Social Computing Research Articles

PACMHCI V8, CSCW2, November 2024 Editorial

We are delighted to present this issue of the Proceedings of the ACM on Human-Computer Interaction to the Computer-Supported Cooperative Work and Social Computing (CSCW) community. The issue contains 171 papers, of which 117 were accepted from the July 2023 cycle, and 55 were accepted from the January 2024 cycle, and one was withdrawn. Exploring a broad range of inspiring and timely topics, the issue collects the sustained efforts of a community of authors, external reviewers, Associate Chairs, and Editors. The result is an outstanding selection of high-quality contributions which are certain to shape the future of the field. Our deepest gratitude as Papers Chairs goes to this community for its determination to extend the CSCW traditions of collegiality and rigor in crafting extraordinary scholarship.

Measuring Epistemic Trust: Towards a New Lens for Democratic Legitimacy, Misinformation, and Echo Chambers

Trust is crucial for the functioning of complex societies, and an important concern for CSCW. Our purpose is to use research from philosophy, social science, and CSCW to provide a novel account of trust in the 'post-truth' era. Testimony, from one speaker to another, underlies many social systems. Epistemic trust, or testimonial credibility, is the likelihood to accept a speaker's claim due to beliefs about their competence or sincerity. Epistemic trust is closely related to several 'pathological epistemic phenomena': democratic (il)legitimacy, the spread of misinformation, and echo chambers. To the best of our knowledge, this theoretical contribution is novel in the field of social computing. We further argue that epistemic trust is no philosophical novelty: it is measurable. Weakly supervised text classification approaches achieve F_1 scores of around 80 to 85 per cent on detecting epistemic distrust. This is also, to the best of our knowledge, a novel task in natural language processing. We measure expressions of epistemic distrust across 954 political communities on Reddit. We find that expressions of epistemic distrust are relatively rare, although there are substantial differences between communities. Conspiratorial communities and those focused on controversial political topics tend to express more distrust. Communities with strong epistemic norms enforced by moderation are likely to express low levels. While we find users to be an important potential source of contagion of epistemic distrust, community norms appear to dominate. It is likely that epistemic trust is more useful as an aggregated risk factor. Finally, we argue that policymakers should be aware of epistemic trust considering their reliance on legitimacy underwritten by testimony.

How developments in natural language processing help us in understanding human behaviour.

The ways people use language can reveal clues to their emotions, social behaviours, thinking styles, cultures and the worlds around them. In the past two decades, research at the intersection of social psychology and computer science has been developing tools to analyse natural language from written or spoken text to better understand social processes and behaviour. The goal of this Review is to provide a brief overview of the methods and data currently being used and to discuss the underlying meaning of what language analyses can reveal in comparison with more traditional methodologies such as surveys or hand-scored language samples.

Exploring the frontiers: A comprehensive bibliometric analysis of robotics in science education

The present bibliometric review focuses on the 50 research articles indexed in Scopus Database in robotics science education (RSE) between the years 2012 and 2024. The analysis of publications shows the steady growth rates which is especially notable for 2021 thereby specifying the growing demand in the field. These are researchers from universities named The Aristotle University of Thessaloniki, The Kaunas University of Technology, and the NYU Tandon School of Engineering. The funding bodies that have greatly supported research work include National Science Foundation, CAS-TWAS Scholarships, and EPF Lausanne. The current study emphasizes that the topic of robotics in the learning process involves tendencies of the social sciences, engineering, computer science, psychology and other sciences. The United States leads in publication output, followed by countries like Greece, Turkey, and Lithuania, highlighting a global interest in leveraging robotics for educational innovation geographically. The location of the research studies points to the fact that, there is a need to reach out to various countries as a way of encouraging the use of robotics in the STEM curriculum. The following analysis offers insights into the current trends, risks, and opportunities of robotic innovations in education study and underlined the need for a continuous stakeholders’ discourse on the best use of robots in enhancing science learning. This bibliometric analysis emphasizes the dynamic growth, disciplinary integration, and international collaboration characterizing RSE, underscoring the importance of ongoing dialogue and cooperation to realize its transformative potential in STEM education.

Identification of dynamic networks community by fusing deep learning and evolutionary clustering

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.

Ethnicity Detection with Convolutional Neural Network (CNN): Bangladesh Perspectives

Ethnicity detection, or the automatic determination of people's ethnic backgrounds using visual data, has become important in a number of disciplines, including social sciences, demographics, and computer vision. With a focus on Bangladesh's complex cultural landscape, we describe in this paper a novel method for ethnicity detection using convolutional neural networks (CNNs). We gathered a dataset of photographs depicting members of the three main ethnic groups living in the Chittagong hill neighborhood: Chakma, Marma, and Tripura. Utilizing the DenseNet121 model's robust feature extraction skills, we customized the architecture to meet the needs of our particular ethnicity detection application. A wide range of performance indicators were used to train and assess our unique CNN model. The outcomes illustrate accurate ethnicity detection technology's potential for identifying and resolving social inequities while showcasing a promising level of accuracy. To address possible biases in the model's prediction, ethical considerations are also covered. Overall, this work advances knowledge of Bangladesh's ethnic variety and demonstrates the potential of CNNs for ethnicity detection. The results provide up new avenues for investigation and applications that advance a more diverse and inclusive society.

Plasma glucose, HbA1c, insulin and lipid profile in Sudanese type 2 diabetic patients with cardiovascular disease: a case control study

Background: Type 2 diabetes mellitus (T2DM) and its consequences are a serious global public health issue. By 2030, the number of people with type 2 diabetes is predicted to reach 439 million. The purpose of this study is to evaluate the plasma levels of glucose, HbA1c, insulin, and lipid profile in Sudanese T2DM patients. Methods: This case control study included 165 Sudanese patients with diabetic type 2 and a cardiovascular condition as cases and 165 diabetic type 2 volunteers without a cardiovascular disorder as controls. The concentrations of plasma glucose, HbA1c, and lipid profile were assessed using a Mindray BS-480 auto-chemistry analyzer, and insulin was analyzed using a Cobase 411 auto analyzer. The collected data were analyzed using statistical tools for social science computer programs (SPSS version 21). Results: According to the findings, (59.4%) of patients between the ages of (50-69). Females made up 50.9%. (38.2%) of patients had an illness duration of between (8-15 years). (41.8%) of individuals did not have hypertension. There was a substantial rise in BMI, FBG, HbA1c, HDL-C, and insulin among diabetics with cardiovascular disease compared to diabetics without cardiovascular disease (p-value = 0.001, 0.000, 0.018, and 0.000). Females had significantly higher blood TC, LDL-C, HDL-C, and BMI than males (p-values = 0.000, 0,001, and 0.000, respectively). There were significant positive correlation between FBS, HBA1c, insulin and duration of disease (r=0.155, p, value=0.005) (r=0.160, p, value=0.004)(r=0.103, p. value=0.061)respectively, while there were significant negative correlation between TC, TG,LDL-C, HDL-C and duration of disease (r=-0.152, p, value= 0.006)(r=-0.023, p, value=0.678)(r=-0.113, p, value= 0.040)(r=-0.145, p, value=0.008)respectively. Conclusion: When comparing diabetics with cardiovascular disease to diabetics without cardiovascular disease, there was a substantial rise in BMI, FBG, HbA1c, HDL-C, and insulin. FBS, HBA1c, insulin, and illness duration all had a strong positive connection.

Computer Vision Models for Image Analysis in Advertising Research

This study introduces computer vision models for image analysis in advertising research. It reviews the literature in social science and computer science and identifies three categories and nine types of image analysis. The study uses these categories and types as a framework to select 12 computer vision models and compare them on their capability, accuracy, availability, and usability. Nine models are single-functional models, and three are multi-functional models; all 12 have been used in advertising research. The study also demonstrates how two models are used to classify a sample of image ads and assess the aesthetic scores of these ads to answer the research question about the relationship between content categories and aesthetic scores in image ads. It outlines several key steps for the use of computer vision models in advertising research and proposes future research directions. The study can serve as a guide to advertising researchers.

Social Support Domains Associated with Social Isolation in MCI

ABSTRACT The millions of older adults who are affected by social isolation report less social support, which has been shown to benefit well-being and decrease risk for cognitive decline. Social support is measured in different ways, including emotional, informational, and instrumental support. As an exploratory study, this secondary data analysis of 30 older adults living with Mild Cognitive Impairment examined what social support subtype is associated with social isolation. Social isolation was measured using the Patient Reported Outcomes Measurement Information System (PROMIS®) Social Isolation Computer Adaptive Test (CAT). Social support subtypes were measured using the PROMIS Informational, Emotional, and Instrumental Support CATs. We found that informational support was significantly associated with decreased self-reported social isolation. This finding increases our understanding of informational support’s potential and can inform future strategies for older adults with MCI facing social isolation.

New Data Sources for Demographic Research

AbstractWe 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.

Detrimental to Our Digital Well-Being: Campus Sexual Misconduct and Technology-Facilitated Sexual Violence Among University Students in Singapore.

Although increasingly prevalent in Singapore, campus sexual assault and harassment and technology-facilitated sexual violence (TFSV) remain underresearched. Conducted by scholars across social work, gender studies, policy studies, communication, and computer science, this interdisciplinary study explores the impact of technologies such as social media and online platforms on the digital well-being of university students in Singapore who experience TFSV and campus sexual misconduct. We conducted online surveys with 314 students and interviews with 28 students, the majority of whom were women and identified as victim-survivors. Our analysis revealed participants did not perceive technologies as entirely detrimental and possessed limited awareness of digital well-being. These findings contribute to understanding young victim-survivors' digital well-being and relationship to technology in Singapore by highlighting the experiences of college students.

Foreword for the Collection of Papers from the Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy

The 2022 Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy brought together research experts from many domains of social sciences, demography, public policy, statistics, and computer science to address key challenges in the use of the differentially private Census noisy measurement files to support social research and policy decisions.

Leveraging social computing for epidemic surveillance: A case study

Social media platforms have become a popular source of information for real-time monitoring of events and user behavior. In particular, Twitter provides invaluable information related to diseases and public health to build real-time disease surveillance systems. Effective use of such social media platforms for public health surveillance requires data-driven AI models which are hindered by the difficult, expensive, and time-consuming task of collecting high-quality and large-scale datasets. In this paper, we build and analyze the Epidemic TweetBank (EpiBank) dataset containing 271 million English tweets related to six epidemic-prone diseases COVID19, Flu, Hepatitis, Dengue, Malaria, and HIV/AIDs. For this purpose, we develop a tool of ESS-T (Epidemic Surveillance Study via Twitter) which collects tweets according to provided input parameters and keywords. Also, our tool assigns location to tweets with 95% accuracy value and performs analysis of collected tweets focusing on temporal distribution, spatial patterns, users, entities, sentiment, and misinformation. Leveraging ESS-T, we build two geo-tagged datasets of EpiBank-global and EpiBank-Pak containing 86 million tweets from 190 countries and 2.6 million tweets from Pakistan, respectively. Our spatial analysis of EpiBank-global for COVID19, Malaria, and Dengue indicates that our framework correctly identifies high-risk epidemic-prone countries according to World Health Organization (WHO) statistics.

How adolescents learn to build social bonds: A developmental computational account of social explore-exploit decision-making

Building social bonds is a critical task of adolescence that affords opportunities for learning, identity formation, and social support. Failing to develop close relationships in adolescence hinders adult interpersonal functioning and contributes to problems such as loneliness and depression. During adolescence, increased reward sensitivity and greater social flexibility both contribute to healthy social development, yet we lack a clear theory of how these processes interact to support social functioning. Here, we propose synthesizing these two literatures using a computational reinforcement learning framework that recasts how adolescents pursue and learn from social rewards as a social explore-exploit problem. To become socially skilled, adolescents must balance both their efforts to form individual bonds within specific groups and manage memberships across multiple groups to maximize access to social resources. We draw on insights from sociological studies on social capital in collective networks and neurocognitive research on foraging and cooperation to describe the social explore-exploit dilemma faced by adolescents navigating a modern world with increasing access to diverse resources and group memberships. Our account provides important new directions for examining the dynamics of adolescent behavior in social groups and understanding how social value computations can support positive relationships into adulthood.

Scalability through Pulverisation: Declarative deployment reconfiguration at runtime

In recent years, the infrastructure supporting the execution of situated distributed computations evolved at a fast pace. Modern collective adaptive applications – as found in the Internet of Things, swarm robotics, and social computing – are designed to be executed on very diverse devices and to be deployed on infrastructures composed of devices ranging from cloud servers to wearable devices, constituting together a cloud–edge continuum. The availability of such an infrastructure opens to better resource utilisation and performance but, at the same time, introduces new challenges to software designers, as applications must be conceived to be able to adapt to changing deployment domains and conditions. In this paper, we introduce a practical framework for the development of systems based on the concept of pulverisation, meant to neatly separate business logic and deployment concerns, allowing applications to be defined independently of the infrastructure they will execute upon, thus supporting scalability. The framework is based on a domain-specific language capturing, in a declarative fashion: pulverised application components, device capabilities, resource allocation, and (runtime re-) configuration policies. The framework, implemented in Kotlin multiplatform and available as open source, is then evaluated in a small-scale real-world demo and in a city-scale simulated scenario, demonstrating the feasibility of the approach and its potential benefits in achieving better trade-offs between performance and resource utilisation.

Cyber Security Challenges: Safeguarding Data in the Digital Era

The Internet is one of the fastest-growing areas of technical infrastructure development. In today’s business environment, disruptive technologies such as cloud computing, social computing, and next-generation mobile computing are fundamentally changing how organizations utilize information technology for sharing information and conducting commerce online. Today more than 80 percent of total commercial transactions are done online, so this field required a high quality of security for transparent and best transactions. The scope of Cyber Security extends not only to the security of IT systems within the enterprise, but also to the broader digital networks upon which they rely including cyber space itself and critical infrastructures. Cyber Security plays an important role in the development of information technology as well as Internet services. Our attention is usually drawn on “Cyber Security” when we hear about “Cyber Crimes”. Our first thought on “National Cyber Security” therefore starts on how good is our infrastructure for handling “Cyber Crimes”. This paper focus on cyber laws and security emerging trends, issues and challenges while adopting new technologies such as mobile computing, e-commerce, and social networking.

The mapping of social networks and computer technology in the star wars universe in 1977-2023: a historical retrospective

The mapping of social networks and computer technology in the star wars universe in 1977-2023: a historical retrospective

WallStreetBets: Assessing the Collective Intelligence of Reddit for Investment Advice

The WallStreetBets (WSB) community on Reddit gained prominence for its role in the GameStop saga and the resulting meme stock phenomenon. Concurrently, this has boosted the popularity of finance-related communities on Reddit, with the top five totalling more than 25 million subscribers at the time of writing. However, little is known about the reliability of the advice disseminated in these communities, which is a relevant research question within the field of social computing. In this study, we examine the collective intelligence of WSB, the largest and most active subreddit focused on the stock market, and assess its potential as a democratizing force in enabling access to financial knowledge. First, we establish that WSB meets several criteria to be considered a collectively intelligent crowd. Then, we test our hypothesis quantitatively by analyzing Reddit posts and financial data from a 28-month period to evaluate how successful an investor relying on WSB recommendations could have been. We define a portfolio of WSB’s most discussed stocks which shows significant growth, outperforming the S&P 500 index over the reviewed time frame. We further find that following buy signals at the time they are posted on WSB leads to positive outcomes over the long run, and that the GameStop hype merely amplified previously existing characteristics. The WSB portfolio underperforms the broader market during downturns, but recovers more quickly and achieves higher profits afterwards. The results of our work can be generalized to comparable finance-related communities, indicating that their original purpose of leisurely entertainment has already been extended towards tangible real-world value.

Human Intention Space - Natural Language Phrase Driven Approach to Place Social Computing Interaction in a Designed Space

Human Intention Space - Natural Language Phrase Driven Approach to Place Social Computing Interaction in a Designed Space

Mapping the field of global research on data literacy: Key and emerging issues and the library connection

This study examines the evolution of data literacy research from 2005 to 2022 by analysing 997 documents identified in Elsevier’s Scopus databases. It highlights pivotal moments, such as the inaugural mention of data literacy in 2005, and observes a consistent increase in annual document production, indicating growing interest. Research articles, particularly in computer science/engineering, education and social sciences/humanities, dominate. The interdisciplinary nature of contributions spans social sciences, computer science and humanities, reflecting the universal applicability of data literacy. Keywords cluster around foundational concepts, educational contexts, specialized domains, and emerging trends like digital transformation and health literacy. The study emphasizes the crucial role of libraries in nurturing data literacy skills, serving as information hubs and providers of educational frameworks. The conclusions underscore the significant growth, interdisciplinary engagement and vital role of libraries. Emerging keywords highlight the evolving trends, ethical considerations and privacy concerns. Implications for further studies on interdisciplinarity and emerging issues were discussed.