Showing papers in "Journal of the Association for Information Science and Technology in 2021"
TL;DR: A multimodal approach combining text and visual analysis of online news stories to automatically detect fake news is proposed, indicating that a multimodals approach outperforms single‐modality approaches, allowing for better fake news detection.
Abstract: Filtering, vetting, and verifying digital information is an area of core interest in information science. Online fake news is a specific type of digital misinformation that poses serious threats to democratic institutions, misguides the public, and can lead to radicalization and violence. Hence, fake news detection is an important problem for information science research. While there have been multiple attempts to identify fake news, most of such efforts have focused on a single modality (e.g., only text‐based or only visual features). However, news articles are increasingly framed as multimodal news stories, and hence, in this work, we propose a multimodal approach combining text and visual analysis of online news stories to automatically detect fake news. Drawing on key theories of information processing and presentation, we identify multiple text and visual features that are associated with fake or credible news articles. We then perform a predictive analysis to detect features most strongly associated with fake news. Next, we combine these features in predictive models using multiple machine‐learning techniques. The experimental results indicate that a multimodal approach outperforms single‐modality approaches, allowing for better fake news detection.
51 citations
TL;DR: The operationalization of the two concepts—“interdisciplinarity” and “synergy”—as different and partly overlapping indicators allows for distinguishing between the effects and the effectiveness of science‐policy interventions in research priorities.
Abstract: Problem solving often requires crossing boundaries, such as those between disciplines. When policy‐makers call for “interdisciplinarity,” however, they often mean “synergy.” Synergy is gen...
44 citations
TL;DR: This exploratory study found 174 OA journals that, through lack of comprehensive and open archives, vanished from the web between 2000 and 2019, spanning all major research disciplines and geographic regions of the world.
Abstract: The preservation of the scholarly record has been a point of concern since the beginning of knowledge production. With print publications, the responsibility rested primarily with librarians, but the shift toward digital publishing and, in particular, the introduction of open access (OA) have caused ambiguity and complexity. Consequently, the long‐term accessibility of journals is not always guaranteed, and they can even disappear from the web completely. The focus of this exploratory study is on the phenomenon of vanished journals, something that has not been carried out before. For the analysis, we consulted several major bibliographic indexes, such as Scopus, Ulrichsweb, and the Directory of Open Access Journals, and traced the journals through the Internet Archive's Wayback Machine. We found 174 OA journals that, through lack of comprehensive and open archives, vanished from the web between 2000 and 2019, spanning all major research disciplines and geographic regions of the world. Our results raise vital concern for the integrity of the scholarly record and highlight the urgency to take collaborative action to ensure continued access and prevent the loss of more scholarly knowledge. We encourage those interested in the phenomenon of vanished journals to use the public dataset for their own research.
31 citations
TL;DR: In this paper, the authors used the ongoing COVID-19 pandemic as a case study to systematically investigate factors associated with the spread of multi-topic misinformation related to one event on social media based on the heuristic-systematic model.
Abstract: The spread of misinformation on social media has become a major societal issue during recent years. In this work, we used the ongoing COVID-19 pandemic as a case study to systematically investigate factors associated with the spread of multi-topic misinformation related to one event on social media based on the heuristic-systematic model. Among factors related to systematic processing of information, we discovered that the topics of a misinformation story matter, with conspiracy theories being the most likely to be retweeted. As for factors related to heuristic processing of information, such as when citizens look up to their leaders during such a crisis, our results demonstrated that behaviors of a political leader, former US President Donald J. Trump, may have nudged people's sharing of COVID-19 misinformation. Outcomes of this study help social media platform and users better understand and prevent the spread of misinformation on social media.
31 citations
TL;DR: A type of shift in the focus of LIS research is suggested, with social media and data science topics playing a role in well over one‐third of articles published in 2018, compared with approximately 5% in 2012 and virtually none in 2006.
Abstract: Employing approaches adopted from studies of library and information science (LIS) research trends performed by Jarvelin et al., this content analysis systematically examines the evolution...
25 citations
TL;DR: This paper analyzes multiple use‐cases of DH studies in recent literature and lays out a practical decision model for DH experts for when and how to choose the appropriate deep learning approaches for their research and aims to raise awareness of the benefits of utilizing deep learning models in the DH community.
Abstract: Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases presenting a super‐human performance. DNNs are the state‐of‐the‐art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research, such as spell checking, language detection, entity extraction, author detection, question answering, and other tasks. These supervised algorithms learn patterns from a large number of “right” and “wrong” examples and apply them to new examples. However, using DNNs for analyzing the text resources in DH research presents two main challenges: (un)availability of training data and a need for domain adaptation. This paper explores these challenges by analyzing multiple use‐cases of DH studies in recent literature and their possible solutions and lays out a practical decision model for DH experts for when and how to choose the appropriate deep learning approaches for their research. Moreover, in this paper, we aim to raise awareness of the benefits of utilizing deep learning models in the DH community.
24 citations
TL;DR: This work reveals metric gaming up to the point of absurdity: fraudsters publish nonsensical algorithmically generated papers featuring genuine references and stresses the need to screen papers for nonsense before peer‐review and chase citation manipulation in published papers.
Abstract: In 2014 leading publishers withdrew more than 120 nonsensical publications automatically generated with the SCIgen program. Casual observations suggested that similar problematic papers are still published and sold, without follow-up retractions. No systematic screening has been performed and the prevalence of such nonsensical publications in the scientific literature is unknown. Our contribution is 2-fold. First, we designed a detector that combs the scientific literature for grammar-based computer-generated papers. Applied to SCIgen, it has a 83.6% precision. Second, we performed a scientometric study of the 243 detected SCIgen-papers from 19 publishers. We estimate the prevalence of SCIgen-papers to be 75 per million papers in Information and Computing Sciences. Only 19% of the 243 problematic papers were dealt with: formal retraction (12) or silent removal (34). Publishers still serve and sometimes sell the remaining 197 papers without any caveat. We found evidence of citation manipulation via edited SCIgen bibliographies. This work reveals metric gaming up to the point of absurdity: fraudsters publish nonsensical algorithmically generated papers featuring genuine references. It stresses the need to screen papers for nonsense before peer-review and chase citation manipulation in published papers. Overall, this is yet another illustration of the harmful effects of the pressure to publish or perish.
24 citations
TL;DR: The paper is providing an insight into digital tools, methods, and hermeneutics in action, showing that integrated interdisciplinary research needs to build something in between the disciplines while respecting and understanding each other's expertise and expectations.
Abstract: This article considers the interdisciplinary opportunities and challenges of working with digital cultural heritage, such as digitized historical newspapers, and proposes an integrated digital hermeneutics workflow to combine purely disciplinary research approaches from computer science, humanities, and library work. Common interests and motivations of the above‐mentioned disciplines have resulted in interdisciplinary projects and collaborations such as the NewsEye project, which is working on novel solutions on how digital heritage data is (re)searched, accessed, used, and analyzed. We argue that collaborations of different disciplines can benefit from a good understanding of the workflows and traditions of each of the disciplines involved but must find integrated approaches to successfully exploit the full potential of digitized sources. The paper is furthermore providing an insight into digital tools, methods, and hermeneutics in action, showing that integrated interdisciplinary research needs to build something in between the disciplines while respecting and understanding each other's expertise and expectations.
22 citations
TL;DR: It is found that social readjustment is positively correlated with sharing on social media, with both broad audiences and close ties as well as in online spaces separate from one's network of known ties.
Abstract: When people experience major life changes, this often impacts their self‐presentation, networks, and online behavior in substantial ways. To effectively study major life transitions and ev...
21 citations
TL;DR: EmoCred, a model that is based on a long‐short term memory model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non‐credible ones is presented.
Abstract: Fake news is considered one of the main threats of our society. The aim of fake news is usually to confuse readers and trigger intense emotions to them in an attempt to be spread through social networks. Even though recent studies have explored the effectiveness of different linguistic patterns for fake news detection, the role of emotional signals has not yet been explored. In this paper, we focus on extracting emotional signals from claims and evaluating their effectiveness on credibility assessment. First, we explore different methodologies for extracting the emotional signals that can be triggered to the users when they read a claim. Then, we present emoCred, a model that is based on a long-short term memory model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. In addition, we perform an analysis to understand which emotional signals and which terms are the most useful for the different credibility classes. We conduct extensive experiments and a thorough analysis on real-world datasets. Our results indicate the importance of incorporating emotional signals in the credibility assessment problem.
21 citations
TL;DR: It is demonstrated that it is possible to shift away from predominantly rhetorical use of holistic, toward paradigmatically holistic research, which will provide for richer analyses of critical phenomena in the discipline.
Abstract: Many researchers in library and information science have claimed that studies that are holistic are critical to understanding various phenomena. On closer examination, however, the term “h...
TL;DR: The aim of this article is to explore the current status of RDM in Chinese universities, in particular how university libraries have been involved in taking the agenda forward and indicates that Research Data Service at a local level in Chinese Universities are in their infancy.
Abstract: On April 2, 2018, the State Council of China formally released a national Research Data Management (RDM) policy “Measures for Managing Scientific Data”. In this context and given that university libraries have played an important role in supporting RDM at an institutional level in North America, Europe, and Australasia, the aim of this article is to explore the current status of RDM in Chinese universities, in particular how university libraries have been involved in taking the agenda forward. This article uses a mixed‐methods data collection approach and draws on a website analysis of university policies and services; a questionnaire for university librarians; and semi‐structured interviews. Findings indicate that Research Data Service at a local level in Chinese Universities are in their infancy. There is more evidence of activity in developing data repositories than support services. There is little development of local policy. Among the explanations of this may be the existence of a national‐level infrastructure for some subject disciplines, the lack of professionalization of librarianship, and the relatively weak resonance of openness as an idea in the Chinese context.
TL;DR: In this article, the authors use methods and theories derived from critical informatics to examine [anonymized] University's deployment of seven online learning platforms commonly used in higher education to uncover five themes that result from the deployment of corporate learning platforms.
Abstract: The COVID‐19 pandemic emptied classrooms across the globe and pushed administrators, students, educators, and parents into an uneasy alliance with online learning systems already committing serious privacy and intellectual property violations, and actively promoted the precarity of educational labor. In this article, we use methods and theories derived from critical informatics to examine [anonymized] University's deployment of seven online learning platforms commonly used in higher education to uncover five themes that result from the deployment of corporate learning platforms. We conclude by suggesting ways ahead to meaningfully address the structural power and vulnerabilities extended by higher education's use of these platforms. [ABSTRACT FROM AUTHOR] Copyright of Journal of the Association for Information Science & Technology is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
TL;DR: The study used interviews to examine how refugees and asylum seekers dealt with their information needs, finding that information gaps were bridged through people and places.
Abstract: This article discusses the sources of information used by refugees as they navigate integration systems and processes. The study used interviews to examine how refugees and asylum seekers dealt with their information needs, finding that information gaps were bridged through people and places. People included friends, solicitors, and caseworkers, whereas places included service providers, detention centers, and refugee camps. The information needs matrix was used as an analytical tool to examine the operation of sources on refuge-seekers' integration journeys. Our findings expand understandings of information sources and information grounds. The matrix can be used to enhance host societies' capacity to make appropriate information available and to provide evidence for the implementation of the information needs matrix.
TL;DR: A framework articulating key narratives associated with the why, what, how, and who dimensions to discuss paradigm shift(s) in the field of information is proposed.
Abstract: In this opinion paper, we frame a discussion on paradigm shift(s) in the field of information. We believe that in this astonishing historical moment of new directions and new opportunities both the existing paradigms and conceptual models in the field of information can benefit from re‐examination to stay current with the times. We propose a framework articulating key narratives associated with the why, what, how, and who dimensions to discuss paradigm shift(s). The purpose of this opinion paper is to initiate dialogues on ground‐breaking ideas and innovative solutions as well as support research that addresses contemporary challenges in the field of information.
TL;DR: This commentary reflects on some of the challenges and opportunities for data curation in the wake of the COVID‐19 pandemic, and focuses on some topics of particular interest to the information science community: data infrastructures for scholarly communication and research, the politicization of dataCuration and visualization for public‐facing “dashboards,” and human subjects research and policies.
Abstract: In this commentary, the authors, an international group data curation researchers and educators, reflect on some of the challenges and opportunities for data curation in the wake of the COVID-19 pandemic. We focus on some topics of particular interest to the information science community: data infrastructures for scholarly communication and research, the politicization of data curation and visualization for public-facing “dashboards,” and human subjects research and policies. We conclude with some areas of opportunity and need, including broader and richer data curation education in the information schools, the establishment of better data management policy implementations by research funders, the award of formal academic credit for data curation activities and data sharing, and engagement in cooperative action around data ethics and security.
TL;DR: It is shown that participants with a low level of knowledge on search advertising are more likely to click on ads than subjects with a highlevel of knowledge, and subjects with little knowledge show less willingness to scroll down to organic results.
Abstract: According to recent studies, search engine users have little knowledge of Google's business model. In addition, users cannot sufficiently distinguish organic results from advertisements, r...
TL;DR: This study shows that hashtags expand the understanding of the role of technology solutions in gatekeeping and advance research on hierarchical gatekeeping by conducting a content analysis of 77 interdisciplinary studies on hashtags and gatekeeping to confirm how they can implement six gatekeeping mechanisms.
Abstract: Since the inception of gatekeeping research in the 1940s, most studies on gatekeeping have been human‐centric, treating and studying individuals as gatekeepers, who perform their gatekeepi...
TL;DR: It is found that many genres can be quite easily predicted by their lexical signatures and this defines their position on the genre landscape, and it is shown that in terms of canonicity, canonical examples are often at the high end of the topic distribution profile for the genre rather than central as might be predicted by categorization theory.
Abstract: Genre plays an important role in the description, navigation, and discovery of movies, but it is rarely studied at large scale using quantitative methods. This allows an analysis of how genre labels are applied, how genres are composed and how these ingredients change, and how genres compare. We apply unsupervised topic modeling to a large collection of textual movie summaries and then use the model's topic proportions to investigate key questions in genre, including recognizability, mapping, canonicity, and change over time. We find that many genres can be quite easily predicted by their lexical signatures and this defines their position on the genre landscape. We find significant genre composition changes between periods for westerns, science fiction and road movies, reflecting changes in production and consumption values. We show that in terms of canonicity, canonical examples are often at the high end of the topic distribution profile for the genre rather than central as might be predicted by categorization theory.
TL;DR: This work uses part‐of‐speech (POS) focused lexical substitution for data augmentation (PLSDA) to enhance the performance of machine learning algorithms in sentiment analysis and introduces POS constraint and well‐designed augmentation strategies to improve the reliability of lexical data augments.
Abstract: Machine learning methods, especially deep learning models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, deep lea...
TL;DR: It is revealed that the amount and match of received support are positive and significant predictors of new users' continued engagement and can provide insight for designing and managing a sustainable OHC by retaining users.
Abstract: Online health communities (OHCs) have been major resources for people with similar health concerns to interact with each other. They offer easily accessible platforms for users to seek, re...
TL;DR: This essay argues that underrepresented and otherwise marginalized scholars have already produced significant work within social, cultural, and community‐oriented paradigms; social justice and advocacy; and, diversity, equity, and inclusion.
Abstract: While there are calls for new paradigms within the profession, there are also existing subgenres that fit this bill if they would be fully acknowledged. This essay argues that underreprese...
TL;DR: Using data from Sina Weibo and Sina Finance, it is shown that social media does influence mass media sentiment emergence for financial news and the sentiment consistency between social media reaction and prior news articles amplifies the persistence ofmass media sentiment over time.
Abstract: Mass media sentiment of financial news significantly influences investment decisions of investors. Hence, studying how this sentiment emerges is important. In years past, this was straightforward, often dictated by journalists who cover financial news, but this has become more complex now. In this paper, we focus on how social media sentiment affects mass media sentiment. Using data from Sina Weibo and Sina Finance (around 60 million weibos and 6.2 million news articles), we show that social media does influence mass media sentiment emergence for financial news. The sentiment consistency between social media reaction and prior news articles amplifies the persistence of mass media sentiment over time. By contrast, we found limited evidence of social media reducing the persistence of mass media sentiment over time. The results have significant implications for understanding how 2 types of media, treated separately in the literature, may be connected.
TL;DR: It is shown that the timing of the rise in multiple affiliations can be linked to the introduction of more competitive funding structures such as “excellence initiatives” in a number of countries and implications for science and science policy.
Abstract: This study provides the first systematic, international, large‐scale evidence on the extent and nature of multiple institutional affiliations on journal publications. Studying more than 15...
TL;DR: A gold‐standard dataset of software mentions from the manual annotation of 4,971 academic PDFs in biomedicine and economics is introduced, intended to be used for automatic extraction of software mentioning from PDF format research publications by supervised learning at scale.
Abstract: Software contributions to academic research are relatively invisible, especially to the formalized scholarly reputation system based on bibliometrics. In this article, we introduce a gold‐standard dataset of software mentions from the manual annotation of 4,971 academic PDFs in biomedicine and economics. The dataset is intended to be used for automatic extraction of software mentions from PDF format research publications by supervised learning at scale. We provide a description of the dataset and an extended discussion of its creation process, including improved text conversion of academic PDFs. Finally, we reflect on our challenges and lessons learned during the dataset creation, in hope of encouraging more discussion about creating datasets for machine learning use.
TL;DR: Analysis of posts on the Chinese social media platform Weibo during the 2014 Hong Kong Umbrella Movement finds that multimedia posts suffered more intensive censorship deletion than plain text posts, with censorship programs being oriented more toward multimedia content like images than the text content of multimedia posts.
Abstract: Although the Internet allows people to circulate messages using different media, most censorship studies discuss the removal of text content. This article presents a systematic study regarding the censorship of both plain text and multimedia content on the Chinese Internet. By analyzing both censored and surviving posts on the Chinese social media platform Weibo during the 2014 Hong Kong Umbrella Movement, we find that multimedia posts suffered more intensive censorship deletion than plain text posts, with censorship programs being oriented more toward multimedia content like images than the text content of multimedia posts. Our analysis has significant implications for censorship studies, information control, and politics in the “post‐text” era.
TL;DR: This paper serves as an interruption of epistemic injustice by presenting actions toward justice in the form of operationalized interventions of epistemicide.
Abstract: The information professions need a paradigmatic shift to address the epistemicide happening within our field and the ways we have systematically undermined knowledge systems falling outside of Western traditions. Epistemicide is the killing, silencing, annihilation, or devaluing of a knowledge system. We argue epistemicide happens when epistemic injustices are persistent and systematic and collectively work as a structured and systemic oppression of particular ways of knowing. We present epistemicide as a conceptual approach for understanding and analyzing ways knowledge systems are silenced or devalued within Information Science. We extend Fricker's framework by: (a) identifying new types of epistemic injustices, and (b) by adding to Fricker's concepts of Primary and Secondary Harm and introducing the concept of a Third Harm happening at an intergenerational level. Addressing epistemicide is critical for information professionals because we task ourselves with handling knowledge from every field. Acknowledgement of and taking steps to interrupt epistemic injustices and these specific harms are supportive of the social justice movements already happening. This paper serves as an interruption of epistemic injustice by presenting actions toward justice in the form of operationalized interventions of epistemicide.
TL;DR: The results show that Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes).
Abstract: To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in schola...
TL;DR: A proximity‐aware research leadership recommendation (PRLR) model is proposed to systematically integrate critical node attribute information (critical proximities) and network features to conductResearch leadership recommendation by predicting the directed links in the research leadership network.
Abstract: Collaborator recommendation is of great significance for facilitating research collaboration. Proximities have been demonstrated to be significant factors and determinants of research collaboration. Research leadership is associated with not only the capability to integrate resources to launch and sustain the research project but also the production and academic impact of the collaboration team. However, existing studies mainly focus on social or cognitive proximity, failing to integrate critical proximities comprehensively. Besides, existing studies focus on recommending relationships among all the coauthors, ignoring leadership in research collaboration. In this article, we propose a proximity‐aware research leadership recommendation (PRLR) model to systematically integrate critical node attribute information (critical proximities) and network features to conduct research leadership recommendation by predicting the directed links in the research leadership network. PRLR integrates cognitive, geographical, and institutional proximity as node attribute information and constructs a leadership‐aware coauthorship network to preserve the research leadership information. PRLR learns the node attribute information, the local network features, and the global network features with an autoencoder model, a joint probability constraint, and an attribute‐aware skip‐gram model, respectively. Extensive experiments and ablation studies have been conducted, demonstrating that PRLR significantly outperforms the state‐of‐the‐art collaborator recommendation models in research leadership recommendation.
TL;DR: In families managing chronic illness, information behaviors in the context of health‐related social control and the impact of control on patient health behavior are investigated, revealing conflictual information behavior, which led to competitions for control and influence between family members and patients.
Abstract: The relationship between information and control interests social scientists; however, much prior work has focused on organizations rather than families. Work on interactive information behaviors has also focused on organizations and on collaboration rather than conflict. Therefore, in families managing chronic illness, we investigated information behaviors in the context of health‐related social control and the impact of control on patient health behavior. We conducted a qualitative analysis of interviews with 38 family groups and 97 individuals over 2 years. Findings revealed conflictual information behavior, which led to competitions for control and influence between family members and patients. In response to perceived patient health behavior‐related problems, family members sought, shared, and used information for social control of patients by enforcing norms, leveraging expertise, performing surveillance, and structuring the environment. These behaviors clashed with patients' interests and perspectives drawn from their own information acquisition. Patients responded by assessing family‐presented information and using information to resist or appease norm enforcement, refute or agree with expertise, and permit or block surveillance. Over time, some patient behaviors changed; alternatively, patients blocked family access to information about themselves, or family members retreated. The results challenge presumptions of benefit and harmony that have characterized much prior information behavior research.