bluuebunny/crossref_metadata_2025_split huggingface dataset:
“Being a butt while on the internet”: Perceptions of what is and isn't internet trolling
Authors: Chen Yimin 
Published: February 2019

ABSTRACTThe term “internet trolling” has come to encompass a wide range of behaviours, ranging from abusive speech and hacking to sarcastic humour and friendly teasing. While some of these behaviours are clearly antisocial and, in extreme cases, criminal, others are harmless and may even have potential prosocial functions. This study is an attempt to disambiguate some of the competing and contrary interpretations of internet trolling by examining the perspectives of avid internet users for whom trolling is a familiar part of their online lives. Through data collected from in‐depth, semi‐structured interviews, a multifaceted picture of trolling emerges that is at odds with previous media characterizations of internet trolls as merely hateful bullies. On the contrary, most participants in this study did not consider trolling to be a serious problem and many did not consider harmful interactions to be trolling at all. This paper describes some of the defining characteristics of internet trolling in order to differentiate the harmful behaviours from the harmless. This work aims to contribute to the growing body of literature on internet trolling in the hopes of informing regulatory policy and educational initiatives concerning internet use and safety.
Toward a value‐analytic approach to information standards
Authors: Dobreski Brian 
Published: February 2019

ABSTRACTWhile developments in information technology enable exciting new potentials, they may, in the process, inadvertently violate important values such as autonomy or privacy. Responsible, ethical approaches to technology warrant the use of critical perspectives in evaluating our technological practices and artifacts. Key among such artifacts are information standards, influential documents that represent and perpetuate community agreements on ideal practice. In critically examining standards, values represent a promising conceptual lens. This paper advances the use of value analysis on information standards, particularly those devoted to knowledge organization. Value analysis is a methodological approach that guides the elicitation of values from artifacts. Two case studies demonstrate the application of value analysis to knowledge organization standards and their resulting data, and show how values and their implications can be discerned from these information artifacts. Value analysis must next be extended beyond standards as documents to examine the fuller ecosystem within which information standards are situated and enacted in order to fully understand their implications, uphold important community values, and maintain ethical information practices.
Bibliotelemetry of information and environment: Evaluation of an IoT‐powered recommender system
Authors: Hahn Jim 
Published: February 2019

ABSTRACTInternet of Things (IoT) infrastructure within the physical library environment is the basis for an integrative, hybrid approach to digital resource recommenders. The IoT infrastructure provides mobile, dynamic wayfinding support for items in the collection, which includes features for location‐based recommendations. A modular evaluation and analysis herein clarified the nature of users' requests for recommendations based on their location and describes subject areas of the library for which users request recommendations. The modular mobile design allowed for deep exploration of users' bibliographic identifiers throughout the global module system, serving to provide context to the browsing data that are the focus of this study. Bibliotelemetry is introduced as an evaluation method for IoT middleware within library collections.
“Life never matters in the DEMOCRATS MIND”: Examining strategies of retweeted social bots during a mass shooting event
Authors: Kitzie Vanessa L. , Mohammadi Ehsan , Karami Amir 
Published: February 2019

ABSTRACTThis exploratory study examines the strategies of social bots on Twitter that were retweeted following a mass shooting event. Using a case study method to frame our work, we collected over seven million tweets during a one‐month period following a mass shooting in Parkland, Florida. From this dataset, we selected retweets of content generated by over 400 social bot accounts to determine what strategies these bots were using and the effectiveness of these strategies as indicated by the number of retweets. We employed qualitative and quantitative methods to capture both macro‐ and micro‐level perspectives. Our findings suggest that bots engage in more diverse strategies than solely waging disinformation campaigns, including baiting and sharing information. Further, we found that while bots amplify conversation about mass shootings, humans were primarily responsible for disseminating bot‐generated content. These findings add depth to the current understanding of bot strategies and their effectiveness. Understanding these strategies can inform efforts to combat dubious information as well as more insidious disinformation campaigns.
Do longer articles gather more citations? Article length and scholarly impact among top biomedical journals
Authors: Lyu Peng‐hui , Wolfram Dietmar 
Published: February 2019

ABSTRACTPublications represent the most tangible products of research and scholarly communication. Growing pressures placed on researchers to increase their publication counts may encourage them to publish shorter papers, so that more publications may be generated from the same project and program. Is this the case? Furthermore, do shorter papers garner the same impact as longer papers as measured by the citations received? This research examines the relationship between article length and citations received by articles published in three prominent top biomedical research journals over the lifetime of the journals. The study reveals that instead of getting shorter, articles in these journals are getting longer over time. Longer articles in these journals, on average, attract more citations, and are therefore considered more impactful.
Using social and topical distance to analyze information sharing on social media
Authors: Mongeon Philippe 
Published: February 2019

ABSTRACTThe diversity of social media platforms, users and uses has long been a challenge for social media research. The difficulty of inferring meaning from social media acts is also, and perhaps more importantly, due to the fact the even identical acts that are in appearance identical and that occur on the same platforms and contexts can have diverse meanings. This paper proposes a model based on the concepts of social and topical distance that can help shed light on the possible meanings of a social media act. The model considers the social relationship between the content creator and the user, as well as the topical relationship between the content the user's main topics of interest. It is demonstrated by analyzing a set of papers published in JASIST that were tweeted by researchers. The results show that the model can help differentiate between different types of heterogeneous social media acts and users or be used to characterize the attention that a specific content has received on social media.
Community‐driven user evaluation of the Inuvialuit cultural heritage digital library
Authors: Shiri Ali , Stobbs Robyn 
Published: February 2019

ABSTRACTPreservation of and access to aboriginal and indigenous cultural heritage is emerging as a key area of information science research and development. In this study, we report on a community‐driven user interface evaluation within a cultural heritage digital library that was developed for the Inuit communities in the Inuvialuit Settlement Region in Canada's north. The study adopted a culturally‐aware, multi‐method and multidisciplinary user evaluation framework to examine the usability and usefulness of the Digital Library North user interface. The three‐phase user evaluation study consisted of such methods and approaches as environmental scanning, surveys, interviews, information audits, information tables, open houses as well as various community workshops. The iterative nature of the digital library interface development and usability evaluation provided rich and varied feedback and suggestions for the improvement of the user interface and its searching, browsing and navigation functionalities. The three thematized categories that emerged from the diverse data collected over the course of the three phases include user engagement, search and browse, and interface features. The study found that the development of cultural heritage digital libraries and their user interfaces for northern communities requires diverse, multidisciplinary and longitudinal methodologies that ensure their success, and more importantly, their sustainability and acceptance.
Automatic assessment of mental health using phone metadata
Authors: Singh Vivek K. , Long Teng 
Published: February 2019

ABSTRACTAn accurate and timely assessment of mental health of individuals is an important building block toward healthy well‐functioning societies. Traditional methods for assessing mental health and wellbeing are surveys administered by health‐care professionals. Such surveys are labor intensive, costly, and typically intermittent. With the growth in mobile sensing, health informatics, and data science, this work argues a case for using mobile phone meta‐data and data analytics to automatically infer an individual's mental health. Specifically, based on a clinically utilized health measure as ground truth (MHI‐5) and ten‐week phone usage metadata corpus for 45 participants we report that automated machine learning algorithms utilizing phone based features can achieve reasonable accuracy (around 80%) at automatically classifying the level of mental health of an individual. Results could pave way for cheaper, automated, health assessments with timely escalations to mental health professionals and interventions when required.
The impact of fielding on retrieval performance and bias
Authors: Wilkie Colin , Azzopardi Leif 
Published: February 2019

ABSTRACTWithin many domains, such as news, medicine and patent, documents contain a variety of fields such as title, author, body, source, etc. As such fielded retrieval models that query across these fields are often employed. It is largely presumed that fielding provides a better representation of the document and offers more control when querying and that this will lead to improved retrieval performance. However, depending on how the fields are weighted and if the fields are populated, the retrieval algorithm may unduly favour certain documents over others. This is known as algorithmic bias and it can be detrimental to retrieval systems performance. In this paper, we explore the impact of fielding on retrieval bias and performance across a variety of TREC News Test Collections. We perform an extensive large‐scale analysis on two types of fielded retrieval model variations that are based on the popular BM25 retrieval algorithm where either: fields are scored independently and then combined (Model 1), or fields are first combined and then scored (Model 2). Our findings show that for Model 1 fielding, a strong correlation exists between retrieval bias and performance such that as title fields are weighted more heavily, bias increases, while retrieval performance decreases. When weighting is applied to content‐based fields, performance increases as bias decreases, showing that relying more on content may be favourable in terms of fairness and performance. On the other hand, for Model 2 fielding, the relationship between retrieval bias and performance is more complex. But, crucially we show that Model 2 fielding results in lower retrieval bias and greater performance than Model 1 fielding. And, we observed that under Model 1, news articles without titles are substantially less retrievable (i.e. more susceptible to algorithmic bias). These findings have serious ramifications as many popular Open Source Information Retrieval frameworks, commonly used by professional searchers, use the default implementation of Model 1 for their fielded search capability. This research shows the importance of analysing retrieval algorithms with respect to both bias and performance to ensure they minimize any unwanted or unintended biases when maximising performance. Further work is required to examine this phenomenon in more detail and to design fielded retrieval models that have the advantages of control and performance without detrimental biases.
Beyond mandatory use: Probing the affordances of social media for formal learning in the voluntary context
Authors: Zhou Quan , Lee Chei Sian , Sin Sei‐Ching Joanna 
Published: February 2019

ABSTRACTThe integration of social media in formal education has received consistent interest in the recent decade. While most studies have focused on the mandatory use of social media as pedagogical tools, less explored is the context in which students voluntarily choose and use social media to supplement their formal learning. In this study, we employ an affordance approach to exploring the resources in social media that students perceive and use voluntarily for formal learning. Drawing on a qualitative study using focus groups of university students, we identified ten categories of social media affordances for learning in the voluntary context: association, editability, immediacy, interactivity, manageability, modality, persistence, scalability, searchability, and visibility. The affordances mentioned most frequently by students were visibility, persistence, and interactivity. These identified affordances encompass the major resources offered by social media for learning in the voluntary context, and provide insights into understanding, evaluating, and guiding social media adoption in formal education as well as lifelong learning. This paper concludes by presenting limitations and implications for research and practice.
Online targeting of researchers/academics: Ethical obligations and best practices
Authors: Greyson Devon , Cooke Nicole , Gibson Amelia , Julien Heidi 
Published: February 2019

ABSTRACTTargeted online harassment of academics has been on the rise in the US and internationally. Such harassment ranges from online threats and hacking to doxxing and organized campaigns to discredit one's work. It has now become an often‐unrecognized part of the job for academics—particularly socially marginalized academics and those who study controversial topics such as race, ethnicity, gender, and sexuality—to work to protect themselves from such attacks. With this increase in intimidation attempts, new questions for the profession arise regarding what skills we should be instilling in trainees, whether professional association and academic units should be taking explicit positions or action on researcher/faculty harassment, and the obligations of our employers with regard to defending faculty, staff, and students. This panel will describe targeted online harassment of academics, discuss models for individual and institutional response, and raise questions for the profession as a whole. Sponsored by SIG ED.
Glittering in the dark: Memory, culture, and critique in light of the history of information
Authors: La Barre Kathryn , Buckland Michael K. , Arafat Sachi 
Published: February 2019

ABSTRACTAn ethical and human‐centered approach to Information Science requires rigorous, historically‐informed analysis of both the resources that inform this discipline and the cultural role it inhabits. This session will present and discuss significant recent developments in the history and foundations of the field. ASIST has formally established a new volunteer administrative position of ASIST Curator. Kathryn La Barre, the newly‐appointed Curator, will describe the role and responsibilities of this position and assess the state of Information Science history in relation to the conference themes. In two complementary reports: Michael Buckland will examine theoretical accounts of the materials made use of in Information Science; and Sachi Arafat will explain why Information Retrieval and Information Science should be integrated and rethought as a science of technology‐mediated experience, and how this new kind of science relates to the pre‐modern memory arts tradition.
Confronting the expanse of data education: From local open data to global cyberinfrastructure
Authors: Palmer Carole , Greenberg Jane , Lin Xia , Howison James , Weber Nicholas , Oh Sam , Murillo Angela 
Published: February 2019

ABSTRACTAs the demand for data science and data‐intensive capabilities grows in all sectors, educators in schools of information and library and information science are working to deepen and expand their programs to meet workforce expectations. This panel will examine current trends and investments in data education and professionalization, with an emphasis on the unique contributions information professionals bring to the data workforce. Educators will present on current initiatives that represent the breadth and complexity of preparing information professionals for work in data intensive environments. Their unique approaches anticipate and respond to workforce demand, address significant educational challenges, and offer models for making progress within the varying contexts of schools in different regions of the U.S. and Asia. Half of the session will be reserved for audience engagement designed to leverage and share the wealth of experience of the educators and students in attendance. The exchange will generate ideas about new directions and successful approaches that educators can apply to set priorities, position their programs, and collaborate to enrich and provide leadership in data education.
Data type and data source preferences for six social sciences subjects in quantitative data reuses
Authors: Lin Chi‐Shiou , Wang Yi‐Yu 
Published: February 2019

ABSTRACTIn this paper we describe an analysis of the most frequently used data types intersecting data sources in quantitative data re‐ uses in six major social science subject disciplines. Using a sample of 1,319 papers from 40 social sciences journals in Taiwan, this study reveals the reuse patterns among the subjects for a time span of 15 years. The finding shows that the six sub‐ jects formed three groups, each with similar data reuse patterns. It also shows that business data from governments, series surveys from academic institutions, and series surveys from governments are the three most important kinds of data for social scientists, but the major users of those data differed.
Word‐building and world‐stealing: Taking, making, and playing across fictional and material borders
Authors: Stobbs Robyn , Oak Arlene 
Published: February 2019

ABSTRACTInformation practices and experiences are complex and layered; they can traverse borders between fiction and reality and the material and immaterial. This poster will present a visual representation of an ongoing narrative analysis from a pilot study of information practices involved in creative engagements with fictional worlds.
Readiness for and barriers to implementation of linked data by Pakistani libraries
Authors: Warraich Nosheen Fatima , Rorissa Abebe 
Published: February 2019

ABSTRACTThe extant literature adequately documents Linked Data initiatives primarily undertaken and implemented in information environments and cultural institutions in Europe and North America. Information professionals play crucial roles in implementing those initiatives that can potentially improve access to knowledge and information. In order to gauge the level of Linked Data application in libraries and have a broader and global understanding, it is important that researchers also explore this topic in the context of libraries in developing countries. Accordingly, our aim in the current study was to assess the willingness and level of interest in the application of Linked Data by librarians in Pakistan as well as determine their perceptions of the barriers to Linked Data application in their respective libraries. A survey of information professionals in Pakistan showed a broad interest in implementing Linked Data technologies, while acknowledging that potential barriers exist. Where there is a skills gap, participants were willing to attend events to learn more about the technologies and their applications in libraries. These and other dynamics could have implications for the prospects of Linked Data applications in Pakistani libraries.
Drugs and bitcoins: What role do bitcoins play in the darknet market? A preliminary study
Authors: Zhang Chenwei , Wei Ruibin , Liu Xiaozhong 
Published: February 2019

ABSTRACTDarknet markets (DNM) and bitcoins are two emergent topics in the information field and have attracted much attention. However, few has explored them together. This study aims at understanding the relation between them. It is investigated that whether the bitcoin exchange value and the public interest in it are predictable of the daily sales in the darknet markets in the future. By applying the Granger‐causality analysis, we find that the daily bitcoin exchange value with one day lag are Granger causative of the sales, so does the daily searching frequency in Google with one, two, and three‐day(s) lags. From the linear regressions, we find that the increasing bitcoin price reduces the daily DNM sales, while the increasing public interest in bitcoins facilitate the sales. By understanding relation between the DNM sales and bitcoins, two emergent topics in information science, we can get some insights on how to monitor the activities in the DNM, especially those illegal transactions and can further extend to how to regulate the DNM.
Identification of information search strategies and their impact on learning outcome in learning‐related tasks
Authors: Zhu Han , Liu Chang , Song Xiaoxuan 
Published: February 2019

ABSTRACTThis study aims to identify users' information search strategies in learning‐related tasks and investigates how the search strategies would influence their learning outcome. We conducted a lab‐based user experiment with 36 participants, recorded their search interactions during search process and measured their learning outcome by having them answer multiple choice questions after search. We extracted 14 behavioural features in three dimensions and conducted k‐means clustering analysis. Results showed that there were four types of search strategies: Economic Browsing, Supplementary Browsing, Exhaustive Browsing and Exhaustive Searching. It was found that Supplementary Browsing Strategy achieved the best learning outcome, while Economic Browsing Strategy showed the worst outcome, with Exhaustive Searching Strategy and Exhaustive Browsing Strategy in between. The findings indicated that careful selection of content pages and frequent querying were highly beneficial for users' learning during search. This study provides a deeper understanding of users' search strategies and has valuable implications on optimizing search systems to help users achieve better learning outcome.
Gender matters most. The impact on short‐term risk aversion following a financial crash
Authors: Byder James , Agudelo Diego A. , Arango Ignacio 
Published: February 2019

AbstractThis paper examines how investors in an emerging market react to a domestic financial crisis. We conjecture that risk aversion increases following such events and that the effect is more pronounced among specific groups of investors. Our study makes use of a unique dataset of mutual fund investors from one of Colombia's largest stock brokers. Our results reveal that women and self‐employed individuals make the largest withdrawals from risky funds after financial crises.