ACP-based social computing and parallel intelligence： Societies 5.0 and beyond

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.

Statistical Analysis of Network Based Issues and Their Impact on Social Computing Practices in Pakistan

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.

A social computing method for energy safety

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.

On social computing research collaboration patterns： a social network perspective

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.

Federated transfer learning for disaster classification in social computing networks

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.

Social Computing Unhinged

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.

Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis

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.

A Defense Model against Mobile Phone Malicious Codes Based on Social Computing

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.

Detection of consensuses and treatment principles of diabetic nephropathy in traditional Chinese medicine: A new approach

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.

Fostering artificial societies using social learning and social control in parallel emergency management systems

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.

From Symbols to Embeddings:A Tale of Two Representations in Computational Social Science

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.

Negative Sentiment Shift on a Chinese Movie-Rating Website

Shifting to negativity is more and more prevalent in online communities and may play a key role in group polarization.While current research indicates a close relationship between group polarization and negative sentiment,they often link negative sentiment shifts with echo chambers and misinformation within echo chambers.In this work,we explore the sentiment drift using over 4 million comments from a Chinese online movie-rating community that is less affected by misinformation than other mainstream online communities and has no echo chamber structures.We measure the sentiment shift of the community and users of different engagement levels.Our analysis reveals that while the community does not show a tendency toward negativity,users of higher engagement levels are generally more negative,considering factors like the different movies they consume.The results indicate a fitting-in process,suggesting the possible mechanism of group identity on sentiment shift on social media platforms.These findings also provide guidance on web design to tackle the negativity issue and expand sentiment shift analysis to non-English contexts.

Collaborative eye tracking based code review through real-time shared gaze visualization

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.

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

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.

Tracking Co-evolution of Behavior and Relationships with Mobile Phones

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.

Argumentative Conversational Agents for Online Discussions

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.

Pandemic Policymaking

This study leverages a high dimensional manifold learning design to explore the latent structure of the pandemic policymaking space only based on bill-level characteristics of pandemic-focused bills from 1973 to 2020.Results indicate the COVID-19 era of policymaking maps extremely closely onto prior periods of related policymaking.This suggests that there is striking uniformity in Congressional policymaking related to these types of large-scale crises over time,despite currently operating in a unique era of hyperpolarization,division,and ineffective governance.