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Online interpersonal harm, such as harassment and discrimination, is prevalent on social media platforms. Most platforms adopt content moderation as the primary solution, relying on measures like bans and content removal. These measures follow principles of punitive justice, which holds that perpetrators of harm should receive punishment in proportion to the offense. However, these strategies often fall short of addressing the needs of affected individuals — the survivors — who are typically excluded from decision-making and left with various unmet needs. My dissertation adopts a restorative justice lens and investigates ways to empower online harm survivors in addressing their unique needs. This approach emphasizes survivors' needs and agency, reconceptualizes views on perpetrator accountability, and mobilizes community resources for a collective response to the issue. Through interviews and co-design sessions with survivors, I identified key survivor needs such as sensemaking, emotional support, safety, retribution, and transformation. Building on these insights, I focused on survivors' needs for sensemaking and developed a social computing system to facilitate a structured sensemaking process, connecting survivors with available resources and stakeholders in addressing the harm. Furthermore, I applied a restorative justice lens to understand how survivors, perpetrators, and moderators currently navigate harm within existing moderation practices, examining both the opportunities and challenges of integrating restorative justice practices into the content moderation landscape. My research highlights the urgent need for social media platforms to incorporate justice and ethical values into their operational frameworks. It advocates for a survivor-centered approach to addressing online interpersonal harm, viewing it as a multi-stakeholder, cross-platform process. This approach calls for a shift towards viewing harm as a communal challenge and emphasizes cultivating a resilient community culture in the long term, rather than perceiving harm as isolated incidents within a perpetrator-centric framework.

People must perform bureaucratic, administrative work in daily life, such as applying for official documents, concluding contracts, organizing purchases, managing pension plans, etc. This work is time-consuming and unequally distributed in the household. At its best, it is perceived as boring; at its worst, it is mentally and emotionally stressful, leaving people overwhelmed and unable to fulfill their obligations. People can benefit from the digitalization of domestic bureaucracy automating repetitive tasks, reducing mental effort, and saving time for leisure activities. In recent years, there has been a need for more empirical knowledge about the use of technology or the working environment for this purpose. This paper presents insights from an online survey with 617 socio-demographically distributed participants highlighting the devices, tools, special software, and common places people favor for accomplishing these office-like household chores. Our results provide a solid empirical basis that not only quantifies previous qualitative results now using the German adult population but also offers orientation for further in-depth research as well as design. [ABSTRACT FROM AUTHOR]

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 non-residential 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 non-residential 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.

Community detection is a critical component of network analysis and a hot topic in social computing. Detecting community structure in dynamic networks has important theoretical and practical implications for understanding the intrinsic function of networks and predicting network behavior. However, the majority of existing dynamic community detection methods adopt shallow models, which have limited ability to excavate complex non-linear structures and tend to generate undesirable community structures. In order to obtain an accurate and robust community structure in dynamic networks, we are inspired by network representation learning and utilize the deep learning to detect evolving communities in dynamic networks. In this paper, we propose a novel dynamic community detection method by fusing Deep Learning and Evolutionary Clustering (DLEC). This work attempts to combine deep learning and evolutionary clustering into a unified framework. First, we propose a matrix construction strategy to fully reveal the inherent community structures via the underlying community memberships. Then, we develop a novel multi-layer deep autoencoder framework that consists of multiple non-linear functions to extract the latent deep representation of the dynamic network. Based on the evolutionary clustering framework, a graph regularization term is introduced to ensure the smoothness of the community evolution. Finally, we employ the K-means clustering algorithm on the low-dimensional network space to obtain the community structure. Extensive experimental results on synthetic and real-world networks show that the proposed DLEC algorithm can effectively detect high-quality communities in dynamic networks. [ABSTRACT FROM AUTHOR]

The proliferation of fake news on social media platforms necessitates the development of reliable datasets for effective fake news detection and veracity analysis. In this article, we introduce a veracity dataset of Arabic tweets called "VERA-ARAB", a pioneering large-scale dataset designed to enhance fake news detection in Arabic tweets. VERA-ARAB is a balanced, multi-domain, and multi-dialectal dataset, containing both fake and true news, meticulously verified by fact-checking experts from Misbar. Comprising approximately 20,000 tweets from 13,000 distinct users and covering 884 different claims, the dataset includes detailed information such as news text, user details, and spatiotemporal data, spanning diverse domains like sports and politics. We leveraged the X API to retrieve and structure the dataset, providing a comprehensive data dictionary to describe the raw data and conducting a thorough statistical descriptive analysis. This analysis reveals insightful patterns and distributions, visualized according to data type and nature. We also evaluated the dataset using multiple machine learning classification models, exploring various social and textual features. Our findings indicate promising results, particularly with textual features, underscoring the dataset's potential for enhancing fake news detection. Furthermore, we outline future work aimed at expanding VERA-ARAB to establish it as a benchmark for Arabic tweets in fake news detection. We also discuss other potential applications that could leverage the VERA-ARAB dataset, emphasizing its value and versatility for advancing the field of fake news detection in Arabic social media. Potential applications include user veracity assessment, topic modeling, and named entity recognition, demonstrating the dataset's wide-ranging utility for broader research in information quality management on social media. [ABSTRACT FROM AUTHOR]

We are in the early stages of a new era of demographic research that offers exciting opportunities to quantify demographic phenomena at a scale and resolution once unimaginable. These scientific possibilities are opened up by new sources of data, such as the digital traces that arise from ubiquitous social computing, massive longitudinal datasets produced by the digitization of historical records, and information about previously inaccessible populations reached through innovations in classic modes of data collection. In this commentary, we describe five promising new sources of demographic data and their potential appeal. We identify cross‐cutting challenges shared by these new data sources and argue that realizing their full potential will demand both innovative methodological developments and continued investment in high‐quality, traditional surveys and censuses. Despite these considerable challenges, the future is bright: these new sources of data will lead demographers to develop new theories and revisit and sharpen old ones. [ABSTRACT FROM AUTHOR]

As virtual teamwork became widespread, the importance of the usability of groupware has increased, as groupware must have high usability in order to properly support teamwork. Several studies have focused on the usability evaluation of groupware, yet, both in terms of science and practice, there is a lack of a method that, like existing empirical single-user methods, can be routinely applied in software development practice. The new Team Usability Testing method differs from previous methods in that it is an empirical method for evaluating synchronous (real-time) collaborative software that explores the team usability problems with the help of real or potential users. The method consists of questionnaires, screen recording videos and group interviews, and the framework of data processing is based on the theory of the mechanics of collaboration. In the four steps of the development of the method, empirical studies under laboratory conditions and real working conditions, and then, for validation purposes, analytical, heuristic evaluation with the involvement of usability experts were performed. During the development and the application of the method, the communication patterns of different teams were examined in terms of their relationship to software usability. In this paper we present the results of the second laboratory study and the heuristic evaluation in details. Overall, the method is able to explore team usability problems of different types of teams, making it a valuable element in existing methods for evaluating the usability of groupware. [ABSTRACT FROM AUTHOR]

Web annotation environments are widely used in education based on the premise that student interaction in these environments benefits individual and group learning. However, there is little research on factors driving student interaction in web annotation activities. In this study we asked: What dynamics could explain social interaction among students in web annotation activities in college classrooms? Recognizing the mediated nature of social interaction in web annotation, we hypothesized that student interaction is driven by multiple factors including previous relational events and semantic features of annotation content. Following a novel network analysis method named relational event modeling, we analyzed a rich dataset from four online classes. Results indicated annotation popularity was initially predictive of student replies, meaning popular annotation threads were more likely to attract new replies. However, this effect nearly diminished when adding thread-level semantic cohesion to the model, indicating an significant role played by semantic cohesion in attracting new responses. This paper makes important progress towards modeling social interaction in digital environments as a dynamic, mediated phenomenon. This study contributes empirical insights into web annotation and calls for future work to investigate social interaction as a dynamic network phenomenon. [ABSTRACT FROM AUTHOR]

The advent of big data marks a profound shift in our epistemological framework, introducing a new knowledge paradigm where the social landscape is shaped by data processing, perceived as both comprehensive and natural. This transformative shift challenges traditional notions of human agency in societal understanding, positioning empirical quantification at the forefront of inquiry. Beyond philosophical implications, pragmatic challenges abound in big data research--from issues of commensuration and the influence of action grammars to the dominance of correlational over causal relationships, the prevalence of everyday data over historical archives, and the pervasive impact of algorithms on data ecosystems. This manuscript undertakes a comprehensive exploration of these challenges, proposing strategies for navigating them within emerging disciplines such as Digital Humanities, Social Computing, and Cultural Analysis. Methodologically anchored in constructivist principles and critical discourse analysis (CDA), the study investigates how socio-cultural contexts shape data and knowledge production. Drawing on extensive literature and meta-analyses, it synthesizes diverse perspectives to underscore the necessity for methodological innovation and reflexivity in addressing the complexities of big data research, ensuring the integrity and depth of social inquiry amidst evolving data-driven methodologies. [ABSTRACT FROM AUTHOR]

Soft skills, such as personality characteristics, communication skills and leadership, affect personal career performance greatly. Therefore, predicting the soft skills of interviewees can provide interviewers with a strong reference for the decision of hiring. Nowadays, as asynchronous video interviews have gradually become a popular form of interviews, automatic interview evaluation of soft skills has attracted widespread attention from researchers. However, existing automatic evaluation methods have two significant drawbacks. First, most of them model the problem as multi-modal fusion of long-term sequences, while ignoring the consistency of multi-modal expression in short-time windows, which is a key attribute of the interview scene. Second, without embedding of professional knowledge in the interview field, the interpretability of the model is relatively weak. To address the above problems, we propose a novel Multi-modal Window-Consistency Fusion network, namely MWCF, to capture the expression consistency of different modalities in a short-time window and re-weight the language signals to enhance important portions in verbal clues. Meanwhile, in order to enhance the interpretability of the evaluation model, we introduce the professional knowledge of interviewers by proposing a topic generation module based on question attention, and embedding the most representative keywords under different soft skills into the model. Furthermore, a real-world interview dataset is built by developing an asynchronous interview platform, and extensive experiments are conducted to show the superior performance of our proposed model. [ABSTRACT FROM AUTHOR]

Stance detection is a crucial task in natural language processing and social computing, focusing on classifying expressed attitudes towards specific targets based on the input text. Conventional methods predominantly view stance detection as a task of target-oriented, sentence-level text classification. On popular social media platforms like Twitter, users often express their opinions through hashtags in addition to textual content within tweets. However, current methods primarily treat hashtags as data retrieval labels, neglecting to effectively utilize the semantic information they carry. In this paper, we propose a large language model knowledge-enhanced stance detection framework (LKESD) for stance detection. LKESD contains three main components: an instruction-prompted background knowledge acquisition module (IPBKA) that retrieves background knowledge of hashtags by providing handcrafted prompts to large language models (LLMs); a graph convolutional feature-enhancement module (GCFEM) is designed to extract the semantic representations of words that frequently co-occur with hashtags in the dataset by leveraging textual associations; an a knowledge fusion network (KFN) is proposed to selectively integrate graph representations and LLM features using a prompt-tuning framework. Extensive experimental results on three benchmark datasets demonstrate that our LKESD method outperforms 2.7% on all setups over compared methods, validating its effectiveness in stance detection tasks. [ABSTRACT FROM AUTHOR]

The proliferation of fake news on social media platforms necessitates the development of reliable datasets for effective fake news detection and veracity analysis. In this article, we introduce a veracity dataset of Arabic tweets called “VERA-ARAB”, a pioneering large-scale dataset designed to enhance fake news detection in Arabic tweets. VERA-ARAB is a balanced, multi-domain, and multi-dialectal dataset, containing both fake and true news, meticulously verified by fact-checking experts from Misbar. Comprising approximately 20,000 tweets from 13,000 distinct users and covering 884 different claims, the dataset includes detailed information such as news text, user details, and spatiotemporal data, spanning diverse domains like sports and politics. We leveraged the X API to retrieve and structure the dataset, providing a comprehensive data dictionary to describe the raw data and conducting a thorough statistical descriptive analysis. This analysis reveals insightful patterns and distributions, visualized according to data type and nature. We also evaluated the dataset using multiple machine learning classification models, exploring various social and textual features. Our findings indicate promising results, particularly with textual features, underscoring the dataset’s potential for enhancing fake news detection. Furthermore, we outline future work aimed at expanding VERA-ARAB to establish it as a benchmark for Arabic tweets in fake news detection. We also discuss other potential applications that could leverage the VERA-ARAB dataset, emphasizing its value and versatility for advancing the field of fake news detection in Arabic social media. Potential applications include user veracity assessment, topic modeling, and named entity recognition, demonstrating the dataset's wide-ranging utility for broader research in information quality management on social media.

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.

This document is a summary of a special issue titled "Artificial Intelligence in Complex Networks." The special issue explores the integration of artificial intelligence (AI) into complex networks and its impact on various scientific research areas. The articles cover topics such as key node identification, community detection, recommendation systems, object detection, data processing, and optimal decision algorithms in complex networks. The papers were contributed by researchers from Europe and Asia, showcasing a diverse range of perspectives and techniques. The document emphasizes the interdisciplinary nature of studying complex networks and highlights the need for responsible and ethical implementation of AI technologies. [Extracted from the article]

Digitally enabled means for judgment aggregation have renewed interest in "wisdom of the crowd" effects and kick-started collective intelligence design as an emerging field in the cognitive and computational sciences. A keenly debated question here is whether social influence helps or hinders collective accuracy on estimation tasks, with recent results on the role of network structure hinting at a reconciliation of seemingly contradictory past results. Yet, despite a growing body of literature linking social network structure and collective accuracy, strategies for exploiting network structure to harness crowd wisdom are underexplored. We introduce one such strategy: rewiring algorithms that dynamically manipulate the structure of communicating social networks. Through agent-based simulations and an online multiplayer experiment, we provide a proof of concept showing how rewiring algorithms can increase the accuracy of collective estimations—even in the absence of knowledge of the ground truth. However, we also find that the algorithms' effects are contingent on the distribution of estimates initially held by individuals before communication occurs. CCS Concepts: •Human-centered computing → Collaborative and social computing• Applied computing → Psychology. [ABSTRACT FROM AUTHOR]

Smartphones have become highly personalized interfaces between people and the technology ecosystem around them. In this sense, they play a key role for a technology shift from the current Internet of Things to a future human-centric paradigm of an Internet of People, automatically adapting smart things and services to the preferences and context of their users. In this paper, we propose the use of Complex Event Processing (CEP) engines deployed in the users' smartphones granting them context-awareness capabilities in order to react to external stimulus, and enabling them to interact both with smart things and services in the surroundings of the users. With that purpose, we have designed a communication architecture that interconnects CEP engines running on smartphones, providing a framework for building applications for Mobile-based Collaborative Social Computing (MCSC). For that, we make use of previous works of the authors with Digital Avatars, a framework which promotes the use of smartphones for inferring and sharing a unique digital avatar or virtual profile of each user. The resulting framework, which we have called Collaborative CEP, allows to implement complex interactions among users, and between them and the IoT, a common need in Collaborative Social Computing applications. We provide a proof of concept based on the implementation of a Cops and Robbers game to test the expressiveness and correct functioning of the framework, and we evaluate its performance and efficiency. [ABSTRACT FROM AUTHOR]

Continuous reinforcement learning carries potential security risks when applied in real-world scenarios, which could have significant societal implications. While its field of application is expanding, the majority of applications still remain confined to virtual environments. If only a single continuous learning method is applied to an unmanned system, it will still forget previously learned experiences, and retraining will be required when it encounters unknown environments. This reduces the learning efficiency of the unmanned system. To address these issues, some scholars have suggested prioritizing the experience playback pool and using transfer learning to apply previously learned strategies to new environments. However, these methods only alleviate the speed at which the unmanned system forgets its experiences and do not fundamentally solve the problem. Additionally, they cannot prevent dangerous actions and falling into local optima. Therefore, we propose a dual decision-making continuous learning method based on simulation to reality (Sim2Real). This method employs a knowledge body to eliminate the local optimal dilemma, and corrects bad strategies in a timely manner to ensure that the unmanned system makes the best decision every time. Our experimental results demonstrate that our method has a 30% higher success rate than other state-of-the-art methods, and the model transfer to real scenes is still highly effective. [ABSTRACT FROM AUTHOR]

Combating misinformation is one of the urgent social crises. Much research has shown that disinformation can lead to social panic and adversely affect society. It is crucial to promptly detect and counteract misinformation to reduce its adverse effects. Although progress in text-based fact verification has been made, the community needs further exploration into the user-oriented results. To address this gap, we integrate theories from linguistics, journalism, psychology, and cognitive science. We propose a disinformation detection algorithm based on multidimensional content analysis. This algorithm combines human factors and user perception in text interactive media to establish six dimensions for comprehensive content analysis. We have proposed a quantitative calculation method corresponding to six dimensions to detect misinformation. The average accuracy of the proposed model test on four datasets is 95.28%. The results show that this algorithm can effectively analyze from a multidisciplinary theoretical perspective and effectively identify misinformation in Chinese and English.

Combating misinformation is one of the urgent social crises. Much research has shown that disinformation can lead to social panic and adversely affect society. It is crucial to promptly detect and counteract misinformation to reduce its adverse effects. Although progress in text-based fact verification has been made, the community needs further exploration into the user-oriented results. To address this gap, we integrate theories from linguistics, journalism, psychology, and cognitive science. We propose a disinformation detection algorithm based on multidimensional content analysis. This algorithm combines human factors and user perception in text interactive media to establish six dimensions for comprehensive content analysis. We have proposed a quantitative calculation method corresponding to six dimensions to detect misinformation. The average accuracy of the proposed model test on four datasets is 95.28%. The results show that this algorithm can effectively analyze from a multidisciplinary theoretical perspective and effectively identify misinformation in Chinese and English. [ABSTRACT FROM AUTHOR]

Industry 4.0 focuses on the realization of smart manufacturing based on cyber-physical systems (CPS). However, emerging Industry 5.0 and Society 5.0 reaches beyond CPS and covers the entire value chain of manufacturing, and faces economic, environmental, and social challenges. To meet such challenges, we regard Industry 5.0 as a socio-technical revolution based on the socio-cyber-physical system (SCPS), and propose a socio-technically enhanced wisdom manufacturing architecture and framework beyond CPS-based Industry 4.0/smart manufacturing with especially concerning transition enabling technologies such as artificial intelligence, social Internet of Things (SIoT), big data, machine learning, edge computing, social computing, 3D printing, blockchains, digital twins, and cobots. Finally we address the roadmap to blockchainized value-added SCPS-based Industrial Metaverse for Industry/Society 5.0, which will achieve high utilization of resources and provide products and services to satisfy experience-driven individual needs via metamanufacturing cloud services towards smart, resilient, sustainable, and human-centric solutions. [ABSTRACT FROM AUTHOR]

As consumer adoption of immersive technologies grows, virtual avatars will play a prominent role in the future of social computing. However, as people begin to interact more frequently through virtual avatars, it is important to ensure that the research community has validated tools to evaluate the effects and consequences of such technologies. We present the first iteration of a new, freely available 3D avatar library called the Virtual Avatar Library for Inclusion and Diversity (VALID), which includes 210 fully rigged avatars with a focus on advancing racial diversity and inclusion. We also provide a detailed process for creating, iterating, and validating avatars of diversity. Through a large online study (n = 132) with participants from 33 countries, we provide statistically validated labels for each avatar's perceived race and gender. Through our validation study, we also advance knowledge pertaining to the perception of an avatar's race. In particular, we found that avatars of some races were more accurately identified by participants of the same race. [ABSTRACT FROM AUTHOR]

Abstract Smart grids are typically modelled as cyber–physical power systems, with limited consideration given to the social aspects. Specifically, traditional power system studies tend to overlook the behaviour of stakeholders, such as end‐users. However, the impact of end‐users and their behaviour on power system operation and response to disturbances is significant, particularly with respect to demand response and distributed energy resources. Therefore, it is essential to plan and operate smart grids by taking into account both the technical and social aspects, given the crucial role of active and passive end‐users, as well as the intermittency of renewable energy sources. In order to optimize system efficiency, reliability, and resilience, it is important to consider the level of cooperation, flexibility, and other social features of various stakeholders, including consumers, prosumers, and microgrids. This article aims to address the gaps and challenges associated with modelling social behaviour in power systems, as well as the human‐centred approach for future development and validation of socio‐technical power system models. As the cyber–physical–social system of energy emerges as an important topic, it is imperative to adopt a human‐centred approach in this domain. Considering the significance of computational social science for power system applications, this article proposes a list of research topics that must be addressed to improve the reliability and resilience of power systems in terms of both operation and planning. Solving these problems could have far‐reaching implications for power systems, energy markets, community usage, and energy strategies.