Journal of the Association for Information Science and Technology最新文献

This study presents a significant advancement in the realm of information science by exploring and validating a semiotic scale tailored for library software interfaces. Employing a design-based approach, the investigation identifies 14 crucial semiotic elements specific to library software interfaces, forming the cornerstone of its theoretical framework. The process of developing the semiotic scale involves creating user personas, conducting a comprehensive semiotic analysis, and engaging in productive collaboration with experts. Through meticulous evaluation and refinement, achieved by expert assessments and user testing, the scale offers actionable insights for guiding interface enhancement. The study's findings encompass a breadth of revelations, including user needs, user personas, outcomes of semiotic analysis, criteria development, and evaluation results across a diverse range of library software interfaces. Serving as a valuable tool for interface designers, the semiotic scale facilitates the alignment of design choices with user preferences and requirements. Its inherent adaptability ensures applicability across a spectrum of library software interfaces. The study's paramount contribution lies in bridging the gap between theoretical semiotics and practical design considerations. The comprehensive model offered by the development and validation of the semiotic scale empowers the evaluation of semiotic elements' impact on user perceptions and interface usability.

The question of how to appropriately care for and share Indigenous knowledges is central to cultural heritage documentation and, contemporary archival praxes and discourses. Transforming the standards of Indigenous knowledge management (IKM) necessitates confronting the legacies of colonial control and Indigenous expurgation that settler colonial galleries, libraries, archives, and museums (GLAM) have contributed to (intentionally or not). Writing of and within the Māori context in New Zealand, we argue that a renewed orientation toward dialogic collaboration between institutions and Indigenous communities is crucial for revitalizing archival practices and partnerships in ways that center Indigenous values. Moreover, we purport that aligning institutional practices with Indigenous values is insufficient if Indigenous communities are neither made to feel welcome within archival spaces nor empowered to utilize them. As such, we identify the notions of relationality, responsibility, and respect as vital in supporting this ethical transformation of Indigenous knowledge stewardship both in New Zealand and in other Indigenous contexts. We propose that current efforts to reshape IKM praxes in culturally responsible and responsive ways can be meaningfully expanded through dialogic engagement, and an ethics of care.

Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human feelings by ardently pursuing the allure of novelty, a fact that cannot be disregarded in the sentiment analysis. This paper aims to enrich data through the lens of subculture, to address the insufficient training data faced by sentiment analysis. To this end, a new approach of subculture-based data augmentation (SCDA) is proposed, which engenders enhanced texts for each training text by leveraging the creation of specific subcultural expression generators. The extensive experiments attest to the effectiveness and potential of SCDA. The results also shed light on the phenomenon that disparate subcultural expressions elicit varying degrees of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting the linear reversibility of certain subcultural expressions.

In this article, I explore the relationships between how humans have evolved to interact with the material world and how we interact with our information worlds. I argue that shaping processes, exemplified by how early humans created stone tools, are core ways to interact with the world that are appropriated to interact with information to create information solutions. To test these claims, I examine existing studies of information use from a shaping perspective. I finish by discussing how this evolutionary perspective to information use can benefit discussions of information behavior.

In this paper, I propose sculpting as an analogy for information use. I provide a background to the use of metaphors and analogies in Information Science, propose five analogical mappings between sculpting and information use, and then discuss the benefits to this analogy for thinking about how we use information.

Social media have long been studied from platform-centric perspectives, which entail sampling messages based on criteria such as keywords and specific accounts. In contrast, user-centric approaches attempt to reconstruct the personalized information environments users create for themselves. Most user-centric studies analyze what users have accessed directly through browsers (e.g., through clicks) rather than what they may have seen in their social media feeds. This study introduces a data collection system of our own design called PIEGraph that links survey data with posts collected from participants' personalized X (formerly known as Twitter) timelines. Thus, in contrast with previous research, our data include much more than what users decide to click on. We measure the total amount of data in our participants' respective feeds and conduct descriptive and inferential analyses of three other quantities of interest: political content, ideological skew, and fact quality ratings. Our results are relevant to ongoing debates about digital echo chambers, misinformation, and conspiracy theories; and our general methodological approach could be applied to social media beyond X/Twitter contingent on data availability.

While bibliographic classifications are traditionally based on disciplines, the logical alternative is phenomenon-based classification. Although not prevalent, this approach has been explored in the 20th century by J.D. Brown, the Classification Research Group, and others. Its principles have been stated in the León Manifesto (2007) and are currently represented by such general schemes as the Basic Concepts Classification and the Integrative Levels Classification. A phenomenon-based classification lists classes of phenomena, including things and processes irrespective of the discipline studying them (which can optionally be specified as an additional facet). Facets can work in a phenomenon-based system much as in a disciplinary one. This kind of system will promote the identification of potential relationships between research in different disciplines, and will especially benefit interdisciplinary work. The paper reviews the theory, history, structure, advantages, applications, and evaluation of phenomenon-based classification systems.

The gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio-technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio-technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio-technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio-technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio-technical issues in the gig economy that suggest short- and long-term opportunities for future research directions.

The threat of deplatforming as a content moderation strategy works to reduce harmful anti-vaccine content and health mis/disinformation on particular social media platforms. However, through an analysis of alt-health influencers, I show how the perpetual threat of being deplatformed allows them to strategically grow their audiences while funneling users to fringe platforms where the alt-health influencers share anti-vaccine and extremist content. Alt-health influencers are thus able to “launder” their reputations and their content through mainstream social media platforms, acting in accordance with each platform's specific content moderation policies, while persuasively deploying “deep stories” and developing parasocial relationships across mainstream and fringe platforms, building an alt-health influence network across platforms, and using threats of “censorship” on one platform to sell and legitimize their statuses as truth-tellers and informational authorities on another. The threat of deplatforming aids in the spreading of health mis/disinformation across social media platforms, demonstrating the necessity of addressing well-known alt-health influencers who are detrimental to the health of the overall information ecosystem through decisive, consistent, and multi-platform-coordinated deplatforming.

Deepfakes are a potential source of disinformation and the ability to detect them is imperative. While research focused on algorithmic detection methods, there is little work conducted on how people identify deepfakes. This research attempts to fill this gap. Using semi-structured interviews, participants were asked to identify real and deepfake videos and explain how their decisions were made. Three categories of deepfake identification strategies emerged: the use of surface video and audio cues, processing of the messages conveyed in the video, and the searching of external sources. Participants often used multiple strategies within each category. However, identification challenges occurred due to participants' preconceived notions of deepfake characteristics and the message embodied in the video. This work contributes to research by shifting the focus from the algorithmic detection of deepfakes to human-oriented strategies. Practically, the findings provide guidance on how people can identify deepfakes, which can also form the basis for the development of educational materials.