<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>It is imperative that all stakeholders within the research ecosystem take responsibility to improve research integrity and reliability of published research. Based on the unique experiences of a specialist publishing ethics and research integrity team within a major publisher, this article provides insights into the observed trends of misconduct and how those have evolved over time, and addresses key actions needed to improve the interface between researchers, funders, institutions and publishers to collectively improve research integrity on a global scale.</p>
</abstract>

Perspectives from a publishing ethics and research integrity team for required improvements

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>We utilize a modified <italic>Sigmoid</italic> function to handle the fat-tail distributed citation counts. We also remove the target paper in calculating the contribution of co-citations. Following previous studies, we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society (APS) and the Microsoft Academic Graph (MAG) dataset to test the accuracy of our proposed method (NCCAS). In addition, we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS.</p>
</sec>
<sec>
<h3>Finding</h3>
<p>Compared with the state-of-the-art methods, NCCAS gives the most accurate prediction of Nobel laureates. Furthermore, the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors. The results by NCCAS are also more robust to malicious manipulation. Finally, we perform ablation studies to show the contribution of different components in our methods.</p>
</sec>
<sec>
<h3>Research limitations</h3>
<p>Due to limited ground truth on the true leading author of a work, the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers.</p>
</sec>
<sec>
<h3>Practical implications</h3>
<p>NCCAS is successfully applied to a large number of publications, demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>Compared with existing methods, NCCAS not only identifies the leading author of a paper more accurately, but also makes the deification more distinguishable and more robust, providing a new tool for related studies.</p>
</sec>
</abstract>

An author credit allocation method with improved distinguishability and robustness

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>This paper investigated the differences between conference papers and journal papers in the field of computer science based on Bayesian network, a knowledge-representative framework that can model relationships among all variables in the network. We defined the variables required for Bayesian networks modeling, calculated the values of each variable based Aminer dataset (a literature data set in the field of computer science), learned the Bayesian network and derived some findings based on network inference.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>The study found that conferences are more attractive to senior scholars, the academic impact of conference papers is slightly higher than journal papers, and it is uncertain whether conference papers are more innovative than journal papers.</p>
</sec>
<sec>
<h3>Research limitations</h3>
<p>The study was limited to the field of computer science and employed Aminer dataset as the sample. Further studies involving more diverse datasets and different fields could provide a more complete picture of the matter.</p>
</sec>
<sec>
<h3>Practical implications</h3>
<p>By demonstrating that Bayesian networks can effectively analyze issues in Scientometrics, the study offers valuable insights that may enhance researchers’ understanding of the differences between journal and conference in computer science.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>Academic conferences play a crucial role in facilitating scholarly exchange and knowledge dissemination within the field of computer science. Several studies have been conducted to examine the distinctions between conference papers and journal papers in terms of various factors, such as authors, citations, h-index and others. Those studies were carried out from different (independent) perspectives, lacking a systematic examination of the connections and interactions between multiple perspectives. This paper supplements this deficiency based on Bayesian network modeling.</p>
</sec>
</abstract>

Differences between journal and conference in computer science: a bibliometric view based on Bayesian network

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>Nowadays, public opinions during public emergencies involve not only textual contents but also contain images. However, the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis, lacking the combination of multimodal contents. In this paper, we propose to combine texts and images generated in the social media to perform sentiment analysis.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>We propose a Deep Multimodal Fusion Model (DMFM), which combines textual and visual sentiment analysis. We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis. BiLSTM is employed to generate encoded textual embeddings. To fully excavate visual information from images, a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy. A multimodal fusion method is implemented to fuse textual and visual embeddings completely, producing predicted labels.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>We performed extensive experiments on Weibo and Twitter public emergency datasets, to evaluate the performance of our proposed model. Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models. The introduction of images can boost the performance of sentiment analysis during public emergencies.</p>
</sec>
<sec>
<h3>Research limitations</h3>
<p>In the future, we will test our model in a wider dataset. We will also consider a better way to learn the multimodal fusion information.</p>
</sec>
<sec>
<h3>Practical implications</h3>
<p>We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>We consider the images posted by online users during public emergencies on social platforms. The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.</p>
</sec>
</abstract>

Multimodal sentiment analysis for social media contents during public emergencies

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>Media exaggerations of health research may confuse readers’ understanding, erode public trust in science and medicine, and cause disease mismanagement. This study built artificial intelligence (AI) models to automatically identify and correct news headlines exaggerating obesity-related research findings.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>We searched popular digital media outlets to collect 523 headlines exaggerating obesity-related research findings. The reasons for exaggerations include: inferring causality from observational studies, inferring human outcomes from animal research, inferring distant/end outcomes (e.g., obesity) from immediate/intermediate outcomes (e.g., calorie intake), and generalizing findings to the population from a subgroup or convenience sample. Each headline was paired with the title and abstract of the peer-reviewed journal publication covered by the news article. We drafted an exaggeration-free counterpart for each original headline and fined-tuned a BERT model to differentiate between them. We further fine-tuned three generative language models—BART, PEGASUS, and T5 to autogenerate exaggeration-free headlines based on a journal publication’s title and abstract. Model performance was evaluated using the ROUGE metrics by comparing model-generated headlines with journal publication titles.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>The fine-tuned BERT model achieved 92.5% accuracy in differentiating between exaggeration-free and original headlines. Baseline ROUGE scores averaged 0.311 for ROUGE-1, 0.113 for ROUGE-2, 0.253 for ROUGE-L, and 0.253 ROUGE-Lsum. PEGASUS, T5, and BART all outperformed the baseline. The best-performing BART model attained 0.447 for ROUGE-1, 0.221 for ROUGE-2, 0.402 for ROUGE-L, and 0.402 for ROUGE-Lsum.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>This study demonstrated the feasibility of leveraging AI to automatically identify and correct news headlines exaggerating obesity-related research findings.</p>
</sec>
</abstract>

Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings

AHEAD OF PRINT

Volume 9 (2024): Issue 1 (February 2024)

Volume 8 (2023): Issue 4 (November 2023)

Volume 8 (2023): Issue 3 (June 2023)

Volume 8 (2023): Issue 2 (April 2023)

Volume 8 (2023): Issue 1 (February 2023)

Volume 7 (2022): Issue 4 (November 2022)

Volume 7 (2022): Issue 3 (August 2022)

Volume 7 (2022): Issue 2 (April 2022)

Volume 7 (2022): Issue 1 (February 2022)

Volume 6 (2021): Issue 4 (November 2021)

Volume 6 (2021): Issue 3 (June 2021)

Volume 6 (2021): Issue 2 (April 2021)

Volume 6 (2021): Issue 1 (February 2021)

Volume 5 (2020): Issue 4 (November 2020)

Volume 5 (2020): Issue 3 (August 2020)

Volume 5 (2020): Issue 2 (April 2020)

Volume 5 (2020): Issue 1 (February 2020)

Volume 4 (2019): Issue 4 (December 2019)

Volume 4 (2019): Issue 3 (August 2019)

Volume 4 (2019): Issue 2 (May 2019)

Volume 4 (2019): Issue 1 (February 2019)

Volume 3 (2018): Issue 4 (November 2018)

Volume 3 (2018): Issue 3 (August 2018)

Volume 3 (2018): Issue 2 (May 2018)

Volume 3 (2018): Issue 1 (February 2018)

Volume 2 (2017): Issue 4 (December 2017)

Volume 2 (2017): Issue 3 (August 2017)

Volume 2 (2017): Issue 2 (May 2017)

Volume 2 (2017): Issue 1 (February 2017)

Volume 1 (2016): Issue 4 (November 2016)

Volume 1 (2016): Issue 3 (August 2016)

Volume 1 (2016): Issue 2 (May 2016)

Volume 1 (2016): Issue 1 (February 2016)

Journal of Data and Information Science

Journal of Data and Information Science (JDIS, formerly Chinese Journal of Library and Information Science), sponsored by the Chinese Academy of Sciences (CAS) and published quarterly by the National Science Library of CAS, is the first internationally published English-language academic journal in Library and Information Science and related fields from China.  The Journal of Data and Information Science (JDIS) focuses on data-based research oriented toward the exploration of scientific research and innovation. The main areas of interest are science of science, evidence-based policymaking, research evaluation, computational social science, and scientometrics/bibliometrics/altmetrics/ informetrics. Emphasis is given to research that focuses on data, analytics, and knowledge discovery, and supports decision making and science policy. This includes modeling, innovation, data security, media and communications, and social development. Topics may include studies of metadata or full content data, text or non-textural data, structured or non-structural data, domain-specific or cross-domain data, and dynamic or interactive data.  Specific topic areas may include (but are not limited to):    Knowledge organization  Knowledge discovery and data mining  Knowledge integration and fusion  Semantic Web  Science of science  Bibliometrics and scientometrics  Analytic and diagnostic informetrics  Competitive intelligence  Predictive analysis  Social network analysis and metrics  Semantic and interactively analytic retrieval  Evidence-based policy analysis  Intelligent knowledge production  Knowledge-driven workflow management and decision-making  Knowledge-driven collaboration and its management  Domain knowledge infrastructure with knowledge fusion and analytics  Training for data &amp; information scientists  Development of data and information services    JDIS publishes theoretical and empirical work. Systematic reviews are welcome and applied research in development of advanced methods, services, and best practices is also an important part. But simple application of established informetrics on a specific research field or country is out of the scope.Welcome to submit your papers to JDIS.  Why subscribe and read  JDIS is the first and only English journal from China in Library and Information Science and related fields. With an aim to disseminate the cutting-edge research in these fields, it is devoted to the study and application of the theories, methods, techniques, services, and infrastructural facilities using big data to support knowledge discovery for decision and policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. JDIS has gathered a big body of high profile experts across the world who contribute their research to the journal. The international authors account for around 62% in its first publication year (2016).  Why submit  JDIS is the first and only English journal from China in Library and Information Science and related fields. It owns a number of world front-line scholars as editorial board members or reviewers. The turnaround time on average for a manuscript from submission to final decision is less than two and a half months.  Archiving  Sciendo archives the contents of this journal in Portico- digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 