Human-computer Interaction Research Research Articles

The use of information behaviour research in human-computer interaction

Introduction. Information behaviour research is criticised for its lack of applicability in practice. An area that can potentially benefit from information behaviour research is human computer interaction. We investigate the extent to which information behaviour is used (cited) in human computer interaction research. Method. We retrieved 21,720 human computer interaction articles published from 2011 to 2020. Analysis. We searched for information behaviour articles in 880,533 references cited by human computer interaction articles. A random sample of 400 citations was drawn and their citation type was categorised as affirmation, negation, application, perfunctory, and review. Results. Only 0.5 per cent of references in human computer interaction were information behaviour sources. 11.2% (2,432 out of 21,720) of human computer interaction papers cited information behaviour sources. Although the most common citation type was review (45%), a considerable number of citations were of application type (31.3%), and the rest were perfunctory (19%), affirmation (3.9%) and negation (0.7%). Negation and affirmation citations were more likely to occur in the conclusion of articles. Conclusions. The use of information behaviour research in human computer interaction is promising given the amount of application citations. Human computer interaction is an area where information behaviour research can influence practice and convergence and collaboration between the two fields can improve the usability of information behaviour research.

Experimental Operationalizations of Anthropomorphism in HCI Contexts:A Scoping Review

Anthropomorphism of computerized agents, avatars, and technologies has been the focus of a large body of research in human-computer interaction (HCI). Yet, operational definitions of anthropomorphism vary greatly, creating the potential for error when broad theoretical conclusions are drawn from operationalizations lacking in content validity. This scoping review aimed to identify and categorize the range of operationalizations of anthropomorphism in experimental studies of computerized agents, avatars, and technologies, adding needed clarity to a diverse area of inquiry. Using five selection criteria, this review categorized the operationalization(s) of anthropomorphism in 31 experiment-based articles published in academic research journals. Results showed a heavy dominance of manipulations of physical appearance as operationalizations of anthropomorphism, which thretens content validity and raises questions about the understanding of anthropomorphism in HCI.

Sketching dialogue: incorporating sketching in empathetic semi-Structured interviews for human-computer interaction research

ABSTRACT We introduce a novel data collection technique, Sketching Dialogue, which incorporates sketching as an integral part of the semi-structured interview process. This approach was evaluated and validated using two case studies involving 21 participants. Through the first case study, we demonstrate the value of Sketching Dialogue as an additional supportive artefact for interactive discussion in the interview setting. The second case study involved a process of other researchers applying the method within six different studies on a range of topics. We outline how Sketching Dialogue provided a more effective form of engagement with participants, especially regarding (personally) sensitive topics or where empathetic engagement was required. Through this work, we illustrate how sketching can enhance the quality of traditional research interviews for studies in human–computer interaction, in part due to its particularly flexible and accessible nature. Based on our experience, we also reflect upon the steps and practices needed to introduce a new qualitative research method to human–computer interaction researchers and designers.

A Review of Multimodel User Interface in Human Computer Interface Design

The evolution of computers has inspired the conception of Human-Computer Interaction (HCI). Experiments in HCI are increasingly including members of the next generation (those in their twenties and thirties) who are well educated and proficient in modern technologies. In this paper, behavioral and technological issues are both addressed in the field of HCI. The primary goal of this research in HCI is to shed light on previously unseen perspectives of human behavior and its connection to technology. The concept of resilience has been used in several contexts, including the corporate world, psychology, education, and the economics. Self-efficacy, optimism, self-awareness, and the ability to derive meaning from one's experiences are just a few of the foundational factors that contribute to resilience. An improved organization's resilience may be achieved via the use of this method. All of this will be done by resilience in order to increase people's awareness of the tools at their disposal for dealing with the difficulties that already exist.

How do people experience the temporality of everyday life changes? Towards the exploration of existential time in HCI

The idea of time as an existential concern has found scarce attention in the Human-Computer Interaction (HCI) community. Still, time in our life is mostly experienced in connection with important existential matters. Life changes are emblematic examples of aspects of our existence that are fundamentally temporal. In this article, I explore how people experience the temporalities of “change”, by asking 16 participants to report on life changes they consider relevant and their temporal aspects. I found that temporal features like velocity, direction, and synchronization deeply impact the process of change, by affecting the participants’ opportunities to make sense of and act upon their own transformations. The study findings point to a conception of existential time that contributes to both HCI research on time and life changing events, by highlighting the relativity of temporal slowness and speediness, the malleability of time, and the fundamental openness of our time experiences.

Visuospatial working memory and attention control make the difference between experts, regulars and non-players of the videogame League of Legends.

Video games have been postulated as an emerging field for studying the cognition-expertise relationship. Despite this, some methodological practices hinder scientific advance (e.g., heterogeneous samples, an ambiguous definition of expertise, etc.). League of Legends (LOL) is a massively played video game with a moderately defined structure that meets the requirements to overcome current study limitations. The aim of this study was to analyze cognitive differences among expert LOL players, regular LOL players, and non-videogame players. A sample of 80 participants was enrolled in three different groups of expertise. Participants were evaluated with behavioral tests of working memory, attention, cognitive flexibility, and inhibition. Kruskal-Wallis tests for group comparison showed that the experts performed significantly better than regular players and non-videogame players in the working memory test. Significant differences were also found between players and non-videogame players in the attention test. Methodological implications for future research in neuroscience and human-computer interaction are discussed.

Fabrication of Nāmya: A Bend and Touch-Sensitive Flexible Smartphone-Sized Prototype

In human-computer interaction research, prototypes allow for communicating design ideas and conducting early user studies to understand user experience without developing the actual product. For investigating deformation-based interaction, functional prototyping becomes challenging due to the unavailability of commercial platforms and the marginal availability of flexible electronic components. During functional prototyping, incurred time and cost are essential factors that further depend on the ease of stiffness customization, reproduction, and upgrade. To offer these advantages, this work presents the fabrication workflow of Nāmya, a smartphone-sized flexible prototype that can detect bend gestures and touch-based inputs using off-the-shelf sensors and flexible materials. This do-it-yourself (DIY) approach to fabricating deformable prototypes focuses on addressing the challenges of selecting flexible material, type of sensor, and sensor positions. We also demonstrate that the proposed use of a flexible three-dimensional- (3D-) printed internal structure with sensor pockets and the one-part silicone cast allows the development of robust deformable prototypes. This fabrication process offers the opportunity to easily customize device stiffness, reproduce prototypes with similar physical properties, and upgrade existing prototypes.

Human–Computer Interaction Research Needs a Theory of Social Structure: The Dark Side of Digital Technology Systems Hidden in User Experience

Human–Computer Interaction Research Needs a Theory of Social Structure: The Dark Side of Digital Technology Systems Hidden in User Experience

Emotion Detection on text using Machine Learning and Deep Learning Techniques

Abstract: Emotion detection on text is an important field of research in Artificial Intelligence and human-computer interaction. Emotions play key role in human interaction. Emotion detection is closely associated with sentiment detection, in which we detect the polarity of the text. But in emotion detection, we detect emotions such as joy, love, surprise, sadness, fear, and anger. Emotion detection helps the machines to understand human behavior and ultimately it provides users with emotional awareness feedback. In this paper, we are going to compare Machine Learning and Deep Learning techniques with their accuracy and f1 scores. The experimental results show the results provided by deep learning techniques (Bi LSTM and Bi GRU) with word embeddings are more accurate than the other techniques.

Can the Internet of Things Persuade Me? An Investigation Into Power Dynamics in Human-Internet of Things Interaction.

The advent of artificial intelligence (AI) and the Internet of Things (IoT) has revolutionized user experience with objects. Things can perform social roles and convey persuasive messages to users, posing an important research question for communication and human-computer interaction researchers: What are the factors and underlying mechanisms that shape persuasive effects of IoT? Bridging the reactance theory and the computers are social actors paradigm, this study focuses on how power dynamics are shaped in human-IoT interactions and its implications on persuasion. Specifically, the study examines the effects of the social role assigned to the IoT mobile app agent and the scope of IoT controlled by the app on users’ perceived power and subsequent persuasive outcomes. The results reveal that when the mobile IoT app is for controlling a smart home, the servant (vs. companion) agent elicits greater perceived power over IoT for users, leading to less threat-to-freedom and better persuasive outcomes, including attitude, intention, and actual behavior. However, such a difference is not observed when the mobile app is for controlling a single smart device (i.e., smart fridge). The study findings offer valuable implications for communication practitioners interested in using IoT as a persuasive tool.

Feminicide and counterdata production: Activist efforts to monitor and challenge gender-related violence

SummaryGender-related violence against women and its lethal outcome, feminicide, are a serious problem throughout the world. Official government data on gender violence and feminicide are often absent, incomplete, infrequently updated, and contested. We draw on data feminism to situate feminicide data as missing data. Building on qualitative interviews, this study discusses the informatic work of ten activist and civil society organizations across six countries who combat missing data by producing counterdata. Activists enact alternative epistemological approaches to data science that center care, memory, and justice. Activists also face significant information challenges that increase monitoring labor and add emotional burden to reading about violent deaths. This work contributes to literature on data activism and critical data studies, proposing feminicide data practices as an important research subject. The empirical insights contribute to human-computer interaction (HCI) research, suggesting ways that the field may support and sustain the counterdata production practices of activists.

Identifying Competition and Mutualism between Online Groups

Platforms often host multiple online groups with overlapping topics and members. How can researchers and designers understand how related groups affect each other? Inspired by population ecology, prior research in social computing and human-computer interaction has studied related groups by correlating group size with degrees of overlap in content and membership, but has produced puzzling results: overlap is associated with competition in some contexts but with mutual-ism in others. We suggest that this inconsistency results from aggregating intergroup relationships into an overall environmental effect that obscures the diversity of competition and mutualism among related groups. Drawing on the framework of community ecology, we introduce a time-series method for inferring competition and mutualism. We then use this frame-work to inform a large-scale analysis of clusters of subreddits that all have high user overlap. We find that mutualism is more common than competition.

Data Sensemaking in Self-Tracking: Towards a New Generation of Self-Tracking Tools

Human-Computer Interaction (HCI) researchers have been increasingly interested in investigating self-trackers’ experience with self-tracking tools (STT) to get meaningful insights from their data. However, the literature lacks a coherent, integrated and dedicated source on designing tools that support self-trackers’ sensemaking practices. To address this, we carried out a systematic literature review by synthesizing the findings of 91 articles published before 2021 in HCI literature. We identified four data sensemaking modes that self-trackers go through (i.e., self-calibration, data augmentation, data handling, and realization). We also identified four design implications for designing self-tracking tools that support self-trackers’ data sensemaking practices (i.e., customized tracking experience, guided sensemaking, collaborative sensemaking, and learning sensemaking through self-experimentation). We provide a research agenda with nine directions for advancing HCI studies on data sensemaking practices. With these contributions, we created an analytical information source that could guide designers and researchers in understanding, studying, and designing for self-trackers’ data sensemaking practices.

Consumer shopping emotion and interest database: a unique database with a multimodal emotion recognition method for retail service robots to infer consumer shopping intentions better than humans

Empowering retail service robots with empathy is one of the current research hotspots in the field of artificial intelligence. Identifying consumer emotions, understanding the changes in shopping interests, and developing appropriate sales strategies is a challenging task for retail service robots. We investigate the feasibility of using computer vision methods for empowering robots with empathy by examining the correlation between consumer emotion and levels of shopping interest. To this end, we construct the first video database of consumer sentiment changes in a business context and propose a deep learning method that uses multimodal information to infer consumers’ shopping intentions, and conduct preliminary experimental validation on this database. The experimental results show that the proposed method is 7% and 10% more accurate than manual assessment (n = 40) in identifying consumer emotions and predicting consumer shopping interest levels, respectively. Thus, the proposed method is valid and effective. We anticipate that the results of this study will have considerable implications for human–computer interaction research in service robots.

Regretful pleasure: Toward an understanding of flow cost in media use.

Flow (state of intense focus) during media use has been largely considered a desirable experience, with technologies developed to maximize the chance of encountering flow in computer-mediated environments. However, the total absorption of attention due to flow could be problematic in contexts where the user has multiple predetermined goals, and engaging with the flow-inducing media could cost them resources that may be otherwise devoted to other goals. When flow imposes a cost on the user’s goal performance, it may also reduce their post-experience gratification with the flow-inducing media. The present study proposes a novel theoretical framework to begin understanding the potential cost of flow in media use with supporting evidence from two survey and vignette studies (N = 235 and N = 245). Its findings will extend human-computer interaction research by highlighting the double-edged impact that flow might have on media users’ larger goal performance and downstream well-being.

Review on Human-Computer Interaction: An Evolution of Usablility

Abstract: This paper's goal is to give a broad overview of the field of Human-Computer Interaction. The basic definitions and terminology are covered, as well as a study of existing technologies and recent breakthroughs in the field, popular architectures used in the design of HCI systems, including unimodal and multimodal configurations, and HCI applications. A major goal of human-computer interaction research is to make systems more usable, valuable, and to give users with experiences tailored to their specific knowledge and goals. In an information-rich environment, the challenge is not just to make information available to individuals at any time, in any place, and in any form, but also to keep it up to date as per current trends and demands. Keywords: HCI - Human Computer Interaction, Multimodal HCI, Unimodal HCI, MMHCI - Multimodal HCI, UX - User Experience.

Digital Media Design for Dynamic Gesture Interaction with Image Processing

With the rapid development of human-computer interaction technology and research in the fields of ergonomics and user experience, people have increasingly demanded robot availability and ease of use. The rapid development of modern computer computing and digital media technology has made people’s contact and interaction with computers more and more frequent, and they have appeared more in people’s daily lives. Therefore, the convenience and freedom of computer communication have been proposed. Based on the above background, the research content of this paper is the design of dynamic gesture interactive digital media based on image processing. This paper proposes a design scheme of an interactive touch system based on image processing and a dynamic gesture recognition method and uses the time difference method to experimentally simulate the system proposed in this paper. Image processing is the technique of analyzing images with a computer to achieve the desired results. Image processing technology generally includes three parts: image compression, enhancement and restoration, and matching, description, and recognition. The experimental results show that, for the same motion trajectory, the accuracy depends on the complexity of the gesture degree. And the system’s recognition accuracy rate is always maintained above 98%, confirming that the system has the performance requirements of high recognition accuracy, fast response speed, and stable work. Finally, the system was tested for performance and function, which verified that the system meets the real-time requirements. For a particular gesture operator, the accuracy rate has a great relationship with the standard degree of its operation. If the similarity with the model is high, you can achieve a high recognition rate.

Impressions that last: representing the meaningful museum experience

ABSTRACT Research in human–computer interaction (HCI) has identified meaning as an important, yet poorly understood concept in interaction design contexts. Central to this development is the increasing emphasis on designing products and technologies that promote leisure, personal fulfillment, and well-being. As spaces of profound historical significance and societal value, museums offer a unique perspective on how people construct meaning during their interactions in museum spaces and with collections, which may help to deepen notions of the content of meaningful interaction and support innovative design for cultural heritage contexts. The present work reports on the results of two studies that investigate meaning-making in museums. The first is an experience narrative study (N = 32) that analyzed 175 memorable museum visits, resulting in the establishment of 23 triggers that inform meaningful interaction in museums. A second study (N = 354) validated the comprehensiveness and generalisability of the triggers by asking participants to apply them to their own memorable museum experiences. We conclude with a framework of meaning in museums featuring the 23 triggers and two descriptive categories of temporality and scope. Our findings contribute to meaning research in HCI for museums through an articulation of the content of meaning-making in the cultural sector.

Toward Research-Informed Design Implications for Interventions Limiting Smartphone Use: Functionalities Review of Digital Well-being Apps.

BackgroundMuch research in human-computer interaction has focused on well-being and how it can be better supported through a range of technologies, from affective interfaces to mindfulness systems. At the same time, we have seen a growing number of commercial digital well-being apps. However, there has been limited scholarly work reviewing these apps.ObjectiveThis paper aims to report on an autoethnographic study and functionality review of the 39 most popular commercial digital well-being apps on Google Play Store and 17 apps described in academic papers.MethodsFrom 1250 apps on Google Play Store, we selected 39 (3.12%) digital well-being apps, and from Google Scholar, we identified 17 papers describing academic apps. Both sets of digital well-being apps were analyzed through a review of their functionalities based on their descriptions. The commercial apps were also analyzed through autoethnography, wherein the first author interacted with them to understand how these functionalities work and how they may be experienced by users in their daily lives.ResultsOur findings indicate that these apps focus mostly on limiting screen time, and we advanced a richer conversation about such apps, articulating the distinctions among monitoring use, tracking use against set limits, and 4 specific interventions supporting limited use.ConclusionsWe conclude with 6 implications for designing digital well-being apps, namely calling to move beyond screen time and support the broader focus of digital well-being; supporting meaningful use rather than limiting meaningless use; leveraging (digital) navigation in design for friction; supporting collaborative interaction to limit phone overuse; supporting explicit, time-based visualizations for monitoring functionality; and supporting the ethical design of digital well-being apps.

What's Up With These Conversational Health Agents? From Users' Critiques to Implications for Design.

Recent advancements in speech recognition technology in combination with increased access to smart speaker devices are expanding conversational interactions to ever-new areas of our lives – including our health and wellbeing. Prior human-computer interaction research suggests that Conversational Agents (CAs) have the potential to support a variety of health-related outcomes, due in part to their intuitive and engaging nature. Realizing this potential requires however developing a rich understanding of users' needs and experiences in relation to these still-emerging technologies. To inform the design of CAs for health and wellbeing, we analyze 2741 critical reviews of 485 Alexa health and fitness Skills using an automated topic modeling approach; identifying 15 subjects of criticism across four key areas of design (functionality, reliability, usability, pleasurability). Based on these findings, we discuss implications for the design of engaging CAs to support health and wellbeing.