Human Information Behavior Research Articles

Data, not documents: Moving beyond theories of information‐seeking behavior to advance data discovery

AbstractMany theories of human information behavior (HIB) assume that information objects are in text document format. This paper argues four important HIB theories are insufficient for describing users' search strategies for data because of assumptions about the attributes of objects that users seek. We first review and compare four HIB theories: Bates' berrypicking, Marchionni's electronic information search, Dervin's sense‐making, and Meho and Tibbo's social scientist information‐seeking. All four theories assume that information‐seekers search for text documents. Next, we compare these theories to search behavior by analyzing Google Analytics data from the Inter‐university Consortium for Political and Social Research (ICPSR). Users took direct, scenic, and orienting paths when searching for data. We also interviewed ICPSR users (n = 20), and they said they needed dataset documentation and contextual information to find data. However, Dervin's sense‐making alone cannot explain the information‐seeking behaviors that we observed. Instead, what mattered most were object attributes determined by the type of information that users sought (i.e., data, not documents). We conclude by suggesting an alternative frame for building user‐centered data discovery tools.

“Why Should We Care?”: Motivations of Public Librarians in the American South to Expand Civic Engagement for Racial Justice

ABSTRACTAn important aspect of human information behavior research is to understand the motivations of people as they interact with information and technologies to make a difference. This paper presents exploratory findings of a qualitative study of public librarians' motivations to expand civic engagement for racial justice in the American South. The geographical region is stereotypically characterized historically as being solely racially intolerant in public perceptions, scholarly discourse, political networks, and news media. This research challenges such limited notions, highlighting public librarian's logic‐based motivations to further civic engagement for racial justice in context of the nation's historical/contemporary racialized political climate. In the process, the authors acknowledge the voices/perspectives of public librarians, an often‐underrepresented group in privileged academic information behavior research. Thematic narrative analysis is explored as a strategic mode of presenting the findings.

MF-Match: A Semi-Supervised Model for Human Action Recognition.

Human action recognition (HAR) technology based on radar signals has garnered significant attention from both industry and academia due to its exceptional privacy-preserving capabilities, noncontact sensing characteristics, and insensitivity to lighting conditions. However, the scarcity of accurately labeled human radar data poses a significant challenge in meeting the demand for large-scale training datasets required by deep model-based HAR technology, thus substantially impeding technological advancements in this field. To address this issue, a semi-supervised learning algorithm, MF-Match, is proposed in this paper. This algorithm computes pseudo-labels for larger-scale unsupervised radar data, enabling the model to extract embedded human behavioral information and enhance the accuracy of HAR algorithms. Furthermore, the method incorporates contrastive learning principles to improve the quality of model-generated pseudo-labels and mitigate the impact of mislabeled pseudo-labels on recognition performance. Experimental results demonstrate that this method achieves action recognition accuracies of 86.69% and 91.48% on two widely used radar spectrum datasets, respectively, utilizing only 10% labeled data, thereby validating the effectiveness of the proposed approach.

A HUG taxonomy of humans with potential in human–robot hugs

Humans can easily perform various types of hugs in human contact and affection experience. With the prevalence of robots in social applications, they would be expected to possess the capability of hugs as humans do. However, it is still not an easy task for robots, considering the complex force and spatial constraints of robot hugs. In this work, we propose the HUG taxonomy, which distinguishes between different hugging patterns based on human demonstrations and prior knowledge. In this taxonomy, hugs are arranged according to (1) hugging tightness, (2) hugging style, and (3) bilateral coordination, resulting in 16 different hug types. We then further study the hug type preference of humans in different scenarios and roles. Furthermore, we propose a rule-based classification system to validate the potential of this taxonomy in human–robot hugs based on a humanoid robot with an E-skin of contact sensation. The HUG taxonomy could provide human hugging behavior information in advance, facilitating the action control of humanoid robots. We believe the results of our work can benefit future studies on human–robot hugging interactions.

I'm the same, I'm the same, I'm trying to change: Investigating the role of human information behavior in view change

AbstractInformation is powerful; it can influence peoples' views and, in turn, their behavior. Much recent research and discussion on the role information plays in view change has focused on filter bubbles, echo chambers and misinformation and how they might influence what people think and how they act. However, no prior work has focused specifically on understanding the human information behavior (HIB) that drives and facilitates view change. We report findings from interviews with 18 people who recently changed views on issues they considered important. We found a tight symbiotic relationship between HIB and view change; passive information encountering sparked change, often spurring follow‐up active seeking and verification which progressed the change to a “point of no return,” supported making the change and reinforced the decision to change. When shared, information that contributed to the change sometimes sparked changes in others (as did expressing or debating the change), serving as an information encounter that perpetuated a cycle of HIB and view change. This understanding of the integral role of HIB in view change can inform policy and systems design to promote view change autonomy and a broader research agenda of understanding HIB to support democratic principles and values.

A Systematic Review of Human Information Behavior: Implications for Research, Teaching, and Learning

This systematic review provides an exploratory analysis of Human Information Behavior from the searchers’ affective perspective within the domain of Higher Education. Additionally, results of the review will guide discussion for the design of information systems in higher education and related teaching practices. The review seeks to reveal research gaps in the existing literature and identify new directions for further study. Overall, this review will provide a valuable contribution to the field of HIB and its implications for research, teaching, and learning.

Research Trends in Library and Information Science in South Africa and Nigeria: A Content Analysis

Abstract A review of the research trends in library and information science (LIS) is important as it provides insight into the research practices of scholars in the field. This study investigated the research trends in master’s and doctoral research outputs relating to LIS produced in Nigeria and South Africa between 2009 and 2015 to identify the most researched subject areas and the most neglected subject areas in LIS research in the two countries. A total of 400 master’s and doctoral research outputs produced in Nigeria and South Africa were analysed by means of a qualitative content analysis, and 15 research areas were created by means of modified subject categorisation adopted from the extant literature. The findings were that the most researched subject area in both countries was human information behavior in respect of both master’s and doctoral research outputs. The least researched areas in LIS research outputs in Nigeria were archives and records management, LIS history and developments, bibliometrics, scientometrics and informatics, knowledge and information resource management. In South Africa, bibliometrics, scientometrics and informatics were the least researched areas in respect of doctoral research outputs, and LIS education and pedagogy were the least researched areas in master’s research outputs. The positivist research paradigm was more prevalent. The implication of the findings is that LIS schools in Nigeria and South Africa need to focus more on research areas that deal with current trends and challenges confronting the profession. In today’s world of work, most services are driven by information technology and research should be directed towards addressing the implementation of recent trends in the profession. Since the sample framework used for Nigeria was low, it may not be appropriate to generalise this finding to other institutions.

Ethical Issues of Human Information Behaviour and Human Information Interactions

Abstract The objectives of the study are to determine the main ethical factors related to human information interactions. The main research question was: What is the perception of ethical issues of information work and future topics of information ethics in digital environment? A brief analysis of related ethical issues of information is presented. The main applied methodology was a Delphi study on information ethics with selected experts from Slovakia and Czech Republic. The data were analysed with the use of mixed methods of discourse and content analyses and conceptual modelling. We present results of the discourse analyses (first round, 19 experts; discussion, 6 experts). Results are interpreted with regard to the ethical issues of work with digital information, main ethical dilemmas of the use of advanced technologies, and values of information. Results are visualised in three conceptual maps. A final conceptual model represents the epistemic consensus of experts, including social and cultural rules, value tensions between man and technologies, and utility, truth, and objectivity of information. We propose to include the identified ethical factors into models of human information behaviour. Recommendations for practice focus on value-sensitive design of digital services and products in the intercultural contexts of information literacy, education, science, workplaces, and everyday information use.

Climate change information seeking

AbstractThis research develops and tests a model of individual intentions to actively seek information about climate change. Our premise is that the individual's intention to actively seek information about climate change would determine their knowledge of and attitudes towards climate change, and this would in turn influence how they act or change their behaviors in response to that risk. Our model identifies key cognitive, affective, and situational variables drawn from research in human information behavior and risk communication. We conducted an online survey in which 212 participants in Canada and the United States responded. The results showed that the model was able to explain more than 40% of the variance in intention to seek climate change information. Social Norms, Affective Response, and Social Trust were the most important variables in influencing intention to seek climate change information. We conclude that climate change information seeking has a strong social dimension where social norms and expectations of relevant and respected others exert a major influence, and that the individual's emotional response towards the risk of climate change is more important than the individual's cognitive perception of how much information they need on climate change.

Parsing through paradigms: uncertainty and decision-making in human information behavior

PurposeThis study demonstrates how individual paradigms implicate the questions asked, methods used and results drawn in association with a common object of study in human information behavior (HIB) research – the relationship between uncertainty and decision-making.Design/methodology/approachThe author uses textual case studies to examine uncertainty and decision-making through the framework of four paradigms used in HIB research: positivism, cognitivism, collectivism and constructionism and suggests deconstructionism as a paradigm which raises new questions around this topic.FindingsPositivistic approaches to uncertainty are often systems oriented; cognitive approaches are often user-oriented; collectivist approaches are intersubjective; and constructionist approaches blend a subjective and intersubjective research orientation. Deconstructionism raises new questions around ethics and responsibility in relation to decision-making, and the author therefore situates it as a new paradigmatic approach for this topic in HIB research.Originality/valueDespite the presence of research aimed at recognizing and defining paradigms in HIB research, a comparative micro-examination of how individual paradigms implicate a specific research topic has yet to be conducted. Each paradigm uniquely shapes the ways in which uncertainty and decision-making are characterized, but the four central ones examined here have thus far left out questions of ethics and responsibility as being core elements of decision-making as tied to uncertainty. Therefore, this paper introduces deconstructionism as a paradigm new to HIB uncertainty research, arguing that it provides an important and novel complication of existent research questions and approaches.

Eye tracking method based on mobile big data in computer environment

The human brain needs to rely on the information obtained by the eyes to process information. To a certain extent, the eyes are the reflection of the human brain activity. Based on this, many people pursue eyeball changes to judge a person’s psychological state, and eye tracking technology has also been proposed, playing an increasingly important role in different fields. The purpose is to study the human attention distribution system in the computer environment. We use the eye tracking technology of the eye tracker to record any actions of each learner during learning, use the function of the eye tracker to obtain various data, and use statistical analysis to analyze it. The experimental results show that the eyes can reflect human behavior information well. Analyzing eye movement data can deepen people’s understanding of the mechanism of attention distribution and improve learning efficiency by 20%.

Adaptation and Validation of the Big Five Inventory-2-Short Form in a Sample of Moroccan Young Adults

A Person’s reactions and human behavioral information may be easily understood and anticipated if the personality traits underlying differences are correctly identified. The purpose of this study is to examine the adaptation and validation of the Big Five Inventory 2 Short Form (BFI-2-S) in the Moroccan context. This measure identifies the five major personality traits of persons: extraversion, agreeableness, conscientiousness, negative-Emotionally, and open-mindedness. The factor analysis confirms the five domains described by the BFI-2-S authors. The means and internal consistency of the five domains obtained are very satisfactory and close to those found in previous studies. The psychometric properties obtained from this study support the BFI-2-S's applicability to Arabic-speaking young adults in the Moroccan context.

Examining the predictors of multitasking information behavior among library and information science professionals in Pakistan

PurposeThe multitasking phenomenon has been prevailing in the technology-driven information environment. People are engaged in multitasking to process information and deal with personal and professional information tasks. This study aims to explore the external predictors of multitasking information behavior (MIB) of library and information science (LIS) professionals from Pakistan.Design/methodology/approachThis is a quantitative study based on a questionnaire survey, and data was collected through Google Form; the link was shared via e-mail and WhatsApp to get maximum responses. The sampling includes the LIS professionals working in Higher Education Commission-recognized universities of Khyber Pakhtunkhwa (KPK), Pakistan. A total of 126 responses were received from 41 universities of KPK.FindingsPearson correlation and regression were applied by using SPSS for data analysis. The findings revealed that time pressure (TP) is a good predictor of multitasking because when professionals got tasks with deadlines, they try to perform multiple activities at a time. Therefore, TP is a predictor of human MIB. This study also revealed that the work environment is a less significant predictor of MIB, and the use of multiple information resources is a weak predictor of MIB. It is noted that individual covariates were not predictors of the multitasking information. However, TP was the most significant predictor among all the contextual and individual factors predicting MIB.Research limitations/implicationsThis research line is significant because MIB is a new dimension of human information behavior among LIS professionals. The findings are beneficial for LIS professionals to increase their work productivity and performance by rationalizing the significant predictors.Originality/valueTo the best of the authors’ knowledge, no such study is available that highlighted the MIB among LIS professionals. Therefore, this study will highlight external factors’ effects on LIS professionals’ MIB. This study will contribute to the literature on libraries and information management as this study describes the LIS professionals’ behavior.

Real-Time Through-Wall Multihuman Localization and Behavior Recognition Based on MIMO Radar

Human localization and behavior recognition (HLBR) is an important research topic in wireless sensing and computer vision. Most studies mainly use cameras and millimeter-wave radars, which can achieve fine-grained human localization; however, they cannot solve the problem of wall occlusion. Although methods that use Wi-Fi can penetrate walls, they cannot detect the precise localization of people due to their narrow bandwidth limitation. The above problems limit the application of HLBR in real life. This paper proposes a real-time, through-wall, multi-human localization and behavior recognition system, RTWLBR. In this system, we design a multiple-input multiple-output (MIMO) radar in the 1-2 GHz frequency range. An ultrawide band (UWB) positioning device and a camera are attached to the self-developed, through-wall radar system to capture the simultaneous localization, behavior information, and radar reflection heatmap of people behind a wall, where location information and behavior information are jointly embedded and encoded as a confidence matrix, which serves as a supervision signal for neural network training. A multi-feature fusion network based on a 3D convolutional neural network (3D CNN) is designed to learn human localization and behavior information from radar heatmaps. A joint heatmap compression autoencoder network is utilized to assist network training to reduce quantization error and running costs. The experimental results show that the RTWLBR method can perform real-time localization and behavior recognition of target humans behind a 24 cm brick wall.

Information literacy, well-being, and rural older adults in a pandemic

Many older adults residing in rural communities faced particularly damaging consequences of the COVID-19 pandemic crisis. Given the impact of the pandemic on the ability to rely on traditional forms of information (i.e. interpersonal communication), reliance on digital technology and other non-traditional sources of information grew. Those who lacked the information literacy, defined in this study as the ability to find and evaluate the reliability of information to address an information need, may have faced psychologically damaging consequences caused by the proliferation of misinformation and information poverty. Informed by an ecological understanding of human information behavior, this paper reports on the findings of a study of rural older adults’ information literacy skills and sense of well-being during the COVID-19 pandemic crisis. A mail survey, based on concepts from psychology and information science and question wordings adapted from the 2021 Health and Retirement Survey and Jones-Jang et al.’s study of literacies and fake news, was distributed in the summer of 2021 to older adults living in rural Kansas. About 206 valid responses to the survey were received. These were analyzed using correlation and regression testing. The findings indicate a significant relationship between information literacy skills and one’s overall sense of well-being. Personal (age, gender, health, life control), economic (employment, finances), and social-relational resources (social engagement, relationship quality) all were shown to relate positively to information literacy skills. These findings provide definition to our understanding of the importance of information literacy in modern understandings of psychological well-being.

Transitioning Towards Sustainability: The Information Practices of Sustainable Food Consumers

ABSTRACTSustainability is an emergent topic due to the increased climate and social issues caused by human activities. While sustainability is not a new concept in library and information science, its scope is often limited to library and information systems. This study aims to expand sustainability to human information behavior and practice. This poster reports initial findings from a pilot study in Jan – April 2022. Interviews and photo diaries with 6 sustainable food consumers about their information behaviors and practices were conducted and analyzed using the constructivist grounded theory methodology. We found that participants use sensory and embodied information in assessing the real impact of their sustainable food behaviors, and that this process is essential in the transition to sustainable food behaviors. Participants assess the impact of their transition to sustainable food by comparing information at different times, places, and between actors (individuals and food providers). This research sheds light on the role of time in everyday information use, and demonstrates that sustainability is a fruitful research area in human information behavior and practice.

Hiker on trail: Information practices in the wild. A mixed methods approach to investigate real-time mobile technology use by long-distance backpackers

Long distance hikers' information practices are considered via an ‘in-field’ methodology that generated rich experiential data. Participants documented and reflected on their experiences and use of mobile digital technology while undertaking a two-week coast-to-coast walk across the Scottish Highlands. Empirical evidence on the adoption and use of mobile digital technology by backpackers, and the implications this has for their practices and interactions is scarce. “Research in the Wild” (RITW) mixed methods facilitated exploration of how and why mobile digital technology is employed via analysis of in-field video-logs, interviews with eight participants, and a survey. Analysis of the research interview data provides insights into fieldwork participants' reactions to, and experiences with, the methods employed. The affordances and issues of the approach are discussed, highlighting the benefits and challenges of mixed methods for Human Computer Interaction and Information Behavior research in the arena of outdoor recreation.

An effective behavior recognition method in the video session using convolutional neural network.

In order to further improve the accuracy of the video-based behavior recognition method, an effective behavior recognition method in the video session using convolutional neural network is proposed. Specifically, by adding the target detection phase before the behavior recognition algorithm, the body region in the video can be accurately extracted to reduce the interference of redundant and unnecessary background noises, and at the same time, the inappropriate images can be replaced, which has reached the role of balance background trade-off, and finally, the neural network can learn the human behavior information with emphasis. By adding fragmentation and stochastic sampling, the long-time time-domain modeling of the whole video session can be established, so that the model can obtain video-level expression ability. Finally, the improved loss function is used for behavior recognition to solve the problem of classification difficulty and possible sample imbalance. In addition, we conducted the hyperparametric experiment, the ablation experiment and the contrast experiment on different open source and benchmark datasets. Compared with other commonly used behavior recognition algorithms, the experimental results verify the effectiveness of the proposed method. In addition, the related deep learning-based methods used in behavior recognition are reviewed at the beginning of this paper, and the challenges in behavior recognition and future research directions are prospected at the end of this paper, which will undoubtedly play a double role in the work of later researchers.

E-cigarette puffing topography: The importance of assessing user behaviour to inform emissions testing.

Analysis of the chemical composition of e-cigarette emissions is an important step in determining whether e-cigarettes offer both individual and population-level harm reduction potential. Commonly, e-cigarette emissions for chemical analysis are collected when using e-cigarettes according to standardised puffing regimens, such as those recommended by the International Organization for Standardization (ISO) or the Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA). While the use of such standard puffing regimens affords a degree of uniformity between studies and are also recommended by regulatory authorities who require the submission of e-cigarette emissions data to make decisions regarding allowing a product to be commercially marketed, the standardised regimens do not necessarily reflect human puffing behaviour. This can lead to under- or over-estimating real-world emissions from e-cigarettes and inaccuracy in determining their harm reduction potential. In this review, we describe how human puffing behaviour (topography) information can be collected both in the clinical laboratory and in the real world using a variety of different methodologies. We further discuss how this information can be used to dictate e-cigarette puffing regimens for collecting emissions for chemical analyses and how this may lead to better predictions both of human yields of e-cigarette emissions constituents and of risk assessments to predict e-cigarette tobacco harm reduction potential.

Conversational Voice Assistants and a Case Study of Long-Term Users: A Human Information Behaviours Perspective

ABSTRACT This paper describes the human information behaviours of long-time users of voice assistants or chatbot software that focuses on voice interaction to retrieve information, and which operate on technologies that use artificial intelligence to understand and act upon the information requests of the user through a conversational interface. The paper reports on a case study based on 10 interviews with adult participants who have used voice assistants in their homes for at least six months. Interviews were semi-structured and focused on connecting to the users’ and their family’s experiences of using voice assistants in their daily lives. Insights from our findings focus on two areas, how the humanness of voice assistants and their perceived personality affect information retrieval, and secondly, the difficulties in transitioning from text-based information seeking to speech-based interactions. This paper makes a case for expanding human information behaviour studies to include interactive conversational information retrieval devices such as voice assistants.