bluuebunny/crossref_metadata_2025_split huggingface dataset:
Voices of the Stacks: A Multifaceted Inquiry into Academic Librarians' Tweets
Authors: Ghosh Souvick , Thajudeen James 
Published: October 2023

ABSTRACTTwitter has emerged as an important forum for discussion among academic librarians. In this research, we take a mixed‐methods approach to study the thematic content and sentiment of tweets authored by academic librarians in the United States, Canada, and the United Kingdom. We found differences in the semantic content and themes present in the data from each country that point to differences in how librarians in each country engage on Twitter. While more work remains to be done, we cast new light on how members of professional communities use social media. Our qualitative analysis identified 11 thematic categories in academic librarians' Twitter discussions, focusing on professional topics. UK librarians exhibited a higher frequency of labor‐ and employment‐related terms compared to their US and Canadian counterparts. Sentiment ratios for US and Canadian tweets were similar, while the UK displayed nearly double the positive‐to‐negative tweet ratio. We also present a methodological intervention comparing two different sentiment analysis methods, VADER, and Zero‐Shot Learning (ZSL), to classify posts by academic librarians. ZSL significantly outperformed the off‐the‐shelf classifier, highlighting how accurate prediction is possible without annotated training data.
The Mining of China's Policies against COVID‐19 from Policy Targets and Policy Tools Perspectives
Authors: Huo Chaoguang , Li Xinru , Zhang Chenwei , Huo Fanfan 
Published: October 2023

ABSTRACTIn response to the global disaster of COVID‐19, every country has implemented various policies. China, as a developing country, has issued policies to combat COVID‐19 that could serve as a reference for future pandemic prevention and control efforts, and may offer lessons for national governance. This paper employs bibliometric methods, text mining, and network analysis to mine and characterize the evolution of China's policies against COVID‐19. Specifically, we extract policy targets and identify the policy tools from each policy, cross‐compare the policy tools used for different policy targets, and characterize their evolution during the pandemic. Our findings show that policy targets have shifted over different stages to balance epidemic prevention with economic development. We also identify several shortcomings in distribution and utilization of policy tools. To improve policy implementation, it is crucial to align policy targets with appropriate policy tools and ensure a balanced and functional approach to policy implementation.
Multi‐Modal Crisis Discourse and Collective Sensemaking on TikTok
Authors: Khoury Christy , Owen‐Smith Alexander , Joh Una , Duan Yiran , Hemsley Jeff 
Published: October 2023

ABSTRACTAn explosion at the Port of Beirut resulted in over 200 fatalities and displaced many more. Hundreds recorded and thousands shared the event, sparking discourse across social media. We provide a mixed‐methods analysis of 26 TikTok videos about the port explosion. Our study involved a semiotic analysis of the videos and a content analysis of the related 21,150 comments. The discourse surrounding the explosion extends a growing body of literature on the role of TikTok in disseminating crisis‐related information. We use a collective sensemaking framework to investigate how TikTok contributes to the understanding of an event. The authors find that Middle Eastern content creators contributed more to collective sensemaking about the event than non‐Middle Eastern creators. We additionally examine the current state of collective sensemaking on TikTok and provide platform implications.
Examining Social Media Policy and Records Management in Massachusetts Municipal Governments
Authors: Kriesberg Adam 
Published: October 2023

ABSTRACTThis paper reports on an exploratory analysis of the social media policies of municipal governments in the Commonwealth of Massachusetts. With a strong Public Records law and language clearly indicating that social media posts are government records, Massachusetts provides a framing for research into municipal policy on social media use, retention, preservation, and access. Of the 351 municipalities in the Commonwealth, just 87 have publicly accessible Social Media policies. These policies indicate that municipal governments take their responsibilities around social media records management seriously, they do not offer many specifics around how they will engage directly with posts from privately‐owned social media platforms. Implications of these findings suggest that the digital preservation community must continue to develop tools and methods to preserve social media records the support broader efforts around government transparency and accountability, as well as advocate for platforms to include features to support responsible public sector social media use.
Sharing Qualitative Interview Data in Dialogue with Research Participants
Authors: Kvale Live Håndlykken , Pharo Nils , Darch Peter 
Published: October 2023

ABSTRACTResearch data sharing is embedded in policies, guidelines and requirements commonly promoted by research funding organizations that demand data to be “as open as possible, as closed as necessary” and FAIR. This paper discusses the challenges of balancing privacy protection with data sharing in a PhD project involving long‐tail, small‐sized qualitative human subjects' data. Based on experiences and feedback from project participants, we argue that privacy protection is about respecting the participants and their self‐image. This can be achieved through dialogue and involvement of the participants building on the principles of shared stewardship. Further, we suggest that de‐identification and plain language consent materials are better at protecting privacy than anonymisation, which in a digital data environment is difficult to achieve and not necessarily a sensible approach for qualitative data, where the gold is in the details. The literature indicates that it matters to participants whether data are reused for research or other purposes, and that they trust the institutions. This supports our claim that research data services must find better solutions for restricted sharing when necessary.
In a Perfect World: Exploring the Desires and Realities for Digitized Historical Image Archives
Authors: Late Elina , Ruotsalainen Hille , Kumpulainen Sanna 
Published: October 2023

ABSTRACTThe primary goal of this paper is to explore users' desires for digitized historical image collections, examining their desires based on different use purposes and information interaction activities. In addition, we investigate the image attributes that users wish to search from the collection. To accomplish this, we conducted 21 qualitative interviews with active users of a digitized historical photograph archive. Our findings suggest that users' desires relate to three contexts: tools, collection, and socio‐organizational issues. Moreover, our results indicate that users require support for various information interaction activities, not just searching. We found that users' desires vary based on their specific use purposes, and that users prioritize conceptual access points that can already mostly be generated through automated annotation methods. Ultimately, this study contributes to a better understanding of users' real‐life image needs and offers implications for improving access to digital image collections.
Person‐OrientedOntologies Analysis for Digital Humanities Collections from a Metadata Crosswalk Perspective
Authors: Liu Rui , McKay Dana , Buchanan George 
Published: October 2023

ABSTRACTMapping between different representations of similar data is a common challenge in digital humanities (DH). In practical DH collections, the ‘person’ is an essential and centric unit and other parts could link to the ‘person’ to form the knowledge base. However, there is still no general and useful person‐oriented ontology in DH community. Many practical DH projects have developed their own ontologies by DH experts, but these ontologies are not interoperable. Therefore, it is important to explore existing biographical ontologies and develop a comprehensive person‐oriented ontology for DH.Using the metadata crosswalk method, we examined the ontologies provided for persons in three DH collections to analyze how they map onto standard ontologies such as FOAF (friend of a friend). This paper uncovers a significant and consistent gap between standard biographical ontologies and those used in practical DH collections, arriving at a set of heterogeneous problems, including different granularities of metadata. Consequently, we propose three key person‐oriented ontological types of elements, drawing on this metadata crosswalk: basic biographical elements, relational elements, and explanatory elements (such as career, connected with role and time). This metadata crosswalk provides a foundation for future matching between person‐oriented ontologies and facilitates semantic interoperability between DH collections.
An Investigation of the Use of Theories in Misinformation Studies
Authors: Ogbadu‐Oladapo Lydia , Chung Hsin‐Husan , Li Jiyuan , Chen Jiangping 
Published: October 2023

ABSTRACTThis paper examines social science and humanity theories that have been applied to studies dealing with misinformation. We identified 273 articles published from 2012 to 2023 from Web of Science, Scopus, and ScienceDirect. These articles are empirical studies that have applied one or more social science or humanity theories. Applying content analysis approach, we identified 124 theories that authors have used in their studies. These theories belong to different disciplines or fields, such as political science, psychology, communication, sociology, and economics. We discuss the top 11 theories and how they have been used to understand misinformation and its impacts. This study provides insights into understanding current misinformation studies and rich resources for information evaluation and information literacy education.
From Principles to Practice: Comparative Analysis of European and United States Ethical AI Frameworks for Assessment and Methodological Application
Authors: Pierson Cameron M. , Hildt Elisabeth 
Published: October 2023

ABSTRACTThe Z‐Inspection® Process is a form of applied research for the ethical assessment of AI systems. It is quickly establishing itself as a robust method to ethically assess AI in Europe. The process is predicated on the European Union's Ethics Guidelines for Trustworthy AI, outlining ethical principles intended to guide European AI development. In contrast, the United States has only recently released its holistic version of such guidelines, the Blueprint for an AI Bill of Rights. The aim of this paper is to assess the suitability of the Blueprint for an AI Bill of Rights as an ethical framework underpinning the use of the Z‐Inspection® Process in the United States. This paper provides preliminary findings of comparative analysis of European and United States ethical frameworks for responsible AI development. Findings outline primary ethical concepts that are shared between respective frameworks. Findings suggest the US Blueprint is suitable as an ethical framework for the Z‐Inspection® Process. There are notable omissions within the US framework which would require further development for Z‐Inspection® use. Discussion will consider opportunities for adapting Z‐Inspection® to the United States context, including contributions from the information professions and research.
“Get Our Feet Wet and Hands Dirty”: Black Community‐Based Librarianship and the Fight Against Information Poverty, 1940‐1975
Authors: Poole Alex H. 
Published: October 2023

ABSTRACTFocusing on information services to marginalized urban communities in the United States between 1940 and 1975, this paper argues that African American librarians played foundational roles in early community‐based librarianship (CBL) initiatives. In doing so, Black librarians skillfully and resourcefully fought information poverty, pushed for social and political equality, and promoted their communities’ well‐being through proactive information provision. By fighting the systemic racism throttling inner‐city Black communities, their labors constituted a seminal contribution to the post‐World War II United States Freedom Struggle. Theirs was a specifically Black CBL. Underpinned by the theoretical work of Elfreda Chatman, this heretofore hidden history offers fruitful lessons for current library and information science practice. It therefore aligns with the annual meeting theme, “Translating Information Research into Practice, Policy, and Action,” particularly its emphasis on “creating effective models of information provision, creating empathetic information services” and on “understanding the power of information to develop human happiness, equality, and wellbeing.”
Study on the Characteristics of Cross‐Domain Knowledge Diffusion from Science to Policy: Evidence from Overton Data
Authors: Ren Chao , Yang Menghui 
Published: October 2023

ABSTRACTThe cross‐domain knowledge diffusion from science to policy is a prevalent phenomenon that demands academic attention. To investigate the characteristics of cross‐domain knowledge diffusion from science to policy, this study suggests using the citation of policies to scientific articles as a basis for quantifying the diffusion strength, breadth, and speed. The study reveals that the strength and breadth of cross‐domain knowledge diffusion from scientific papers to policies conform to a power‐law distribution, while the speed follows a logarithmic normal distribution. Moreover, the papers with the highest diffusion strength, breadth, and fastest diffusion speed are predominantly from world‐renowned universities, scholars, and top journals. The papers with the highest diffusion strength and breadth are mostly from social sciences, especially economics, those with the fastest diffusion speed are mainly from medical and life sciences, followed by social sciences. The findings indicate that cross‐domain knowledge diffusion from science to policy follows the Matthew effect, whereby individuals or institutions with high academic achievements are more likely to achieve successful cross‐domain knowledge diffusion. Furthermore, papers in the field of economics tend to have the higher cross‐domain knowledge diffusion strength and breadth, while those in medical and life sciences have the faster cross‐domain knowledge diffusion speed.
A Study of COVID‐19 Information Behaviors among Black Americans
Authors: Stewart Brenton J. , Ju Boryung , Walker Jessie J. 
Published: October 2023

ABSTRACTMany Black Americans expressed skepticism and hesitancy about the Covid‐19 vaccine. The objective of this research is to examine whether there is a relationship between Covid‐19 vaccine status, Covid‐19 vaccine knowledge, and information seeking about the coronavirus. A national online questionnaire of 222 Black identified individuals completed the instrument in the Summer of 2021. We conducted data analysis using ANOVA and nonparametric correlation tests among the variables. Findings show that individuals who have received the COVID‐19 vaccine exhibited greater COVID‐19 vaccine‐related knowledge compared to people who declined the vaccine, or were hesitant adopters. There is no significant difference in the level of knowledge between those who are hesitant about the vaccine and those who declined it. Finally, there is a moderate positive correlation between COVID‐19 vaccine knowledge and engagement in COVID‐19‐related information seeking. We conclude with strategies to improve the Black American health information environment by way of practice, policy and action.
Characterizing and Early Predicting User Performance for Adaptive Search Path Recommendation
Authors: Wang Ben , Liu Jiqun 
Published: October 2023

ABSTRACTUser search performance is multidimensional in nature and may be better characterized by metrics that depict users' interactions with both relevant and irrelevant results. Despite previous research on one‐dimensional measures, it is still unclear how to characterize different dimensions of user performance and leverage the knowledge in developing proactive recommendations. To address this gap, we propose and empirically test a framework of search performance evaluation and build early performance prediction models to simulate proactive search path recommendations. Experimental results from four datasets of diverse types (1,482 sessions and 5,140 query segments from both controlled lab and natural settings) demonstrate that: 1) Cluster patterns characterized by cost‐gain‐based multifaceted metrics can effectively differentiate high‐performing users from other searchers, which form the empirical basis for proactive recommendations; 2) whole‐session performance can be reliably predicted at early stages of sessions (e.g., first and second queries); 3) recommendations built upon the search paths of system‐identified high‐performing searchers can significantly improve the search performance of struggling users. Experimental results demonstrate the potential of our approach for leveraging collective wisdom from automatically identified high‐performance user groups in developing and evaluating proactive in‐situ search recommendations.
Finding the Aha! Moment of Search: A Preliminary Examination of Insight Learning During Search
Authors: Wang Xinyue , Liu Chang 
Published: October 2023

ABSTRACTResearch in information behavior has examined search difficulties and how people learn during searches but has not fully examined how searchers solve the difficulties on their own and gain new knowledge during this process. This study introduced the concept of insight learning during search to provide a new perspective for the studies in Search as Learning (SAL) and to optimize searchers' experiences in a more efficient, innovative, and joyful way to combine search and learning. As a preliminary study, we conducted self‐reported interviews with 30 participants to collect cases of insight learning during the search process. Based on thematic analysis of the data, we summarized the benefits of insight learning during search, described the process of how aha! occurred after impasse, and identified the antecedent, key, and consequence of insight during the search process. We aimed to help generate more insights by providing three dimensions of key factors to think about. A preliminary understanding of the insights formed in this study could contribute to further discussion about learning during the search and could help design new search tools that support effective learning.
Curating for Contrarian Communities: Data Practices of Anthropogenic Climate Change Skeptics
Authors: Wofford Morgan F. , Thomer Andrea K. 
Published: October 2023

ABSTRACTThe open data movement is often touted as a sweeping strategy to democratize science, promote diverse data reuse, facilitate reproducibility, accelerate innovation, and much more. However, the potential perils of open data are seldom examined and discussed in equal measure to these promises. As we continue to invest in open data, we need to study the full spectrum of what open data facilitates in practice, which can then inform future policy and design decisions. This paper aims to address this gap by presenting an investigative digital ethnography of one contrarian community, anthropogenic climate change (ACC) skeptics, to describe how they process, analyze, preserve, and share data. Skeptics often engage in data reuse similar to conventional data reusers, albeit for unconventional purposes and with varying degrees of trust and expertise. The data practices of ACC skeptics challenge the assumption that open data is universally beneficial. These findings carry implications for data repositories and how they might curate data and design databases with this type of reuse in mind.
Using Explainable AI to Understand Team Formation and Team Impact
Authors: Xu Huimin , Saar‐Tsechansky Maytal , Song Min , Ding Ying 
Published: October 2023

ABSTRACTThe citation of scientific papers is considered a simple and direct indicator of papers' impact. This paper predicts papers' citations through team‐related variables, team composition, and team structure. Team composition includes team size, male/female dominance, academia/industry collaboration, unique race number, and unique country number. Team structures are made up of team power level and team power hierarchy. Team members' previous citation number, H‐index, previous collaborators, career age, and previous paper numbers are a proxy of team power. We calculated the mean value and Gini coefficient to represent team power level (the collective team capability) and team power hierarchy (the vertical difference of power distribution within a team). Taking 1,675,035 CS teams in the DBLP dataset, we trained the XGBoost model to predict high/low citation. Our model has reached 0.71 in AUC and 70.45% in accuracy rate. Utilizing Explainable AI method SHAP to evaluate features' relative importance in predicting team citation categories, we found that team structure plays a more critical role than team composition in predicting team citation. High team power level, flat team power structure, diverse race background, large team, collaboration with industry, and male‐dominated teams can bring higher team citations. Our project can provide insights into how to form the best scientific teams and maximize team impact from team composition and team structure.
Refueling Mental Health: Digital Reading Predicts Psychological Resilience for Left‐behind Children in Rural China
Authors: Zhao Liang , Zeng Zihan , Liu Jiayi , Zhang Yachen 
Published: October 2023

ABSTRACTBeyond the easy and equal access to massive online contents, will digital reading bring more social welfare values for disadvantaged groups? Take left‐behind children (LBC) in rural China as example, whose mental health issue has widely aroused public concern. Lacking of sufficient parental supervision and educational resources leads to insufficient development of psychological resilience and makes them vulnerable to mental health problems. As psychological resilience is a critical protective factor for maintaining mental health, in this paper, we wonder whether digital reading could perform as an alternative way with easy accessibility and numerous resources to supplement LBC's resilience development. We conducted a field questionnaire study on LBC (N = 217) and investigated the predictive effect of digital reading on psychological resilience. After controlling sociodemographic variables and common resilience protective factors, hierarchical regression results demonstrated that digital reading predicted an additional 4.3% of the variation in resilience above the control variables, indicating digital reading a positive promoter of LBC's psychological resilience. Moreover, by systematically exploring the fine‐grained digital reading variables, we also found intrinsic motivation to read and recreational digital reading (e.g., reading comics and communicating online) to be the two most stronger predictors of psychological resilience.