Social Computing Research Articles

Analyze the usage of urban greenways through social media images and computer vision

Urban greenway is an emerging form of urban landscape offering multifaceted benefits to public health, economy, and ecology. However, the usage and user experiences of greenways are often challenging to measure because it is costly to survey such large areas. Based on the online postings from Instagram in 2017, this paper used Computer Vision (CV) technology to analyze and compare how the general public uses two typical greenway parks, The High Line in New York City and the Atlanta Beltline in Atlanta. Face and object detection analysis were conducted to infer user composition, activities, and key experiences. We presented the temporal patterns of Instagram postings as well as the group gatherings, smiling, and representative objects detected from photos. Our results have shown high user engagement levels for both parks while teens are significantly underrepresented. The High Line had more group activities and was more active during weekdays than the Atlanta Beltline. Stronger sense of escape and physical activities can be found in Atlanta Beltline. In summary, social media images like Instagram can provide strong empirical evidence for urban greenway usage when combined with artificial intelligence technologies, which can support the future practice of landscape architecture and urban design.

Applying Social Computing to Analyze the Effect of Negative Emotions on Social Desirability

Applying Social Computing to Analyze the Effect of Negative Emotions on Social Desirability

A Social Computing Based Analysis on Team Efficacy and Outcome of Real-time Online Sports Capstone Design Class

A Social Computing Based Analysis on Team Efficacy and Outcome of Real-time Online Sports Capstone Design Class

Analysis of Factors on Affecting Sugar Tax Awareness among Adults in South Korea Using Social Computing Technology

Analysis of Factors on Affecting Sugar Tax Awareness among Adults in South Korea Using Social Computing Technology

Applying Social Computing to Analyze the Effect of Tax Officials’ Organizational Communication on Job Performance

Applying Social Computing to Analyze the Effect of Tax Officials’ Organizational Communication on Job Performance

I, chatbot: How ‘human’ should chatbots be?

Dr Oli Buckley's research combines social science and computer science to explore how chatbot technology can be optimised to provide a more effective communication method while ensuring trust in users.

ELM-NeuralWalk: trust evaluation for online social networks

Trust relationship plays an important role in online shopping, recommendation systems, internet of things, etc. The problem of trust evaluation among users in online social network (OSN) has attracted much attention, and has become a hot issue in the domain of social computing. However, the way of trust propagation and aggregation in OSN is still not clear, as well as the accuracy of trust calculation. In order to calculate the indirect trust, an ELM-NeuralWalk algorithm to implement trust propagation and aggregation is proposed. ELM-WalkNet firstly learns two-hop trust calculation rules, then calculates two-hop trust among users in the OSN. After that, ELM-NeuralWalk updates the OSN with the calculated trust value, so as to realise the calculation of multi-hop trust among users through iterative calling ELM-WalkNet. Unlike traditional solutions that use inference methods, ELM-WalkNet can learn trust calculation rules in an inductive way and accurately calculate indirect trust between users. Experiments on two real OSN datasets showed that ELM-NeuralWalk outperforms existing solutions.

Contested social representations of a religious ritual in the Philippines: Text mining online discourses on the Traslación

The article examines the production of contested social representations of a Philippine religious ritual in online spaces using a mixed methods approach to semantic network analysis. Comments from the online media coverage of the 2019 Traslación were mined from the Facebook pages of the eight most popular Philippine media outlets. A semantic network consisting of collocation of frequently used words was generated using R and Gephi. Network analysis produced three word clusters highlighting bifurcations linguistically, in the use of Filipino or English, and substantively, in contested representations. Analysis of these themes reveals the centrality of discourses reifying the ritual as authentic expressions of faith. A minority of discourses reflect criticisms of the Traslación as idolatry. This reflects the dichotomy of discourses in the Traslación literature. The research demonstrates the relevance of social computing in the analysis of meanings of cultural phenomena across large populations.

Managing Reputation in Collaborative Social Computing Applications.

Managing Reputation in Collaborative Social Computing Applications.

Meta-Analisis Simulasi Virtual dalam Pembelajaran untuk Mendukung Berpikir Kritis

This study aims to conduct an analysis based on available data sources (meta-analysis) on the trend of using virtual simulations in learning to support critical thinking. The SCOPUS database was used as the data source in this study. This meta-analysis study was conducted in December 2021 by including the keywords “virtual, simulation, learning, and critical thinking” in the search documents section on the SCOPUS page. In the initial search, 112 documents were found, this includes all “subject areas, documents, and source types” and in the range from 1999 to 2021. Not a single document was found under 1999. The subject areas included are social sciences, computer science, and others (a total of 23 subject areas). Contains 8 documents and source types (articles, conference papers, book chapters, and others). The results are documented in (.csv) and (.ris) files. The files are processed using Microsoft Excel for document graphic visualization by year, print screens of SCOPUS data sources for document types and subject areas, and comprehensive analysis of quantitative data displayed on SCOPUS data sources. Finally, the results in this study show that the research trend of virtual simulation in learning to support critical thinking has penetrated in many fields. The assessment of SCOPUS primary data sources shows journal articles as the most “document and source type” (50%) followed by conference papers and others. In addition, social science is the most document “subject area” (28.2%) followed by computer science and others.

Four Decades Research on Higher Vocational Education: A Bibliometric Review

Vocational education is seen as an integral part of the educational system, either at the school level or at a post-secondary level. It is indicated that vocational education has immensely influenced life and plays a vital role in evolving societies worldwide. Thus, analysing these scientific domains evolution and creation are essential. This study discusses the bibliometric review of scholarly research performed in higher vocational education in recent years. The study uses the Scopus database and a wide variety of bibliometric metrics, including publications, citations, and publishing patterns. A graphical visualisation of bibliographic details using the VOS viewer software is also presented. Results show that the number of studies covering this subject has fluctuated, with the highest number of publications being 65 in 2020. Often, by reviewing publications and research areas, it can be construed that higher vocational education research is diverse, including social sciences, computer science, and engineering. The bibliometric review conducted offers a rigorous and thorough view of higher vocational education research that may benefit higher vocational education practitioners and researchers keen on the pursuit of enhancing or discovering new knowledge in this field.

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing.

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices’ trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach.

A conceptual framework for multidisciplinary design research with example application to agent-based modeling

The design community traditionally excels at multidisciplinary research efforts, integrating work from social science, engineering, computer science, and other disciplines. Simultaneously, design is also developing as a discipline in its own right. In order to balance a multidisciplinary 1 1 In this work we use multidisciplinary in the general sense, referring to a non-specific combination of disciplines. tradition against consolidation around a core set of disciplinary foundations, the design community must make intentional decisions about engaging in multidisciplinary research. Towards this end, we propose the Degrees of Disciplinarity Framework, a lingua franca for describing varying relationships between disciplines in a given research effort. We also introduce research archetypes in relation to this conceptual framework and provide examples of the framework using agent-based modeling research in design. Further, we discuss implications of this framework for the education of future design practitioners and researchers. • The design community must intentionally consider multidisciplinarity. • This work introduces the Degrees of Disciplinarity Framework. • The framework is demonstrated through examples to agent-based modeling.

Toward a generalized notion of discrete time for modeling temporal networks

AbstractMany real-world networks, including social networks and computer networks for example, are temporal networks. This means that the vertices and edges change over time. However, most approaches for modeling and analyzing temporal networks do not explicitly discuss the underlying notion of time. In this paper, we therefore introduce a generalized notion of discrete time for modeling temporal networks. Our approach also allows for considering nondeterministic time and incomplete data, two issues that are often found when analyzing datasets extracted from online social networks, for example. In order to demonstrate the consequences of our generalized notion of time, we also discuss the implications for the computation of (shortest) temporal paths in temporal networks. In addition, we implemented an R-package that provides programming support for all concepts discussed in this paper. The R-package is publicly available for download.

Korean popular culture analytics in social media streaming: evidence from YouTube channels in Thailand

This research aimed to study and analyze the influence and impact of Korean popular culture (K-pop) on Thai society. In this study, we used Social Network Analysis (SNA) to analyze streaming data obtained from a variety of YouTube channels belonging to YouTubers across the world, text analytics to analyze demographic characteristics, YouTuber's presentation techniques, as well as subscriber behavior, and multiple correlations analysis to analyze the relationship between factors affecting YouTube Channels in Thailand. The findings revealed that five Thai YouTube Channels were influencing Thai society. Furthermore, there were robust positive correlations between the number of dislikes and the number of comments (0.79), and the number of likes and comments (0.65). Additionally, there was a positive correlation between the number of views and the number of dislikes and one between the number of likes and dislikes. Future research can supplement the present findings with other social media sources to yield an even more diverse and comprehensive analysis. These analytics can be applied to various situations, including corporate marketing strategies, political campaigns, or disease/symptom analysis in medicine. This research extends to social computing by revealing intelligent trends in social networks.

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 to Create Government Public Policy Document Blueprint Draft Based on Social Media Data About Covid-19 Using LSTM and MMR Hybrid Algorithms

Abstract- Determining a policy is often limited in a short time so that decisions are prone to inaccuracies and are ultimately judged to be less targeted. Therefore, there is a necessity to use data mining technology. Currently, especially due to the continuously increasing case of the spread of COVID-19 in Indonesia, in order to reduce the rate of spread of COVID-19, the government has established a COVID-19 vaccination and emergency Community Activity Restriction Implementation (PPKM) policies. For the success of the policies, the government is required to ascertain and understand the attitude of the society. Hence, the policies can be accepted and supported by the society. YouTube is one of the sources to discover people’s attitudes because in YouTube, people can express their opinions freely. In this study, a model based on Natural Language Processing (NLP) with the Deep Learning method was developed to analyze people’s attitudes from their writings or posts on social media. As for the algorithm stages, first the model analysis was created using the Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms as a comparison. In the COVID-19 vaccination policy, the Bi-LSTM algorithm provided a better evaluation value, i.e., the accuracy value of 87.20%, recall of 87.14%, precision of 87.14%, and F1-score of 87.14%. In the emergency PPKM policy, the LSTM algorithm provided a better evaluation value of 95.13%, recall of 93.94%, precision of 94.08%, and F1-score of 94.01%. In addition, using the Maximum Marginal Relevance (MMR) method to obtain recommendations related to the COVID-19 vaccination policy, the result showed that the government needs to carry out re-socialization regarding vaccination and the impact of the vaccine so that the society can become more cooperative and the emergency PPKM policy needs to be reviewed because it has an impact on the society’s economy.

Advances in parallel and distributed computing and its applications

Advances in parallel and distributed computing and its applications

Fostering Cyber-Physical Social Systems through an Ontological Approach to Personality Classification Based on Social Media Posts.

The exponential increase in social networks has led to emergent convergence of cyber-physical systems (CPS) and social computing, accelerating the creation of smart communities and smart organizations and enabling the concept of cyber-physical social systems. Social media platforms have made a significant contribution to what we call human behavior modeling. This paper presents a novel approach to developing a users’ segmentation tool for the Romanian language, based on the four DISC personality types, based on social media statement analysis. We propose and design the ontological modeling approach of the specific vocabulary for each personality and its mapping with text from posts on social networks. This research proposal adds significant value both in terms of scientific and technological contributions (by developing semantic technologies and tools), as well as in terms of business, social and economic impact (by supporting the investigation of smart communities in the context of cyber-physical social systems). For the validation of the model developed we used a dataset of almost 2000 posts retrieved from 10 social medial accounts (Facebook and Twitter) and we have obtained an accuracy of over 90% in identifying the personality profile of the users.

An Ownership Verification Mechanism Against Encrypted Forwarding Attacks in Data-Driven Social Computing

Data-driven deep learning has accelerated the spread of social computing applications. To develop a reliable social application, service providers need massive data on human behavior and interactions. As the data is highly relevant to users’ privacy, researchers have conducted extensive research on how to securely build a collaborative training model. Cryptography methods are an essential component of collaborative training which is used to protect privacy information in gradients. However, the encrypted gradient is semantically invisible, so it is difficult to detect malicious participants forwarding other’s gradient to profit unfairly. In this paper, we propose a data ownership verification mechanism based on Σ-protocol and Pedersen commitment, which can help prevent gradient stealing behavior. We deploy the Paillier algorithm on the encoded gradient to protect privacy information in collaborative training. In addition, we design a united commitment scheme to complete the verification process of commitments in batches, and reduce verification consumption for aggregators in large-scale social computing. The evaluation of the experiments demonstrates the effectiveness and efficiency of our proposed mechanism.