bluuebunny/crossref_metadata_2025_split huggingface dataset:
Considering individual and community contexts within information pedagogy, scholarship, and practice
Authors: Roeschley Ana , Buchanan Sarah A. , Burke Mary , Graf Ann , Zavalina Oksana L. 
Published: October 2020

AbstractContext—“the complex web of social relations which [the individual] inhabits” (Spratt & Florian, Teaching and Teacher Education, 2015, 49, 89–96, p. 90) touches everything, including individual and group circumstances, positionalities, feelings, experiences, and environments. Context shapes not only how we communicate information in the field and in the classroom, but also several significant processes: searching for information, accessing information, and the interpretation and use of information (Encheva, Journal of Library Administration, 2016, 56, 595–602; Mitchell, Technical Services Quarterly, 2017, 34, 283–296; Yeo et al., The Annals of the American Academy of Political and Social Science, 2015, 658, 172–191). Furthermore, such contexts affect how information scientists teach and conduct their research and how information (in a classroom setting or otherwise) can be delivered to individuals and communities for action. How we, as information scholars and professionals, take individual and community contexts into account will affect our work. Issues of context will be thoroughly interrogated from a number of different perspectives by both the panelists and audience members in this interactive and participation‐based session.
Standing out in the academic LIS job market: An interactive panel not just for doctoral students
Authors: Agarwal Naresh Kumar , Oh Kyong Eun , Williams Rachel D. , Freeburg Darin , Rosenbaum Howard , Kwaśnik Barbara H. 
Published: October 2020

AbstractThis proposal builds on the previous successes of the interactive panels sponsored by the ASIS&T Special Interest Group on Education (SIG ED) at the 2017, 2015, 2013, 2012, and 2011 annual meetings. The all‐new 2020 panel features faculty in various stages of their academic careers, including a relatively new assistant professor, one who has just completed the tenure process, two associate professors, a full professor, and a professor emerita. The panelists are prepared to address audience questions on topics such as the traditional academic job market, alternative career paths including post‐doctoral opportunities, development of personal research agendas, challenges of online instruction (Sithole et al., International Journal of Online Pedagogy and Course Design, 2019, 9, 62–77), as well as the challenges and disruption caused in higher education due to the COVID‐19 situation (Amour, Coronavirus News Roundup for April 21, 2020). The panel discussion will focus around the academic job market and how applicants can present themselves in the best light depending upon the type of position sought. Doctoral students will gain valuable insights on finishing their dissertation, weighing postdoctoral opportunities, and preparing for their first academic position. The panel will be useful not only to doctoral students but also to postdocs, adjuncts, and assistant‐to‐associate‐to‐full professors, as the panelists share their insights and experience through various career stages.
ASIST AM 2020. SIG HFIS panel proposal April 27, 2020
Authors: La Barre Kathryn , Burke Colin , Buckland Michael K. , Gorichanaz Tim 
Published: October 2020

AbstractA progress report and discussion on the preservation of, and access to, historical resources relating to the history of ASIST and of Information Science and Technology generally and guidance for members interested on opportunities and methods.
Making as imaginative crossroads: Ghanaian makers and the geopolitics of technological progress
Authors: Avle Seyram 
Published: October 2020

AbstractThis paper is about the politics of technological progress as it is being played out among a loose network of Ghanaian makers. It unpacks how the practice of ‘making’ unfolds as a site for positioning the self and the nation within a global imaginary of techno futures. The paper argues, first, that ‘making’ in Ghana is emblematic of a crossroads of imaginative possibilities for technological design and production, and second, that this marks a distinct turn in the politics of technological progress, particularly when situated against ongoing econo‐political negotiations between the Global south, the West, and China.
Identifying data‐focused curriculum in worldwide iSchools: Preliminary data acquisition for Asia‐Pacific and European members
Authors: Jeng Wei , Tsai Fu‐Hsuan , Lee Jian‐Sin 
Published: October 2020

AbstractThis paper reports the data cleansing and labeling procedures for identifying 215 data‐focused courses from 1,627 courses offered in 22 iSchool members as of 2019. This preliminary data acquisition is to serve as a starting point for further conducting regular, greater‐scaled data acquisition in data‐focused curriculum across all the iSchools in the near future. The challenges we encountered preliminary data collection include course catalog unavailability, disambiguation, and lack of data for investigating the evolution of courses. Next steps of this study are to expand our data collection to all the iSchool members and create an interactive data dashboard with periodically automatic data acquisition over time. We anticipate such public datasets and interactive visualizations are beneficial for iSchool educators and researchers who are interested in the development of data‐focused curricula, as well as potential students.
Targeting precision: A hybrid scientific relation extraction pipeline for improved scholarly knowledge organization
Authors: Jiang Ming , D'Souza Jennifer , Auer Sören , Downie J. Stephen 
Published: October 2020

AbstractKnowledge graphs have been successfully built from unstructured texts in general domains such as newswire by leveraging distant supervision relation signals from linked data repositories such as DBpedia. In contrast, the lack of a comprehensive ontology of scholarly relations makes it difficult to similarly adopt distant supervision to create knowledge graphs over scholarly articles. In light of this difficulty, we propose a hybrid approach to extract scientific concept relations from scholarly publications by: (a) utilizing syntactic rules as a form of distant supervision to link related scientific term pairs; and (b) training a classifier to further identify the relation type per pair. Our system targets a high‐precision performance objective as opposed to high recall, aiming to reduce the noisy results albeit at the cost of extracting fewer relations when building scholarly knowledge graphs over massive‐scale publications. Results on two benchmark datasets show that our hybrid system surpasses the state‐of‐the‐art with an overall 60% F1 score led by the nearly 15% precision boost in identifying related scientific concepts. We further achieved an overall F1 in the range 34.1% to 51.2%, on relation classification, per experimental dataset.
“When someone sees me, I am nothing of the norm”: Examining the discursive role power plays in shaping LGBTQ+ health information practices
Authors: Kitzie Vanessa L. , Wagner Travis L. , Vera A. Nick 
Published: October 2020

AbstractThis paper examines how discursive power shapes LGBTQ+ community health information practices. Informed by analysis of 10 information world maps drawn by SC LGBTQ+ community leaders, our findings indicate that while community can be a valuable construct to reject mainstream discourses of regulation and correction, it inevitably is fraught and not representative of all LGBTQ+ individuals. Findings can inform strategies for community leaders to facilitate more equitable information flow among members by identifying key structural elements impeding this flow at the community level.
Don't judge a journal by its cover?: Appearance of a Journal's website as predictor of blacklisted Open‐Access status
Authors: Liang Lizhen , Acuna Daniel 
Published: October 2020

AbstractThe nature of scientific research has motived an open‐access model of publication supported by article processing fees. Under this rapidly evolving environment and financial incentives, some dubious venues would publish almost anything—for a fee. Many entities keep track of the standards of these new journals, “blacklisting” those deemed problematic. Anecdotal evidence suggests that blacklisted journals tend to have websites with subpar appearance (e.g., old web technologies, unprofessional design). In this work, we systematically explore whether this anecdotal evidence is true. In particular, we evaluate the websites of journals whitelisted and un‐whitelisted by the Directory of Open Access Journals (DOAJ). We use a convolutional neural network to predict whether a journal is whitelisted based on a screenshot of its website and analyze the factors that predict one output vs. the other. Our results show that appearance is indeed a predictive factor, achieving a medium performance (AUC of 0.736). Further, our interpretation suggests that the network considers whitelisting those websites with a table of content, social media links and packed content. Conversely, our model mistakenly whitelists blacklisted journals hosted by Elsevier and blacklists whitelisted websites with serif fonts and non‐Latin characters.
Social media users' behavior of setting limits in information sharing: A heuristic study based on WeChat moments
Authors: Zheng Guangchun , Zhao Zelong 
Published: October 2020

AbstractSocial media has become an important platform for users to share information in recent years. A large number of active users are sharing their daily lives and social issues in various platforms. At the same time, an interesting phenomenon is that functions like "three days visible" and "selected contacts visible" are also popular among users. In order to explore the information sharing behavior from the perspective of why users set these limits, we made a 15‐day observation on 21 college students' sharing behavior on WeChat Moments and conducted in‐depth interviews with 16 of them. The study summarizes the characteristics of users who set different types of limits from the observation. Sixteen factors influencing the behavior of setting limits were identified and categorized into three dimensions: social, psychological and technical.
Live writing: Modeling a creative activity in a virtual small world
Authors: Kizhakkethil Priya 
Published: October 2020

AbstractLooking at fan fiction writing as a personally meaningful activity, this paper covers the preliminary stage in developing a model to look at this creative activity based on extant document experience literature and models. Initial data analysis points to the importance of the social context in which the activity takes place, highlighting the role that social ties in the form of fellow readers play in the process. Exploratory in nature and coming as a byproduct of data analysis, it is hoped this will aid conceptual development in information and document experience, while adding to studies that look at personally meaningful information activities.
Healthy diet recommendation via Food‐Nutrition‐Recipe Graph mining
Authors: Li Kequan , Jiang Zhuoren , Wang Haijiao , Liu Xiaozhong 
Published: October 2020

AbstractGood nutrition and balance dietary pattern play vital roles of leading a healthy lifestyle. Prior studies showed that the healthy diet can successfully reduce the risk of chronic diseases (e.g., type 2 diabetes and cancer) and bring other well‐documented benefits. Existing food recommendation models, however, often solely rely on user's feedback (e.g., click and purchase data), which aims to optimize Click‐Through Rate (CTR) but ignores the importance of health needs of users. Intuitively, a healthy diet recommendation requires a comprehensive consideration of different kinds of information, such as nutrition, ingredients and cooking methods. In this study, by collecting the data from FoodData Central (FDC), recipe websites, and scientific literature, we construct a heterogeneous graph, Food‐Nutrition‐Recipe Graph (FNRG), by integrating information of nutrition, food (ingredients), and recipes. A random walk based graph mining approach is proposed to meet the health needs of users. Experiments results show that the proposed method can successfully address the health information needs for people who suffer from chronic diabetes.
Seasonal characterization of diet discussions on Reddit
Authors: Money Victoria , Karami Amir , Turner‐McGrievy Brie , Kharrazi Hadi 
Published: October 2020

AbstractTo monitor public opinions on diet, large survey data is commonly used though costly and time consuming. Social media has become a mainstream channel of communication and has drastically grown in popularity, connecting users to a ready stream of health information. While the literature has provided valuable information on the use of social media for health, a broader perspective informed by different types of social media platforms would be highly beneficial. Diet has been extensively explored on a few mainstream platforms, further informing public health research. However, diet conversations on Reddit have only been studied within a narrow scope, looking at specific sub‐communities. This study aims to characterize diet‐related posts and their seasonal patterns using a mixed method approach. We collected more than 500,000 posts with subsequent comments from Reddit over the course of a year. Our findings show that Reddit users across all sub‐communities primarily discussed health promotion, fitness plans, a healthy lifestyle, diet and fitness progress, food experiences, weight loss, as well as vegan and vegetarian diets. In addition, seasonal differences based on the weight of most topics, were found to be significant (p < .05).
GotFunding: A grant recommendation system based on scientific articles
Authors: Zeng Tong , Acuna Daniel E. 
Published: October 2020

AbstractObtaining funding is an important part of becoming a successful scientist. Junior faculty spend a great deal of time finding the right agencies and programs that best match their research profile. But what are the factors that influence the best publication–grant matching? Some universities might employ pre‐award personnel to understand these factors, but not all institutions can afford to hire them. Historical records of publications funded by grants can help us understand the matching process and also help us develop recommendation systems to automate it. In this work, we present GotFunding (Grant recOmmendaTion based on past FUNDING), a recommendation system trained on National Institutes of Healthʼs (NIH) grant–publication records. Our system achieves a high performance (NDCG@1 = 0.945) by casting the problem as learning to rank. By analyzing the features that make predictions effective, our results show that the ranking considers most important (a) the year difference between publication and grant, (b) the amount of information provided in the publication, and (c) the relevance of the publication to the grant. We further discuss the implications and future improvements to this work.
Data management plan scorecard
Authors: Bishop Bradley Wade , Ungvari Judit , Gunderman Hannah , Moulaison‐Sandy Heather 
Published: October 2020

AbstractData and how that data are organized, accessed, and used, remain central to the advancement of science, as evidenced through calls for reproducibility and demands to enable re‐use. Data management plans describe the roles, responsibilities, and activities for managing data during and after research. This paper presents the development of a data management plan scorecard. The assessment of data management plans provides one measure to evaluate how data are planned to be shared and a first step down the road towards shareable data. The scorecard provides a widely‐applicable, scalable, and simple tool to assess completeness and quality of 15 data elements commonly required in all data management plans.
Understanding and fighting disinformation and fake news: Towards an information behavior framework
Authors: Agarwal Naresh Kumar , Alsaeedi Farraj 
Published: October 2020

AbstractCurrent efforts to fight misinformation, disinformation, and fake news have been isolated and inadequate. Most models of information behavior deal with information, and not misinformation or disinformation per se. We seek to disambiguate the phenomenon by clarifying terms and highlighting efforts towards fighting fake news. Building upon Karlova and Fisher's, Information Research, 2013, 18, 1–17 model, we propose a new information behavior framework. It should guide practitioners and researchers in library and information science and beyond, and other stakeholders in understanding the phenomenon, and fighting against it.
Teachers' and students' attitudes towards information security: A qualitative study
Authors: Reich Nurit , Aharony Noa , Bouhnik Dan 
Published: October 2020

AbstractThe goal of this research study is to examine teenager's and educational staff's attitudes towards the issue of information security (IS). The study focuses on teenagers and on educational staff from high schools from varied socio‐economic groups. The goal of this study is to eventually instigate a cultural change in the area of IS, therefore, it is important to understand the motivations behind end users' actions. The research findings indicate a clear connection between students' and teachers' evaluation of their information sources and the threat they feel by information risks. This, in turn affects their willingness to apply IS measures. In addition, the necessity of integration of IS into the educational curriculum emerged, as it constitutes an indispensable skill in the 21st century.
Performance evaluation of feature selection methods for aircraft hard landing incident Prediction
Authors: Ding Jingjia , Yuan Xiaoqun , Li Hongxu 
Published: October 2020

AbstractThis poster aims to find out a quick and accurate way for feature selection in hard landing prediction. To make the performance evaluation effective, a general evaluation framework is designed. Then seven classic feature selection methods are carried out by applying four classification algorithms, nine comparison metrics on a real QAR dataset with a total of 3,040 instances. At last, TOPSIS is employed for overall performance evaluation. Results indicate that the data‐driven category is preferable in feature selection of hard landing prediction. Moreover, Gradient Boosting Decision Tree combining with K‐Nearest Neighbor classifier on a balanced training dataset outperforms among 56 possible combination models.
Successful trolling on Reddit: A comparison across subreddits in entertainment, health, politics, and religion
Authors: Fichman Pnina , Sharp Samantha 
Published: October 2020

AbstractReddit can be perceived as a heaven for trolls, but how much of the site actually contains trolling? How successful are these troll comments on subreddits, and do the types of subreddits affect the success of comments? Based on an analysis of 16,516 comments from 208 posts from 13 subreddits in four categories (entertainment, health, politics, and religion), we found that trolling comments account for less than 1 % of all comments, and most of these trolling comments were not successful, regardless of category. While one out of five trolling comments were successful, only two out of a thousand comments were successful trolling. We also found that politics subreddits attract more trolling than subreddits in the other categories, and health subreddits are less susceptible to successful trolling than other subreddits.
PreKindergarten and kindergarten virtual school programs under COVID‐19: A two‐case comparative study
Authors: Hu Zhan , Lu Wenqing 
Published: October 2020

AbstractThis case study aims to explore and compare two cases of virtual school programs, one asynchronized and one synchronized, regarding their pedagogical design and operationalization. The authors collected data from observations of learning activities and analyses of teaching materials. A comparison was drawn on the benefits and drawbacks of two teaching modes, and the strategies the instructors use to address the limitations.
Good, bad, and practical: Exploring human memory in everyday information behavior
Authors: Lee Lo , Ocepek Melissa G. , Makri Stephann 
Published: October 2020

AbstractThis study investigates how memory affects everyday information behavior. Previous literature generally examined memory as a broad category, missing to consider how the variance in memory influences information behavior. To bridge this gap, we interviewed and observed 10 arts and crafts hobbyists when they navigated a creative shopping space designed to spark serendipity. Findings demonstrate the distinction between memory, including positive, negative, and practical, along with their potential impacts on everyday information behavior. This work provides a novel analysis of memory, shedding new light on the subtlety of memory in information behavior research.
Libraries and archives of tomorrow: How future information professionals perceive AI
Authors: Li Lan , Fleischmann Kenneth R. 
Published: October 2020

AbstractHow do future librarians and archivists envision AI and its role in libraries/archives? The goal of this paper is to begin to answer this question using exploratory findings from interviews with library and archives students. Recommendations include developing courses that combine technical aspects of how to leverage AI and library/archives content, to help students build libraries and archives of tomorrow.
A theoretical analysis of independent business owners' preferences for informal information sources
Authors: Chan Joel , Brier Jonathan , Farhadi Zahra , Lee Myeong , Janzen Shawn , Chi Wei‐Wei , Fellows Andrew , Winter Susan 
Published: October 2020

AbstractIndependent business owners often prefer informal information sources to formal ones such as library collections. Part of these preferences is rational because contextual and application‐oriented information is usually available from informal sources, which are theoretically the best matches for this kind of information. This suggests that, in addition to outreach strategies, efforts to integrate informal sources of business‐relevant information can improve public libraries' ability to support independent business owners' information needs. Through interviews with business information providers and users, we explore the multi‐dimensional space of information needs in independent businesses.
What are the source selection criteria?: Lessons from mothers' health information‐seeking behavior
Authors: Lee Hanseul S. 
Published: October 2020

AbstractThis research investigated mothers' health information source selection behavior, specifically, what criteria they consider important for their source selection. Online surveys were completed by 851 mothers: 255 U.S.‐born mothers, 296 Korean‐born mothers, and 300 Korean immigrant mothers living in the United States. Results indicated that health information perceived to be “accurate/credible”, “easily accessible”, and “active/updated” tended to be used most often. Moreover, when a series of one‐way analysis of variance (ANOVA) tests was conducted, statistical group differences were identified in 9 out of 10 source characteristics (e.g., “accurate/credible”, “active/updated”, “interactive/able to respond” etc.). Implications for information professionals when providing health information services are also discussed.