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The data paper is an emerging academic genre that focuses on the description of research data objects. However, there is a lack of empirical knowledge about this rising genre in quantitative science studies, particularly from the perspective of its linguistic features. To fill this gap, this research aims to offer a first quantitative examination of which rhetorical moves—rhetorical units performing a coherent narrative function—are used in data paper abstracts, as well as how these moves are used. To this end, we developed a new classification scheme for rhetorical moves in data paper abstracts by expanding a well‐received system that focuses on English‐language research article abstracts. We used this expanded scheme to classify and analyze rhetorical moves used in two flagship data journals, Scientific Data and Data in Brief. We found that data papers exhibit a combination of introduction, method, results, and discussion‐ and data‐oriented moves and that the usage differences between the journals can be largely explained by journal policies concerning abstract and paper structure. This research offers a novel examination of how the data paper, a data‐oriented knowledge representation, is composed, which greatly contributes to a deeper understanding of research data and its publication in the scholarly communication system. [ABSTRACT FROM AUTHOR]

During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta‐research to help understand science communication in crises and improve its effectiveness. The objective is to study how many Spanish scientific papers on COVID‐19 published during 2020 share their research data. Qualitative and descriptive study applying nine attributes: (a) availability, (b) accessibility, (c) format, (d) licensing, (e) linkage, (f) funding, (g) editorial policy, (h) content, and (i) statistics. We analyzed 1,340 papers, 1,173 (87.5%) did not have research data. A total of 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data, and 5.2% share their data as supplementary material. There is a small percentage that shares their research data; however, it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data. [ABSTRACT FROM AUTHOR]

This article explores the factors influencing the future citations of conference papers. We concentrated on the explanatory power of early attention on conference papers for citations collected from Google Scholar and Scopus. The early attention data includes users' online activities in a conference support system: CN3. Bookmarks from the bibliographic management system, Citeulike, were used as a collateral source of early attention. To examine the chronological contributions of 13 factors on citations, a multiple sequential regression analysis was conducted for three timepoints of the publication cycle—paper submission, time of conferences, and months after conferences. Our results illustrate that online readers' early attention of Citeulike bookmarks were found to have the most influence on the future impact of the conference papers. The early attention records from CN3 made noteworthy improvements to explaining both the Google and Scopus citations as well. We also found that the type of papers the number of papers presented at a conference, and the best article award records were significant factors influencing future citations. However, the magnitude of the effects made by online readers' early attention from both sources appears to be larger than these three traditional factors. [ABSTRACT FROM AUTHOR]

The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time‐consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real‐time visual platform, designed using a zz‐structure‐based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real‐life uses. The results of the evaluation are positive and describe an effective and usable platform. [ABSTRACT FROM AUTHOR]

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of P@5, R@5, and F1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models. [ABSTRACT FROM AUTHOR]

The emphasis on research evaluation has brought scrutiny to the role of self‐citations in the scholarly communication process. While author self‐citations have been studied at length, little is known on national‐level self‐references (SRs). This paper analyses the citation context of national SRs, using the full‐text of 184,859 papers published in PLOS journals. It investigates the differences between national SRs and nonself‐references (NSRs) in terms of their in‐text mention, presence in enumerations, and location features. For all countries, national SRs exhibit a higher level of engagement than NSRs. NSRs are more often found in enumerative citances than SRs, which suggests that researchers pay more attention to domestic than foreign studies. There are more mentions of national research in the methods section, which provides evidence that methodologies developed in a nation are more likely to be used by other researchers from the same nation. Publications from the United States are cited at a higher rate in each of the sections, indicating that the country still maintains a dominant position in science. On the whole, this paper contributes to a better understanding of the role of national SRs in the scholarly communication system, and how it varies across countries and over time. [ABSTRACT FROM AUTHOR]

COVID‐19 has emerged as a major research hotspot and trending topic in recent years, leading to increased publications and citations of related papers. While concerns exist about the potential citation boost in journals publishing these papers, the specifics are not fully understood. This study uses a generalized difference‐in‐differences approach to examine the impact of publishing COVID‐19 papers on journal citation metrics in the Health Sciences fields. Findings indicate that journals publishing COVID‐19 papers in 2020 received significantly higher citation premiums due to COVID‐19 in 2020 and continued to benefit from the premium in 2021 in certain fields. In contrast, journals that began publishing COVID‐19 papers in 2021 experienced weaker citation premiums. The citation premiums exhibit some negative spillover effect: Although the publication volume of non‐COVID‐19 papers also surged, these papers experienced insignificant or negative citation gains, even when published in the same journals as COVID‐19 papers. COVID‐19 papers published in high‐impact journals brought higher citation premiums than those in low‐impact journals in most fields, indicating a potential Matthew effect. These citation premiums can affect various citation‐based journal metrics, such as our simulated impact factor and SCImago Journal Rank, to different degrees. Compared to the simulated impact factor, other normalized journal metrics are less influenced by citation premiums. The results highlight a "gold rush" pattern in which early entrants establish their citation advantage in research hotspots and caution against using citation‐based metrics for research assessment. [ABSTRACT FROM AUTHOR]

Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision‐making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size. [ABSTRACT FROM AUTHOR]

Performance assessment is ubiquitous and crucial in people analytics. Scientific impact, particularly, plays a significant role in the academia. This paper attempts to understand researchers' career trajectories by considering the research community as a social network, where individuals build ties with each other via coauthorship. The resulting linkage facilitates information flow and affects researchers' future impact. Consequently, we systematically investigate the career trajectories of researchers with respect to research impact using the social capital theory as our theoretical foundation. Specifically, for early‐stage and mid‐career academics, we find that connections with prominent researchers associate with greater impact. Brokerage positions, in addition, are beneficial to a researcher's research impact in the long run. For senior researchers, however, the only social network feature that significantly affects their future impact is the reputation of their recently built ties. Finally, we build predictive models on future research impact which can be leveraged by both organizations and individuals. This paper provides empirical evidence for how social networks provide signals on researchers' career dynamics guided by social capital theory. Our findings have implications for individual researchers to strategically plan and promote their careers and for research institutions to better evaluate current as well as prospective employees. [ABSTRACT FROM AUTHOR]

To effectively manage a crisis, most decisions made by governments, organizations, communities, and individuals are based on "shared situational awareness" (SSA) derived from multiple information sources. Developing SSA depends on the alignment of mental models, which "represent our shared version of truth and reality on which we can act." Social media has facilitated public sensemaking during a crisis; however, it has also encouraged mental model dissonance, resulting in the digital destruction of mental models and undermining adequate SSA. The study is concerned with the challenges of creating SSA during the COVID‐19 pandemic in Australia. This paper documents a netnography of Australian public health agencies' Facebook communication, exploring the initial impact of COVID‐19 on SSA creation. Chaos theory is used as a theoretical lens to examine information perception, meaning, and assumptions relating to SSA from pre to post‐pandemic periods. Our study highlights how the initial COVID‐19 "butterfly effect" swamped the public health communication channel, leaving little space for other important health issues. This research contributes to information systems, information science, and communications by illustrating how the emergence of a crisis impacts social media communication, the creation of SSA, and what this means for social media adoption for crisis communication purposes. [ABSTRACT FROM AUTHOR]

How to collect relevance judgment has long been an important problem in Information Retrieval (IR). A popular method is to collect relevance judgment in a point‐wise manner, in which assessors examine and give an absolute relevance score for each item independently of the others. As an alternative, pair‐wise relevance judgment, also named preference judgment, allows an assessor to compare two items side‐by‐side and express their preference for one over the other. Previous work has explored the differences between these two paradigms of relevance judgments from many different aspects. Most of these works are conducted through explicit/implicit feedback. However, few works investigate the underlying neurological mechanisms of the two paradigms. In this paper, we conduct a lab study to investigate and compare point‐wise and pair‐wise relevance judgment in image search scenarios. We study the neurological mechanisms of the two paradigms through an event‐related potential (ERP) analysis of the users' brain signals while viewing images during a search process. We have obtained several observations, such as search engine users tend to pay more attention to preferred items in the point‐wise paradigm but unpreferred items in the pair‐wise paradigm. Furthermore, we test the adoption of brain signals as implicit feedback for predicting pair‐wise relevance judgment, highlighting the feasibility of leveraging brain signals to understand users' relevance judgments. [ABSTRACT FROM AUTHOR]

The production of research and faculty in the US higher education system is concentrated within a few institutions. Concentration of research and resources affects minoritized scholars and the topics with which they are disproportionately associated. This paper examines topical alignment between institutions and authors of varying intersectional identities, and the relationship between research topics and identities with institutional prestige and scientific impact. Our results show statistically significant differences between minoritized scholars and White men in citations and journal impact. The aggregate research profile of prestigious US universities is highly correlated with the research profile of White men, and negatively correlated with the research profile of minoritized women. Furthermore, authors affiliated with more prestigious institutions are associated with increasing inequalities in both citations and journal impact. These results suggest a relationship—which we coin as the Howard‐Harvard effect—in which the topical profile of minoritized scholars is further marginalized in prestigious institutions as compared to mission‐driven institutions. Academic institutions and funders should create policies to mitigate the systemic barriers that prevent the United States from achieving a fully robust scientific ecosystem. [ABSTRACT FROM AUTHOR]

This paper explores the knowledge transfer of internationally mobile scientists. It builds upon previous work on the development of methods for detecting the knowledge transfer of German scientists. Using abstract terms of publications covered in Scopus, this paper proposes a lexical‐based approach to identify knowledge transmitters. These scientists are characterized by acquiring knowledge from their co‐workers during their international stay and transferring it upon return to German co‐workers. Knowledge is operationalized as the co‐occurrence of rarely used abstract terms. Knowledge transfer is expressed as the diffusion of these term combinations in co‐authorship networks. The method developed was validated by contacting the bibliometrically identified knowledge transmitters and asking them what they believe they learned during their stay abroad. A control group of internationally mobile scientists without traceable knowledge transfer was similarly asked to report on their knowledge acquisition. The findings suggest that bibliometric data are capable of detecting knowledge transmitters among German scientists who were internationally mobile. The juxtaposition of the responses on their perceived knowledge acquisition and the bibliometrically identified lexical terms shows that the method proposed is well suited to studying the knowledge transfer of internationally mobile scientists. The strength of the method is its simplicity and high precision. [ABSTRACT FROM AUTHOR]

This paper investigates whether free access to scientific literature increases the participation of under‐represented groups in scientific discourse. To this end, we aggregate and match data tracing access to Sci‐Hub, a widely used black open access (OA) repository or shadow library, and publication data from the Web of Science (WoS). We treat the emergence of Sci‐Hub as an exogenous event granting relatively unrestricted access to publications, which are otherwise hidden behind a paywall. We analyze changes in the publication count of researchers from developing countries in a given journal as a proxy for general participation in scientific discourse. Our results indicate that in the exemplary field of mathematics, free access to academic knowledge is likely to improve the representation of authors from developing countries in international journals. Assuming the desirability of greater international diversity in science (e.g., to generate more original work, reproduce empirical findings in different settings, or shift the research focus toward topics that are overlooked by researchers from more developed countries), our findings lend evidence to the claim of the OA movement that scientific knowledge should be free and widely distributed. [ABSTRACT FROM AUTHOR]

Citation analysis, as a tool for quantitative studies of science, has long emphasized direct citation relations, leaving indirect or high‐order citations overlooked. However, a series of early and recent studies demonstrate the existence of indirect and continuous citation impact across generations. Adding to the literature on high‐order citations, we introduce the concept of a citation cascade: the constitution of a series of subsequent citing events initiated by a certain publication. We investigate this citation structure by analyzing more than 450,000 articles and over 6 million citation relations. We show that citation impact exists not only within the three generations documented in prior research but also in much further generations. Still, our experimental results indicate that two to four generations are generally adequate to trace a work's scientific impact. We also explore specific structural properties—such as depth, width, structural virality, and size—which account for differences among individual citation cascades. Finally, we find evidence that it is more important for a scientific work to inspire trans‐domain (or indirectly related domain) works than to receive only intradomain recognition in order to achieve high impact. Our methods and findings can serve as a new tool for scientific evaluation and the modeling of scientific history. [ABSTRACT FROM AUTHOR]

As part of the effort to digitally transform, organizations are seeking more and better solutions to long‐standing enterprise content management challenges. Such solutions rarely investigate the relationship between knowledge workers' daily work to capture information and the perceived or actual value of that information to the enterprise per established content management strategy. The study described in this paper seeks to identify gaps in content management practices versus policy by modeling the conventions by which one organization's knowledge workers typically generate, store, and later recover their daily work products. Thirty‐five interviews with knowledge workers at the Jet Propulsion Laboratory were conducted on this subject. The results of these interviews provide an insight as to how knowledge workers interact with enterprise content in their dual roles as both the primary creators and primary consumers of enterprise content. This paper, which outlines various permutations of the digital object creation, description, and storage (CDS) model, provides basic strategies for bringing the value perceptions of knowledge workers into alignment with institutional directives related to improving content findability and reuse in the enterprise. [ABSTRACT FROM AUTHOR]

Open science is receiving widespread attention globally, and preprinting offers an important way to implement open science practices in scholarly publishing. To develop a systematic understanding of researchers' adoption of and attitudes toward preprinting, we conducted a survey of authors of research papers published in 2021 and early 2022. Our survey results show that the United States and Europe led the way in the adoption of preprinting. The United States and European respondents reported a higher familiarity with and a stronger commitment to preprinting than their colleagues elsewhere in the world. The adoption of preprinting is much stronger in physics and astronomy as well as mathematics and computer science than in other research areas. Respondents identified free accessibility of preprints and acceleration of research communication as the most important benefits of preprinting. Low reliability and credibility of preprints, sharing results before peer review and premature media coverage are the most significant concerns about preprinting, emphasized in particular by respondents in the life and health sciences. According to respondents, the most crucial strategies to encourage preprinting are integrating preprinting into journal submission workflows and providing recognition for posting preprints. [ABSTRACT FROM AUTHOR]

With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Many features of the U.S. political economy arise from the interactions between large political and economic institutions, and yet we know little about the nature of their interactions and the power distribution between these institutions. In this paper, we present a detailed analysis of networks of U.S.‐based organizations, where edges represent three different kinds of relationships, namely owner–owned (ownerships), donor–donee (donations), and service provider–payee (transactions). Our findings suggest that in the ownerships network, the financial organizations form the core, and banking organizations hold strategic locations in the network. In the transactions network, the government organizations and agencies form the core, and defense‐related organizations form the backbone. In contrast, with the donations network, no specific domain of organizations forms either the core or the backbone. [ABSTRACT FROM AUTHOR]

Term position feature is widely and successfully used in IR and Web search engines, to enhance the retrieval effectiveness. This feature is essentially used for two purposes: to capture query terms proximity or to boost the weight of terms appearing in some parts of a document. In this paper, we are interested in this second category. We propose two novel query‐independent techniques based on absolute term positions in a document, whose goal is to boost the weight of terms appearing in the beginning of a document. The first one considers only the earliest occurrence of a term in a document. The second one takes into account all term positions in a document. We formalize each of these two techniques as a document model based on term position, and then we incorporate it into a basic language model (LM). Two smoothing techniques, Dirichlet and Jelinek‐Mercer, are considered in the basic LM. Experiments conducted on three TREC test collections show that our model, especially the version based on all term positions, achieves significant improvements over the baseline LMs, and it also often performs better than two state‐of‐the‐art baseline models, the chronological term rank model and the Markov random field model. [ABSTRACT FROM AUTHOR]

This study investigates scholars' citation behaviors from a fine‐grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision‐making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest‐based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision‐making, followed by citation authority and citation inertia. However, big‐name scholars whose h‐indexes rank among the top 1% exhibit a unique pattern of citation behaviors—their citation decision‐making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia. [ABSTRACT FROM AUTHOR]

This paper investigates different uses of the Journal Impact Factor (JIF) in national journal rankings and discusses the merits of supplementing metrics with expert assessment. Our focus is national journal rankings used as evidence to support decisions about the distribution of institutional funding or career advancement. The seven countries under comparison are China, Denmark, Finland, Italy, Norway, Poland, and Turkey—and the region of Flanders in Belgium. With the exception of Italy, top‐tier journals used in national rankings include those classified at the highest level, or according to tier, or points implemented. A total of 3,565 (75.8%) out of 4,701 unique top‐tier journals were identified as having a JIF, with 55.7% belonging to the first Journal Impact Factor quartile. Journal rankings in China, Flanders, Poland, and Turkey classify journals with a JIF as being top‐tier, but only when they are in the first quartile of the Average Journal Impact Factor Percentile. Journal rankings that result from expert assessment in Denmark, Finland, and Norway regularly classify journals as top‐tier outside the first quartile, particularly in the social sciences and humanities. We conclude that experts, when tasked with metric‐informed journal rankings, take into account quality dimensions that are not covered by JIFs. [ABSTRACT FROM AUTHOR]

Interdisciplinary research is widely recognized as necessary to tackle some of the grand challenges facing humanity. It is generally believed that interdisciplinarity is becoming increasingly prevalent among Science, Technology, Engineering, and Mathematics (STEM) fields. However, little is known about the evolution of interdisciplinarity in the Social Sciences. Also, how interdisciplinarity and its various aspects evolve over time has seldom been closely quantified and delineated. This paper answers these questions by capturing the disciplinary diversity of the knowledge base of scientific publications in nine broad Social Sciences fields over 55 years. The analysis considers diversity as a whole and its three distinct aspects, namely variety, balance, and disparity. Ordinary least squares (OLS) regressions are also conducted to investigate whether such change, if any, can be found among research with similar characteristics. We find that learning widely and digging deeply have become one of the norms among researchers in Social Sciences. Fields acting as knowledge exporters or independent domains maintain a relatively stable homogeneity in their knowledge base while the knowledge base of importer disciplines evolves towards greater heterogeneity. However, the increase of interdisciplinarity is substantially smaller when controlling for several author and publication related variables. [ABSTRACT FROM AUTHOR]

This research analyzes human‐generated clarification questions to provide insights into how they are used to disambiguate and provide a better understanding of information needs. A set of clarification questions is extracted from posts on the Stack Exchange platform. Novel taxonomy is defined for the annotation of the questions and their responses. We investigate the clarification questions in terms of whether they add any information to the post (the initial question posted by the asker) and the accepted answer, which is the answer chosen by the asker. After identifying, which clarification questions are more useful, we investigated the characteristics of these questions in terms of their types and patterns. Non‐useful clarification questions are identified, and their patterns are compared with useful clarifications. Our analysis indicates that the most useful clarification questions have similar patterns, regardless of topic. This research contributes to an understanding of clarification in conversations and can provide insight for clarification dialogues in conversational search scenarios and for the possible system generation of clarification requests in information‐seeking conversations. [ABSTRACT FROM AUTHOR]

Despite the potential importance of emotional aspects in information seeking, there is a lack of adequate attention to emotions' role in facilitating serendipitous information encountering. This paper contributes to this research gap by investigating the role of emotions during the process of perceiving and experiencing serendipitous information encountering in a controlled laboratory setting. The results show that applying a sketch game can stimulate participants' emotions. Our findings indicate that participants are more likely to experience serendipitous information encountering under the influence of positive emotions. This study contributes to an understanding of the relationship between emotions and the perception of serendipitous information encountering. The implications of the possibilities of facilitating positive emotions to induce serendipitous information encountering are discussed. [ABSTRACT FROM AUTHOR]

Machine learning methods, especially deep learning models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, deep learning models are more demanding for training data. Data augmentation techniques are widely used to generate new instances based on modifications to existing data or relying on external knowledge bases to address annotated data scarcity, which hinders the full potential of machine learning techniques. This paper presents our work using part‐of‐speech (POS) focused lexical substitution for data augmentation (PLSDA) to enhance the performance of machine learning algorithms in sentiment analysis. We exploit POS information to identify words to be replaced and investigate different augmentation strategies to find semantically related substitutions when generating new instances. The choice of POS tags as well as a variety of strategies such as semantic‐based substitution methods and sampling methods are discussed in detail. Performance evaluation focuses on the comparison between PLSDA and two previous lexical substitution‐based data augmentation methods, one of which is thesaurus‐based, and the other is lexicon manipulation based. Our approach is tested on five English sentiment analysis benchmarks: SST‐2, MR, IMDB, Twitter, and AirRecord. Hyperparameters such as the candidate similarity threshold and number of newly generated instances are optimized. Results show that six classifiers (SVM, LSTM, BiLSTM‐AT, bidirectional encoder representations from transformers [BERT], XLNet, and RoBERTa) trained with PLSDA achieve accuracy improvement of more than 0.6% comparing to two previous lexical substitution methods averaged on five benchmarks. Introducing POS constraint and well‐designed augmentation strategies can improve the reliability of lexical data augmentation methods. Consequently, PLSDA significantly improves the performance of sentiment analysis algorithms. [ABSTRACT FROM AUTHOR]

Data reuse has recently become significant in academia and is providing new impetus for academic research. This prompts two questions: What precisely is the data reuse process? What is the connection between each participating element? To address these issues, 42 studies were reviewed to identify the stages and primary data reuse elements. A meta‐synthesis was used to locate and analyze the studies, and inductive coding was used to organize the analytical process. We identified three stages of data reuse—initiation, exploration and collection, and repurposing—and explored how they interact and form iterative characteristics. The results illuminated the data reuse at each stage, including issues of data trust, data sources, scaffolds, and barriers. The results indicated that multisource data and human scaffolds promote reuse behavior effectively. Further, two data and information search patterns were extracted: reticular centripetal patterns and decentralized centripetal patterns. Three paths with elements cooperating through flexible functions and motivated by different action items were identified: data centers, human scaffolds, and publications. This study supports improvements for data infrastructure construction, data reuse, and data reuse research by providing a new perspective on the effect of information behavior and clarifying the stages and contextual relationships between various elements. [ABSTRACT FROM AUTHOR]

Richer forms of online reviews such as videos or follow‐on reviews convey 'additional information and can attract consumers' attention. However, prior studies focused mostly on the relationship between aggregated online reviews and sales. This paper investigates the impact of online review richness (i.e., reviews containing videos or follow‐on reviews) on sales. Leveraging attribute substitution theory, we conjecture that online review richness can provide heuristic cues in the online shopping environment to help consumers make better purchase decisions. Using data from JD.com, we found that reviews containing either videos or follow‐on reviews positively affect sales. In addition, different product types can also serve as heuristic cues to replace target cues, which can further affect how different forms of online review richness affect sales. We found that the impact of online review richness on sales is stronger for utilitarian products than for hedonic products, and stronger for negatively commented products than for positively commented products. Moreover, we conducted two online experiments and confirmed that the causal relationship is from online review richness to sales. The research findings offer practical implications for online retailers and constitute one of the first steps toward a better understanding of the relationship between online review richness and sales. [ABSTRACT FROM AUTHOR]

Social interactions through online social media have become a daily routine of many, and the number of those whose real world (offline) and online lives have become intertwined is continuously growing. As such, the interplay of individuals' online and offline activities has been the subject of numerous research studies, the majority of which explored the impact of people's online actions on their offline activities. The opposite direction of impact—the effect of real‐world activities on online actions—has also received attention but to a lesser degree. To contribute to the latter form of impact, this paper reports on a quasi‐experimental design study that examined the presence of causal relations between real‐world activities of online social media users and their online emotional expressions. To this end, we have collected a large dataset (over 17K users) from Twitter and Foursquare, and systematically aligned user content on the two social media platforms. Users' Foursquare check‐ins provided information about their offline activities, whereas the users' expressions of emotions and moods were derived from their Twitter posts. Since our study was based on a quasi‐experimental design, to minimize the impact of covariates, we applied an innovative model of computing propensity scores. Our main findings can be summarized as follows: (a) users' offline activities do impact their affective expressions, both of emotions and moods, as evidenced in their online shared textual content; (b) the impact depends on the type of offline activity and if the user embarks on or abandons the activity. Our findings can be used to devise a personalized recommendation mechanism to help people better manage their online emotional expressions. [ABSTRACT FROM AUTHOR]

Scientometrics has many citation‐based measurements for characterizing diversity, but most of these measurements depend on human‐designed categories and the granularity of discipline classifications sometimes does not allow in‐depth analysis. As such, the current paper proposes a new measurement for quantifying journals' diversity by utilizing the abstracts of scientific publications in journals, namely topic diversity (TD). Specifically, we apply a topic detection method to extract fine‐grained topics, rather than disciplines, in journals and adapt certain diversity indicators to calculate TD. Since TD only needs as inputs abstracts of publications rather than citing relationships between publications, this measurement has the potential to be widely used in scientometrics. [ABSTRACT FROM AUTHOR]

Diffusion channels are critical to determining the adoption scale, which leads to the ultimate impact of an innovation. The aim of this study is to develop an integrative understanding of the impact of two diffusion channels (i.e., broadcasting vs. virality) on innovation adoption. Using citations of a series of classic algorithms and the time series of co‐authorship as the footprints of their diffusion trajectories, we propose a novel method to analyze the intertwining relationships between broadcasting and virality in the innovation diffusion process. Our findings show that broadcasting and virality have similar diffusion power, but play different roles across diffusion stages. Broadcasting is more powerful in the early stages but may be gradually caught up or even surpassed by virality in the later period. Meanwhile, diffusion speed in virality is significantly faster than broadcasting and members from virality channels tend to adopt the same innovation repetitively. [ABSTRACT FROM AUTHOR]

Social media data (SMD) offer researchers new opportunities to leverage those data for their work in broad areas such as public opinion, digital culture, labor trends, and public health. The success of efforts to save SMD for reuse by researchers will depend on aligning data management and archiving practices with evolving norms around the capture, use, sharing, and security of datasets. This paper presents an initial foray into understanding how established practices for managing and preserving data should adapt to demands from researchers who use and reuse SMD, and from people who are subjects in SMD. We examine the data management practices of researchers who use SMD through a survey, and we analyze published articles that used data from Twitter. We discuss how researchers describe their data management practices and how these practices may differ from the management of conventional data types. We explore conceptual, technical, and ethical challenges for data archives based on the similarities and differences between SMD and other types of research data, focusing on the social sciences. Finally, we suggest areas where archives may need to revise policies, practices, and services in order to create secure, persistent, and usable collections of SMD. [ABSTRACT FROM AUTHOR]

Journals play a critical role in the scientific process because they evaluate the quality of incoming papers and offer an organizing filter for search. However, the role of journals has been called into question because new preprint archives and academic search engines make it easier to find articles independent of the journals that publish them. Research on this issue is complicated by the deeply confounded relationship between article quality and journal reputation. We present an innovative proxy for individual article quality that is divorced from the journal's reputation or impact factor: the number of citations to preprints posted on arXiv.org. Using this measure to study three subfields of physics that were early adopters of arXiv, we show that prior estimates of the effect of journal reputation on an individual article's impact (measured by citations) are likely inflated. While we find that higher‐quality preprints in these subfields are now less likely to be published in journals compared to prior years, we find little systematic evidence that the role of journal reputation on article performance has declined. [ABSTRACT FROM AUTHOR]

This paper examines the role of privacy in the health context by investigating the influence of privacy concerns and perceived benefits on individuals' acceptance of health technologies used by healthcare providers and their own adoption of mobile health technologies. The study adopts a two‐stage sequential mixed‐methods design. The first stage is a quantitative survey of 447 citizens from two countries. The second stage involves 50 qualitative interviews which further untangle the roles of privacy concern and benefits. The integrated findings provide evidence that a privacy paradox exists. While individuals continue to express a high desire for privacy, their consideration of the benefits and privacy concerns prior to adoption is largely skewed toward the benefits, due to a lack of privacy knowledge, emphasis on immediate gratification, overestimation of the benefits, and underestimation of the risks. The study further extends the privacy calculus theory to the health context and acknowledges the factors impacting this comparison of benefits and privacy concerns prior to and postadoption of a new technology. The study provides actionable insights for practice, highlighting the importance of privacy education to foster awareness and control mechanisms to negate the potential negative effects of privacy concern. [ABSTRACT FROM AUTHOR]

Although the open access citation advantage (OACA) has been discussed extensively, scholars lack a clear understanding of the mechanisms through which switching from subscription‐based model to open access (OA) model affects the citation impact of a scholarly journal. Many journals have switched from subscription to OA, yet they later also flipped their preswitching articles (i.e., those under subscription model) to OA, thus leaving no subscription article to be compared with their postswitching OA counterparts. To detect the switching effect, our study instead focused on 60 journals that "reverse flipped" from OA to subscription. We use a difference‐in‐difference (DiD) analytical framework to analyze two propositions related to OACA, based on the bibliographic and citation data of pre‐ and postswitching publications in these journals. Our findings indicate that reverse flipping is unlikely to affect the journals' impact through changing the visibility of their articles. Instead, it could lead to a systematical shift in the submissions to the journals and thus considerably affect their impact. Our findings have important theoretical and practical implications for subsequent studies, funding agencies, and scholarly journals considering a reverse flip. [ABSTRACT FROM AUTHOR]

This study applies Kuhlthau's Information Search Process stage (ISP) model to understand bilingual users' Internet search experience. We conduct a quasi‐field experiment with 30 bilingual searchers and the results suggested that the ISP model was applicable in studying searchers' information retrieval behavior in search tasks. The ISP model was applicable in studying searchers' information retrieval behavior in simple tasks. However, searchers' emotional responses differed from those of the ISP model for a complex task. By testing searchers using different search strategies, the results suggested that search engines with multilanguage search functions provide an advantage for bilingual searchers in the Internet's multilingual environment. The findings showed that when searchers used a search engine as a tool for problem solving, they might experience different feelings in each ISP stage than in searching for information for a term paper using a library. The results echo other research findings that indicate that information seeking is a multifaceted phenomenon. [ABSTRACT FROM AUTHOR]

As the online dissemination of scholarly outputs gets faster and easier, altmetrics, social media based indices, have emerged alongside traditional metrics for research evaluation. In a two‐phase survey, we investigate scholars' familiarity and usage of traditional metrics and altmetrics. In this paper, we present the second phase with 448 participants. We found few traditional metrics, like the Journal Impact Factor and number of citations, are familiar to and often used by scholars for research evaluation. Among altmetrics, only views/downloads, readers, and followers are known to more than half the respondents. Unseen benefits and lack of time are hindrances to using metrics for the evaluation of research outputs. Although social media are well‐known, scholars prefer promoting their research by publishing in journals and attending conferences. We found social media usage, perceived ease of use and usefulness of altmetrics affect the usage of altmetrics. Findings suggest altmetrics have attracted attention in academia and could be considered complementary to traditional metrics. We acknowledge that due to the limited sample size, statistics and demographics in this study, findings cannot be said to be representative of the entire academic population worldwide. Future studies are needed that cover a wider range of academic disciplines around the world. [ABSTRACT FROM AUTHOR]

This study explores the use of PubPeer by the scholarly community, to understand the issues discussed in an online journal club, the disciplines most commented on, and the characteristics of the most prolific users. A sample of 39,985 posts about 24,779 publications were extracted from PubPeer in 2019 and 2020. These comments were divided into seven categories according to their degree of seriousness (Positive review, Critical review, Lack of information, Honest errors, Methodological flaws, Publishing fraud, and Manipulation). The results show that more than two‐thirds of comments are posted to report some type of misconduct, mainly about image manipulation. These comments generate most discussion and take longer to be posted. By discipline, Health Sciences and Life Sciences are the most discussed research areas. The results also reveal "super commenters," users who access the platform to systematically review publications. The study ends by discussing how various disciplines use the site for different purposes. [ABSTRACT FROM AUTHOR]

Email is the primary method of communication with authors of scientific publications. This study sought to measure the reliability, over time, of contact email addresses from biomedical publications, particularly depending on email type. Emails were written to randomly selected email addresses from publications in MEDLINE, and email bounce rates were modeled probabilistically. The use of personal email addresses was quantified and compared to the use of other types of email addresses. Eighteen percent of authors' contact email addresses in MEDLINE were estimated to be invalid. A steadily growing share of email addresses was personal: 32% of all new email addresses in MEDLINE in 2018 were of this kind. These email addresses were less likely to be invalid than email addresses from other types of providers. While the percentage of invalid email addresses was significant, it was lower than previously estimated. Personal email addresses are taking an increasingly more important role by supplying more reliable email addresses to scientists. To mitigate the problem of invalid email addresses, institutions should provide email forwarding, scientific directories should offer the possibility of contacting authors, or scientific authors should use more stable email addresses. [ABSTRACT FROM AUTHOR]

This study examines the relationship between team's gender composition and outputs of funded projects using a large data set of National Institutes of Health (NIH) R01 grants and their associated publications between 1990 and 2017. This study finds that while the women investigators' presence in NIH grants is generally low, higher women investigator presence is on average related to slightly lower number of publications. This study finds empirically that women investigators elect to work in fields in which fewer publications per million‐dollar funding is the norm. For fields where women investigators are relatively well represented, they are as productive as men. The overall lower productivity of women investigators may be attributed to the low representation of women in high productivity fields dominated by men investigators. The findings shed light on possible reasons for gender disparity in grant productivity. [ABSTRACT FROM AUTHOR]

Alongside cognitive and social phenomena, many scholars have examined emotional and affective considerations in information science, but a potential emotional or affective paradigm has not coalesced to the extent of the social or cognitive paradigms. We argue information science research should integrate the social paradigm, as offered by social informatics, with affective and emotional considerations: a socio‐emotional paradigm. A review of existing literature and findings from users' motivations to participate on the Academia section of the Stack Exchange social questioning‐and‐answering site make our case. We uncovered tensions between the intended information‐centric focus of the community and users who believed social, emotional, and affective considerations needed to be foregrounded, speaking to online communities acting as boundary objects, with the "fit" for one user or community not always the same as for another. An integrated socio‐emotional paradigm shows much strength for social informatics and information science research, including uncovering hidden concerns and differences in values, as in our study. Affective and emotional research, often bubbling under in information science, should rise to the surface is not so much a paradigm shift but an integration of social, emotional, and affective considerations into a socio‐emotional paradigm. [ABSTRACT FROM AUTHOR]

The popular outdoor pursuit of backpacking is profoundly changing as the community embraces contemporary information technologies. However, there is little empirical evidence on the adoption and use of consumer electronics by backpackers, nor the implications this has for their habits, practices, and interactions. We investigate long‐distance backpackers' articulations with mobile information technology during the TGO Challenge, a coast‐to‐coast crossing of the Scottish Highlands. By employing mixed methods, we explore how and why backpackers use such technology when planning and undertaking their journeys via a survey (n = 116), pre‐ and post‐challenge interviews with selected TGO participants, and daily in‐field video‐logs. Our results suggest many advantages to using technology in this context, including fluidity of communications and access, while noting that reliance on technology is leading to issues such as increased need for battery power management, and deskilling. The findings highlight implications for the juxtaposition between outdoor recreation, information behavior, and human computer interaction (HCI) and suggest future work in this area. [ABSTRACT FROM AUTHOR]

This empirical research examines the effects of the COVID‐19 pandemic on information management (IM) in remote or hybrid work. We present an in‐depth statistical analysis of 716 responses to questionnaires received from employees and managers of four Israeli government ministries. The participants were asked to report characteristics such as accessibility, retrieval speed, ease of locating, and relevance of information, in order to assess the quality of organizational IM before and during COVID‐19. The findings reveal that IM quality was maintained even when organizations were forced to quickly adapt to working remotely during the pandemic. Regardless of work location, differences in perception of IM were found among organizations of different sizes: large, medium, and small. The majority of respondents who reported not using IM systems (IMS) before COVID‐19 also stated that even after the pandemic's onset, they still did not use them. A lower frequency of IMS use has been associated with a decline in IM quality. Given the far‐reaching changes in IM induced by the pandemic, many of which have the potential to be long‐lasting, these findings serve as an opening for valuable future research. [ABSTRACT FROM AUTHOR]

Bullet screen comments (BSCs) are user‐generated short comments that appear as real‐time overlays on many video platforms, expressing the audience opinions and emotions about different aspects of the ongoing video. Unlike traditional long comments after a show, BSCs are often incomplete, ambiguous in context, and correlated over time. Current studies in sentiment analysis of BSCs rarely address these challenges, motivating us to develop an aspect‐level sentiment analysis framework. Our framework, BSCNET, is a pre‐trained language encoder‐based deep neural classifier designed to enhance semantic understanding. A novel neighbor context construction method is proposed to uncover latent contextual correlation among BSCs over time, and we also incorporate semi‐supervised learning to reduce labeling costs. The framework increases F1 (Macro) and accuracy by up to 10% and 10.2%, respectively. Additionally, we have developed two novel downstream tasks. The first is noisy BSCs identification, which reached F1 (Macro) and accuracy of 90.1% and 98.3%, respectively, through fine‐tuning the BSCNET. The second is the prediction of future episode popularity, where the MAPE is reduced by 11%–19.0% when incorporating sentiment features. Overall, this study provides a methodology reference for aspect‐level sentiment analysis of BSCs and highlights its potential for viewing experience or forthcoming content optimization. [ABSTRACT FROM AUTHOR]

Bibliometric‐enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data‐driven approach, this article describes the development of a bibliometric‐enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric‐enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known‐item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions. [ABSTRACT FROM AUTHOR]

Evaluating the quality of online health information (OHI) is a major challenge facing consumers. We designed PageGraph, an interface that displays quality indicators and associated values for a webpage, based on credibility evaluation models, the nudge theory, and existing empirical research concerning professionals' and consumers' evaluation of OHI quality. A qualitative evaluation of the interface with 16 participants revealed that PageGraph rendered the information and presentation nudges as intended. It provided the participants with easier access to quality indicators, encouraged fresh angles to assess information credibility, provided an evaluation framework, and encouraged validation of initial judgments. We then conducted a quantitative evaluation of the interface involving 60 participants using a between‐subject experimental design. The control group used a regular web browser and evaluated the credibility of 12 preselected webpages, whereas the experimental group evaluated the same webpages with the assistance of PageGraph. PageGraph did not significantly influence participants' evaluation results. The results may be attributed to the insufficiency of the saliency and structure of the nudges implemented and the webpage stimuli's lack of sensitivity to the intervention. Future directions for applying nudges to support OHI evaluation were discussed. [ABSTRACT FROM AUTHOR]