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Because using social media has become a major part of people’s daily lives, many of their personal characteristics are often implicitly or explicitly reflected in the content they share. We present a study of two personal characteristics—age and gender—related to user engagement on Instagram that can be determined through the characterization of images and tags. We demonstrate the strong influence of age and gender on Instagram use in terms of topical and content differences. We then build age and gender classification models that yield F1 scores of up to 88% and 74% in the detection of age and gender, respectively, and that better characterize users by images than by tags. We further demonstrate the robustness of our models using a new set of test data, with which the models exhibit greater overall performance than human raters. Our study highlights that future research should look to exploit images to a greater degree because they complement text and there are many unexamined images with no embedded text available. [ABSTRACT FROM AUTHOR]

With the growing dependence on smartphones for everyday activities, a large number of pedestrians nowadays are constantly fixated on their smartphone screens, and hence are susceptible to walking off pavements or colliding with other pedestrians. Reduced attention and situational awareness can render smartphone-occupied users, or smombies, oblivious to potential risks when using their smartphones while walking or driving. In this paper, we introduce a smartphone application, called Smombie Guardian, that detects obstacles and alerts smombies as they walk while viewing their smartphone screens to prevent potential collisions. Based on a user study with 74 participants who used Smombie Guardian in a real-life scenario, we highlight the effectiveness, usefulness, and unobtrusiveness of the algorithm and Smombie Guardian in helping users to avoid potential obstacles. [ABSTRACT FROM AUTHOR]

Although previous studies have investigated social media users’ privacy attitudes, little focus has been placed on understanding the degree of users’ concern about different types of private information or the changes in users’ privacy attitudes. This article presents novel insights on user attitudes toward 18 privacy items—identified through a review of the literature—and attitudinal changes through a comparative analysis. The authors also discuss the implications of the results that could better support users’ privacy management on social media. [ABSTRACT FROM AUTHOR]

In this study, we leverage human evaluations, content analysis, and computational modeling to generate a comprehensive analysis of readers’ evaluations of authors’ communication quality in social media with respect to four factors: author credibility, interpersonal attraction, communication competence, and intent to interact. We review previous research on the human evaluation process and highlight its limitations in providing sufficient information for readers to assess authors’ communication quality. From our analysis of the evaluations of 1,000 Twitter authors’ communication quality from 300 human evaluators, we provide empirical evidence of the impact of the characteristics of the reader (demographic, social media experience, and personality), author (profile and social media engagement), and content (linguistic, syntactic, similarity, and sentiment) on the evaluation of an author’s communication quality. In addition, based on the author and message characteristics, we demonstrate the potential for building accurate models that can indicate an author’s communication quality. [ABSTRACT FROM AUTHOR]

Social media has been widely adopted for assisting the efforts in emergency response and recovery, but it has been underutilized for emergency planning purposes. Emergency planning in a local community context must leverage accessible and free resources such as social media, because it is largely a volunteer enterprise. We describe our fieldwork with local annual festival emergency planning teams that led to the design of the Community Incident Report (CIR). CIR is a novel emergency planning system that externalizes community knowledge on persisting issues and common mitigation strategies by integrating police reports, local crisis information, and social multimedia content to foster citizens' awareness of local emergency information and to assist emergency planners in planning for recurring and cyclical events. We provide a use case analysis of CIR and its evaluation with 20 local residents, and discuss how it could be extended to inform emergency planning for other community events and local municipalities that share similar characteristics. [ABSTRACT FROM AUTHOR]

Being aware of local community information is critical to maintaining civic engagement and participation. The use of online news and microblog content to create and disseminate community information has long been studied. However, interactions in the online spaces dedicated to local communities tend to only garner very limited usage, and people often do not consider microblog content as a meaningful source of local community information. Local News Chatter (LNC) was designed to address these challenges by augmenting local news feeds with microblog content and presenting them in a tag cloud that displays news topics of varying popularity with different tag sizes. Our study with 30 local residents highlights that LNC increases the visibility of hyperlocal community news information and successfully utilizes microblog as an additional information layer. LNC also increases one’s community awareness and shows the potential for leveraging community knowledge as a deliberation platform for local topics. [ABSTRACT FROM AUTHOR]

Background: Over several years of recent efforts to make sense and detect online hate speech, we still know relatively little about how hateful expressions enter online platforms and whether there are patterns and features characterizing the corpus of hateful speech. Objective: In this research, we introduce a new conceptual framework suitable for better capturing the overall scope and dynamics of the current forms of online hateful speech. Methods: We adopt several Python-based crawlers to collect a comprehensive data set covering a variety of subjects from a multiplicity of online communities in South Korea. We apply the notions of marginalization and polarization in identifying patterns and dynamics of online hateful speech. Results: Our analyses suggest that polarization driven by political orientation and age difference predominates in the hateful speech in most communities, while marginalization of social minority groups is also salient in other communities. Furthermore, we identify a temporal shift in the trends of online hate from gender to age based, reflecting the changing sociopolitical conditions within the polarization dynamics in South Korea. Conclusion: By expanding our understanding of how hatred shifts and evolves in online communities, our study provides theoretical and practical implications for both researchers and policy-makers. [ABSTRACT FROM AUTHOR]

This paper presents a year-long study of our project, aiming at (1) understanding the work practices of clinical staff in trauma intensive care units (TICUs) at a trauma center, with respect to their usage of clinical data interface systems, and (2) developing and evaluating an intuitive and user-centered clinical data interface system for their TICU environments. Based on a long-term field study in an urban trauma center that involved observation-, interview-, and survey-based studies to understand our target users and their working environment, we designed and implemented MediSenseView as a working prototype. MediSenseView is a clinical-data interface system, which was developed through the identification of three core challenges of existing interface system use in a trauma care unit—device separation, usage inefficiency, and system immobility—from the perspectives of three staff groups in our target environment (i.e., doctors, clinical nurses and research nurses), and through an iterative design study. The results from our pilot deployment of MediSenseView and a user study performed with 28 trauma center staff members highlight their work efficiency and satisfaction with MediSenseView compared to existing clinical data interface systems in the hospital. [ABSTRACT FROM AUTHOR]

Research has shown that sensor data generated by a user during a VR experience is closely related to the user's behavior or state, meaning that the VR user can be quantitatively understood and modeled. Eye-tracking as a sensor signal has been studied in prior research, but its usefulness in a VR context has been less examined, and most extant studies have dealt with eye-tracking within a single environment. Our goal is to expand the understanding of the relationship between eye-tracking data and user modeling in VR. In this paper, we examined the role and influence of eye-tracking data in predicting a level of cybersickness and types of locomotion. We developed and applied the same structure of a deep learning model to the multi-sensory data collected from two different studies (cybersickness and locomotion) with a total of 50 participants. The experiment results highlight not only a high applicability of our model to sensor data in a VR context, but also a significant relevance of eye-tracking data as a potential supplement to improving the model's performance and the importance of eye-tracking data in learning processes overall. We conclude by discussing the relevance of these results to potential future studies on this topic. [ABSTRACT FROM AUTHOR]

Social scientists have identified a crisis of community in modern society, for example, Putnam's (Bowling alone: the collapse and revival of American community. Simon and Schuster, New York, ) study of a 40-year decline of community in American society. We argue that the design palette of Web 2.0 offers critical affordances for community information and interaction that enable hyperlocality and suprathresholding. We describe a community-based participatory design research project through which we investigated the design characteristics of hyperlocality and suprathresholding. We speculate on the possibility of a new design model for community networks. [ABSTRACT FROM AUTHOR]

Background: Although fake news creation and consumption are mutually related and can be changed to one another, our review indicates that a significant amount of research has primarily focused on news creation. To mitigate this research gap, we present a comprehensive survey of fake news research, conducted in the fields of computer and social sciences, through the lens of news creation and consumption with internal and external factors. Methods: We collect 2,277 fake news-related literature searching six primary publishers (ACM, IEEE, arXiv, APA, ELSEVIER, and Wiley) from July to September 2020. These articles are screened according to specific inclusion criteria (see Fig 1). Eligible literature are categorized, and temporal trends of fake news research are examined. Results: As a way to acquire more comprehensive understandings of fake news and identify effective countermeasures, our review suggests (1) developing a computational model that considers the characteristics of news consumption environments leveraging insights from social science, (2) understanding the diversity of news consumers through mental models, and (3) increasing consumers' awareness of the characteristics and impacts of fake news through the support of transparent information access and education. Conclusion: We discuss the importance and direction of supporting one's "digital media literacy" in various news generation and consumption environments through the convergence of computational and social science research. [ABSTRACT FROM AUTHOR]

The purpose of this study was to conduct the first usability inquiry of a gamified, behavior change theory-guided mobile app PuzzleWalk for increasing physical activity and reducing sedentary behavior in adults with and without autism spectrum disorder (ASD). Eighteen adults with and without ASD participated in a mixed-methods study that consisted of cognitive walkthrough, system usability assessment, and qualitative interviews. The results of the system usability testing indicated satisfactory quality of the PuzzleWalk system that can be readily applicable to both adults with and without ASD. Several notable issues were identified from the qualitative interviews that address critical insights into unique health and social needs in adults with ASD. Future work is warranted to examine the long-term effects of the PuzzleWalk system on increasing physical activity and reducing sedentary behavior in adults with and without ASD in real-world settings. [ABSTRACT FROM AUTHOR]

The wide use of recommendation systems includes more users and items in system operations, leading to a significant increase in the size of related datasets. However, recommendation algorithms on existing single-machine-based graph engines have been developed without considering the important characteristics of recommendation datasets, i.e., huge size and power-law degree distribution. In this paper, we address how to realize efficient graph- and matrix-factorization-based recommendation algorithms, handling recommendation datasets on RealGraph, a state-of-the-art single-machine-based graph engine. Through extensive experiments, we demonstrate that our recommendation algorithms on RealGraph universally and consistently outperform the algorithms on other graph engines over all datasets up to 34 times. [ABSTRACT FROM AUTHOR]

Primary symptoms of adults with autism spectrum disorder (ASD), such as pervasive social deficits in social interaction and communication, cause adults with ASD to adopt a sedentary lifestyle. Meanwhile, gamified and behavioral theory-based interventions have been shown to improve physical activity in a fun and unobtrusive way. In this paper, we describe the iterative design inquiry process of PuzzleWalk, a gamified, physical activity-promoting mobile app designed for adults with ASD. We report the design rationales and lessons learned across four user-centered design phases with ASD experts and adults with ASD, including user requirement gathering, iterative participatory design, usability evaluation, and field deployment. The design insights generated from this work could inform future research focusing on designing sociotechnical systems, games, and interventions for people with ASD. [ABSTRACT FROM AUTHOR]

A virtual reality (VR) controller plays a key role in supporting interactions between users and the virtual environment. This paper investigates the relationship between the user experience and VR control device modality. We developed a VR firefighting training system integrated with four control devices adapted from real firefighting tools. We iteratively improved the controllers and VR system through a pilot study with six participants and conducted a user study with 30 participants to assess two salient human factor constructs—perceived presence and cognitive load—with three device modality conditions (two standard VR controllers, four real tools, and a hybrid of one real tool and one standard VR controller). We found that having more realistic devices that simulate real tools does not necessarily guarantee a higher level of user experience, highlighting a strategic approach to the development and utilization of VR control devices. Our study gives empirical insights on establishing appropriate combinations of VR control device modality in the context of field-based VR simulation and training. [ABSTRACT FROM AUTHOR]

Abstract: The objectives of this study were to characterise reproductive and metabolic endocrinology parameters in Romney rams from lines selected since 1986, for high or low circulating IGF-1 concentrations. Seasonal changes were recorded in the patterns of IGF-1, insulin and the response of serum testosterone to hCG administration. Serum testosterone concentrations, adjusted for covariance with IGF-1 concentrations, were higher in both strains (P <0.05) in the autumn (March), compared to other times of year. The response of serum testosterone to hCG increased (P <0.01) between mid-winter (July) and the following autumn. Serum testosterone concentrations were higher (P <0.05) in high, compared to low-IGF rams in July and September (spring), but not in March (autumn). The serum IGF-1 concentrations were higher (P <0.01) throughout the entire experiment in the high- versus the low-IGF rams, although the concentrations in the high-IGF rams declined between July and March. Insulin concentrations were also higher in the high-IGF line. The difference between the two lines was greatest in July, but not significant in March. Samples of liver and testes tissue were collected from 4 animals in each line for the polymerase chain reaction (PCR) determinations of mRNA expression for IGF-1, Type 1 IGF receptor (IGF-1R) and IGF-binding proteins (IGFBP''s). Genes for IGF-1, IGF-1R and the IGFBP-2, -3, -4, -5 and -6 were expressed in the testis. Expression of IGF-1R and IGFBP-3 in the testis and that of IGF-1 in the liver, was greater in the high- than the low-IGF line rams. These studies suggest circulating concentrations IGF-1 to be associated with basal, but not gonadotrophin-stimulated steroidogenesis. The differences in IGF-1 status between lines are modulated by the differential expression of mRNA for IGF-1R and the IGFBP''s and by differences between lines in the negative feedback of IGF-1. [Copyright &y& Elsevier]

Smartphone usage is widespread in college classrooms, but there is a lack of measurement studies. We conducted a 14-week measurement study in the wild with 84 first-year college students in Korea. We developed a data collection and processing tool for usage logging, mobility tracking, class evaluation, and class attendance detection. Using this dataset, we quantify students' smartphone usage patterns in the classrooms, ranging from simple use duration and frequency to temporal rhythms and interaction patterns. Furthermore, we identify the key predictors of students' in-class smartphone use and their semester grades. Our results reveal that students use their phones for more than 25% of effective class duration, and phone distractions occur every 3–4 min for over a minute in duration. The key predictors of in-class smartphone use are daily usage habits and class characteristics, and in-class phone usage is negatively correlated with student grades. • Students used their phones for more than 20 min per class, which is over 25% of the effective class duration. • Phone usage was prevalent throughout the class, and phone distractions happened in every 3–4 min for over a minute. • Considerable underestimation of in-class phone usage was found: self-report: 8.25 vs. measurement: 12.28 • Predictors of in-class usage were daily usage behaviors, class size, and lecture organization. • Phone usage patterns such as daily and in-class use habits had a negative relationship with student grades. [ABSTRACT FROM AUTHOR]