multi-level method for role feature modeling and proposes a differential evolution-assisted K-means(DEK)method to identify diverse roles.Our work aims to provide a role identification scheme for cyberbullying scenarios for social edge computing environments to alleviate the general safety issues that cyberbullying brings.The experiments on ten real-world datasets obtained from Weibo and five public datasets show that the proposed DEK outperforms the existing approaches on the method level.After clustering,we obtain nine roles and analyze the characteristics of each role and their evolution trends under different cyberbullying scenarios.Our work in this paper can be placed in devices at the edge of the Internet,leading to better real-time identification performance and adapting to the broad geographic location and high mobility of mobile devices.
文摘The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade documentation,customs clearance,and regulatory compliance.However,traditional SWS implementations face challenges such as data fragmentation,inefficient processing,and limited real-time intelligence.This study proposes a computational social science framework that integrates artificial intelligence(AI),machine learning,network analytics,and blockchain to optimize SWS operations.By employing predictive modeling,agentbased simulations,and algorithmic governance,this research demonstrates how computational methodologies improve trade efficiency,enhance regulatory compliance,and reduce transaction costs.Empirical case studies on AI-driven customs clearance,blockchain-enabled trade transparency,and network-based trade policy simulation illustrate the practical applications of these techniques.The study concludes that interdisciplinary collaboration and algorithmic governance are essential for advancing digital trade facilitation,ensuring resilience,transparency,and adaptability in global trade ecosystems.
基金the National Natural Science Foundation of China Nos.81273876 and 81473800Beijing University of Chinese Medicine Founding for doctoral candidate No.2014-JYBZZ-XS-003.
文摘Objective:To propose and test a new approach based on community detection in the field of social computing for uncovering consensuses and treatment principles in traditional Chinese medicine(TCM).Methods:Three Chinese databases(CNKI,VIP,andWan Fang Data)were searched for published articles on TCM treatment of diabetic nephropathy(DN)from their inception until September 31,2014.Zheng classification and herbdatawereextractedfromincluded articlesand usedto construct a Zheng classification and treatment of diabetic nephropathy(DNZCT)network with nodes denoting Zhengs and herbs and edges denoting corresponding treating relationshipsamong them.Community detection was applied to the DNZCT and detected community structures were analyzed.Results:A network of 201 nodes and 743 edges were constructed and six communities were detected.Nodes clustered in the samecommunity captured the samesemantic topic;different communities had unique characteristics,and indicated different treatment principles.Large communities usually represented similar points of view or consensuses on common Zheng diagnoses and herb prescriptions;small communities might help to indicate unusual Zhengs and herbs.Conclusion:The results suggest that the community detection-based approach is useful and feasible for uncovering consensuses and treatment principles of DN treatment in TCM,and could be used to address other similar problems in TCM.
文摘How can we foster and grow artificial societies so as to cause social properties to emerge that are logical, consistent with real societies, and are expected by design- ers？ We propose a framework for fostering artificial soci- eties using social learning mechanisms and social control ap- proaches. We present the application of fostering artificial so- cieties in parallel emergency management systems. Then we discuss social learning mechanisms in artificial societies, in- cluding observational learning, reinforcement learning, imi- tation learning, and advice-based learning. Furthermore, we discuss social control approaches, including social norms, social policies, social reputations, social commitments, and sanctions.
基金This work was supported by the National Key Research and Development Program of China(No.2020AAA0106501)the National Natural Science Foundation of China(No.62002029)Beijing Academy of Artificial Intelligence(BAAI).
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.
基金supported by grants from Shenzhen Polytechnic University Research(Fund No.6025310042 K)the National Natural Science Foundation of China(No.NSFC62006109 and NSFC12031005).
文摘Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/methodology/approach:Using publication and affiliation data from OpenAlex,we identified 2,896 scientists who relocated between cities in China from 2014 to 2017.We applied propensity score matching(PSM)to compare their academic outcomes post-mobility with a matched group of non-mobile peers.Multiple performance metrics were examined,including publication count,citation impact,number of collaborators,and university prestige.Ordered logistic regression was used to analyze factors influencing moves to higher-level institutions.Findings:Mobility enhances collaboration by increasing the number of coauthors but is associated with a short-term decline in citation impact.Scientists were more likely to move to lower-prestige universities.However,prior collaboration breadth and citation count positively predicted transitions to more prestigious institutions,while the number of publications did not.Research limitations:This study focuses on intra-national mobility within China from 2014 to 2017 and relies on quantitative data,lacking personal or qualitative variables such as gender,discipline-specific norms,or institutional culture.Data coverage for Chinese-language publications may also be limited.Practical implications:This research provides insights into academic hiring patterns and the trade-offs involved in scientist mobility.It offers valuable guidance for institutions aiming to enhance faculty recruitment and retention,as well as for researchers considering career transitions.Originality/value:This is a quantitative analysis of domestic scientist mobility in China using matched comparison and multi-dimensional academic indicators.The integration of university prestige metrics(Double First-Class and citation-based rankings)offers a nuanced view of career dynamics within the Chinese higher education system.
文摘Due to the pandemic’s widespread effects on daily life in the past few years,evaluating and analyzing the impact of COVID-19 on large cities in China accurately and timely has become increasingly significant.The existing research lacks studies on the electricity consumption of residents and non-residents during the pandemic.So based on the four-year electricity consumption data of a large city in China,this paper examines COVID-19’s impact from the perspective of residential and nonresidential electricity consumption through the COVID-19-Power framework and its extended version that predicts electricity consumption in 2020 without the outbreak of COVID-19 for comparison with the actual values.For residential electricity consumption,through comparative analysis of different administrative districts on geographic location and historical electricity consumption habits,the impact of the pandemic on residential electricity consumption is found to be relatively small.For nonresidential electricity consumption,we infer that power-related factors including real estate development and investment,etc.contribute to perception of the special impacts of the pandemic.We conduct predictive analysis of non-residential electricity consumption for different administrative districts by geographic location,different electricity consumption categories.We verify the significant negative impact of the pandemic on the real estate sector by the analysis of electricity consumption across different sectors.Through these research results,details about the impact of the pandemic on people’s lives and electrical load in China’s large cities can be captured by power enterprises to adjust power supply strategies more effectively and pertinently.
基金supported in part by the Washington University Center for Diabetes Translation Research(WU-CDTR)under Grant Number P30DK092950 from the National Institute of Diabetes and Digestive and Kidney Diseases(NIDDK)。
文摘Conducting thematic analysis in qualitative research can be laborious and time-consuming.We propose and evaluate the feasibility of using Generative Pre-trained Transformer(GPT)models to assist public health researchers in extracting themes from interview transcripts.Carefully engineered prompts were used to sequentially extract and synthesize transcripts into a concise set of study-level themes relevant to the study’s goals.An evaluation using a 5-point Likert scale(0−4)assessed GPTgenerated themes across 11 published studies based on four criteria:succinctness,alignment with researcher-identified themes,quality of explanations,and relevance of quotes.Across all four criteria,the scores averaged 3.05(95%Confidence Interval(CI):[2.93,3.16]).Our findings indicate that at least half of the GPT-generated themes align with those in published studies,exhibiting succinctness with minimal repetition,substantial depth of explanations,and relevant quotations.Despite these promising results,practices such as complementing outputs with field-specific knowledge are recommended.
基金We also gratefully acknowledge the grant from National Natural Science Foundation of China(Grant Nos.61772468,62172368)National Key Research&Development Program of China(2016YFB1001403)Fundamental Research Funds for the Provincial Universities of Zhejiang(RF-B2019001).
文摘Code review is intended to find bugs in early development phases,improving code quality for later integration and testing.However,due to the lack of experience with algorithm design,or software development,individual novice programmers face challenges while reviewing code.In this paper,we utilize collaborative eye tracking to record the gaze data from multiple reviewers,and share the gaze visualization among them during the code review process.The visualizations,such as borders highlighting current reviewed code lines,transition lines connecting related reviewed code lines,reveal the visual attention about program functions that can facilitate understanding and bug tracing.This can help novice reviewers to make sense to confirm the potential bugs or avoid repeated reviewing of code,and potentially even help to improve reviewing skills.We built a prototype system,and conducted a user study with paired reviewers.The results showed that the shared real-time visualization allowed the reviewers to find bugs more efficiently.
文摘The COVID-19 pandemic has severely harmed every aspect of our daily lives,resulting in a slew of social problems.Therefore,it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery.To this end,various types of social sensing tools,such as tweeting and publicly released news,have been employed to understand individuals’and communities’thoughts,behaviors,and attitudes during the COVID-19 pandemic.However,some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19.This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience.We use fact-checking organizations to classify news as real,mixed,or fake,and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience(CR).Based on the news articles and tweets collected,we quantify CR based on two key factors,community wellbeing and resource distribution,where resource distribution is assessed by the level of economic resilience and community capital.Based on the estimates of these two factors,we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets.To improve the operationalization and sociological significance of this work,we use dimension reduction techniques to integrate the dimensions.
基金Supported by the Army Research Laboratory of USA (No.W911NF-09-2-0053)Air Force Office of Scientific Research of USA (No.FA9550-10-1-0122)Bruno Lepri's research is funded by PERSI project inside the Marie Curie Cofund 7th Framework
文摘The co-evolution of social relationships and individual behavior in time and space has important implications, but is poorly understood because of the difficulty of closely tracking the everyday life of a com- plete community. We offer evidence that relationships and behavior co-evolve in a student dormitory, based on monthly surveys and location tracking through resident cellular phones over a period of nine months. We demonstrate that a Markov jump process could capture the co-evolution in terms of the rates at which resi- dents visit places and friends. Our co-evolution model will be useful in bridging sensor networks data and organizational dynamics theories, simulating different ways to shape behavior and relationships, and turning mobile phone data into data products.
基金This work was supported by JST CREST Grant Number JPMJCR15E1,Japan.
文摘Artificial Intelligence is revolutionising our communication practices and the ways in which we interact with each other. This revolution does not only impact how we communicate, but it affects the nature of the partners with whom we communicate. Online discussion platforms now allow humans to communicate with artificial agents in the form of socialbots. Such agents have the potential to moderate online discussions and even manipulate and alter public opinions. In this paper, we propose to study this phenomenon using a constructed large-scale agent platform. At the heart of the platform lies an artificial agent that can moderate online discussions using argumentative messages. We investigate the influence of the agent on the evolution of an online debate involving human participants. The agent will dynamically react to their messages by moderating, supporting, or attacking their stances. We conducted two experiments to evaluate the platform while looking at the effects of the conversational agent. The first experiment is a large-scale discussion with 1076 citizens from Afghanistan discussing urban policy-making in the city of Kabul. The goal of the experiment was to increase the citizen involvement in implementing Sustainable Development Goals. The second experiment is a small-scale debate between a group of 16 students about globalisation and taxation in Myanmar. In the first experiment, we found that the agent improved the responsiveness of the participants and increased the number of identified ideas and issues. In the second experiment, we found that the agent polarised the debate by reinforcing the initial stances of the participant.
