-
Pech, G.; Delgado, C.; Sorella, S.P.: Classifying papers into subfields using Abstracts, Titles, Keywords and KeyWords Plus through pattern detection and optimization procedures : an application in Physics (2022)
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Potnis, D.; Halladay, M.: Information practices of administrators for controlling information in an online community of new mothers in rural America (2022)
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- Abstract
- Rarely does any empirical investigation show how administrators routinely control information in online communities and alleviate misinformation, hate speech, and information overload supported by profit-driven algorithms. Thematic analysis of in-depth phone interviews with members and administrators of a "Vaginal Birth After Cesarean" (VBAC) group with over 500 new mothers on Facebook shows that the administrators make 19 choices for recurring, authoritative but evolving 19 information-related activities when (a) forming the VBAC group over Facebook for local new mothers, (b) actively recruiting women who had a VBAC or have related competencies, (c) removing doctors and solicitors from the group, (d) setting up and revising guidelines for interactions in the group, (e) maintaining the focus of the group, (f) initiating distinct threads of conversations on the group, (g) tagging experts during conversations in the group, and (h) correcting misinformation. Thirty-eight information practices of the administrators indicate their nine gatekeeping roles, seven of these roles help administrators alleviate misinformation, hate speech, and information overload. Findings also show that the management of members and their interactions is a prerequisite to controlling information in online communities. Prescriptions to social networking companies and guidelines for administrators of online communities are discussed at the end.
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Reynolds, R.; Aromi, J.; McGowan, C.; Paris, B.: Digital divide, critical-, and crisis-informatics perspectives on K-12 emergency remote teaching during the pandemic (2022)
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Verma, N.; Fleischmann, K.R.; Zhou, L.; Xie, B.; Lee, M.K.; Rich, K.; Shiroma, K.; Jia, C.; Zimmerman, T.: Trust in COVID-19 public health information (2022)
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-
Jetter, H.-C.: Informationsvisualisierung und Visual Analytics (2023)
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Womser-Hacker, C.: Informationswissenschaftliche Perspektiven des Information Retrieval (2023)
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Fan, W.-M.; Jeng, W.; Tang, M.-C.: Using data citation to define a knowledge domain : a case study of the Add-Health dataset (2023)
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Sirén-Heikel, S.; Kjellman, M.; Lindén, C.-G.: At the crossroads of logics : automating newswork with artificial intelligence-(Re)defining journalistic logics from the perspective of technologists (2023)
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Zhu, Y.; Quan, L.; Chen, P.-Y.; Kim, M.C.; Che, C.: Predicting coauthorship using bibliographic network embedding (2023)
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Wu, D.; Xu, H.; Sun, Y.; Lv, S.: What should we teach? : A human-centered data science graduate curriculum model design for iField schools (2023)
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- Abstract
- The information schools, also referred to as iField schools, are leaders in data science education. This study aims to develop a data science graduate curriculum model from an information science perspective to support iField schools in developing data science graduate education. In June 2020, information about 96 data science graduate programs from iField schools worldwide was collected and analyzed using a mixed research method based on inductive content analysis. A wide range of data science competencies and skills development and 12 knowledge topics covered by the curriculum were obtained. The humanistic model is further taken as the theoretical and methodological basis for course model construction, and 12 course knowledge topics are reconstructed into 4 course modules, including (a) data-driven methods and techniques; (b) domain knowledge; (c) legal, moral, and ethical aspects of data; and (d) shaping and developing personal traits, and human-centered data science graduate curriculum model is formed. At the end of the study, the wide application prospect of this model is discussed.
-
Belitz, C.; Ocumpaugh, J.; Ritter, S.; Baker, R.S.; Fancsali, S.E.; Bosch, N.: Constructing categories : moving beyond protected classes in algorithmic fairness (2023)
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Zhang, Y.; Wu, D.; Hagen, L.; Song, I.-Y.; Mostafa, J.; Oh, S.; Anderson, T.; Shah, C.; Bishop, B.W.; Hopfgartner, F.; Eckert, K.; Federer, L.; Saltz, J.S.: Data science curriculum in the iField (2023)
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Han, K.; Rezapour, R.; Nakamura, K.; Devkota, D.; Miller, D.C.; Diesner, J.: ¬An expert-in-the-loop method for domain-specific document categorization based on small training data (2023)
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- Abstract
- Automated text categorization methods are of broad relevance for domain experts since they free researchers and practitioners from manual labeling, save their resources (e.g., time, labor), and enrich the data with information helpful to study substantive questions. Despite a variety of newly developed categorization methods that require substantial amounts of annotated data, little is known about how to build models when (a) labeling texts with categories requires substantial domain expertise and/or in-depth reading, (b) only a few annotated documents are available for model training, and (c) no relevant computational resources, such as pretrained models, are available. In a collaboration with environmental scientists who study the socio-ecological impact of funded biodiversity conservation projects, we develop a method that integrates deep domain expertise with computational models to automatically categorize project reports based on a small sample of 93 annotated documents. Our results suggest that domain expertise can improve automated categorization and that the magnitude of these improvements is influenced by the experts' understanding of categories and their confidence in their annotation, as well as data sparsity and additional category characteristics such as the portion of exclusive keywords that can identify a category.
-
Mehra, B.; Jabery, B.S.: "Don't Say Gay" in Alabama : a taxonomic framework of LGBTQ+ information support services in public libraries - An exploratory website content analysis of critical resistance (2023)
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- Abstract
- The American state of Alabama has recently developed a national notoriety as a toxic place for lesbian, gay, bisexual, transgender, and questioning/queer (LGBTQ+) people owing to several laws that have supported human rights violations and denied their civil liberties. This case study assesses how Alabama's public libraries are providing culturally relevant web access and coverage to LGBTQ+ information to meet their needs/concerns in a region that is oppressive to sexual and gender minorities. In the process, it illustrates public libraries' emerging role as simultaneously impotent to the majority's infringements, while finding creative ways to serve as counter narrative spaces of resistance representing "voices" of, and from, the margins. This exploratory assessment is based on documenting web-based information for LGBTQ+ people in Alabama's 230 public libraries and identifies seven intersectional examples of information offerings, categorized into three groupings: (a) information sources (collections, resources); (b) information policy/planning (assigned role, strategic representation); (c) connections (internal, external, news/events). It provides a taxonomic framework with representative examples that challenge the regional stereotype of solely deficit marginalization. The discussion provides new opportunities to build collaborations of sharing within Alabama's public library networks to better address LGBTQ+ concerns and inequities in their local and regional communities.
-
Zhang, D.; Wu, C.: What online review features really matter? : an explainable deep learning approach for hotel demand forecasting (2023)
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Zhou, Z.; Jin, X.-L.; Hsu, C.; Tang, Z.: User empowerment and well-being with mHealth apps during pandemics : a mix-methods investigation in China (2023)
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-
Zakaria, M.S.: Measuring typographical errors in online catalogs of academic libraries using Ballard's list : a case study from Egypt (2023)
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- Abstract
- Typographical errors in bibliographic records of online library catalogs are a common troublesome phenomenon, spread all over the world. They can affect the retrieval and identification of items in information retrieval systems and thus prevent users from finding the documents they need. The present study was conducted to measure typographical errors in the online catalog of the Egyptian Universities Libraries Consortium (EULC). The investigation depended on Terry Ballard's typographical error terms list. The EULC catalog was searched to identify matched erroneous records. The study found that the total number of erroneous records reached 1686, whereas the mean error rate for each record is 11.24, which is very high. About 396 erroneous records (23.49%) have been retrieved from Section C of Ballard's list (Moderate Probability). The typographical errors found within the abstracts of the study's sample records represented 35.82%. Omissions were the first common type of errors with 54.51%, followed by transpositions at 17.08%. Regarding the analysis of parts of speech, the study found that 63.46% of errors occur in noun terms. The results of the study indicated that typographical errors still pose a serious challenge for information retrieval systems, especially for library systems in the Arab environment. The study proposes some solutions for Egyptian university libraries in order to avoid typographic mistakes in the future.
-
Jia, R.M.; Du, J.T.; Zhao, Y.(C.): Interaction with peers online : LGBTQIA+ individuals' information seeking and meaning-making during the life transitions of identity construction (2024)
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Gu, D.; Liu, H.; Zhao, H.; Yang, X.; Li, M.; Lian, C.: ¬A deep learning and clustering-based topic consistency modeling framework for matching health information supply and demand (2024)
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Yu, L.; Fan, Z.; Li, A.: ¬A hierarchical typology of scholarly information units : based on a deduction-verification study (2020)
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- Date
- 14. 1.2020 11:15:22
