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Proper topic selection is an essential prerequisite for the success of research. To study this, this article proposes an important concerned factor of topic selection-topic popularity, to examine the relationship between topic selection and team performance.

The authors adopt extracted entities on the type of gene/protein, which are used as proxies as topics, to keep track of the development of topic popularity. The decision tree model is used to classify the ascending phase and descending phase of entity popularity based on the temporal trend of entity occurrence frequency. Through comparing various dimensions of team performance – academic performance, research funding, relationship between performance and funding and corresponding author's influence at different phases of topic popularity – the relationship between the selected phase of topic popularity and academic performance of research teams can be explored.

First, topic popularity can impact team performance in the academic productivity and their research work's academic influence. Second, topic popularity can affect the quantity and amount of research funding received by teams. Third, topic popularity can impact the promotion effect of funding on team performance. Fourth, topic popularity can impact the influence of the corresponding author on team performance.

This is a new attempt to conduct team-oriented analysis on the relationship between topic selection and academic performance. Through understanding relationships amongst topic popularity, team performance and research funding, the study would be valuable for researchers and policy makers to conduct reasonable decision making on topic selection.

The integration of social media and e-commerce has resulted in a rising phenomenon among individual content providers (ICPs), who used to offer free content, to provide consumers with paid content, such as online courses, Q&As or consultations. Despite the prevalence of ICPs’ content monetization, empirical research has rarely studied its underlying mechanism. This paper examines how the characteristics of free content contributed by ICPs on social media platforms influence their paid content sales, focusing on the perspective of human brand.

The empirical setting is an online knowledge exchange platform, where users are allowed to provide free content (e.g. answers) on the social media platform and launch paid content (e.g. lectures) on the e-commerce platform. A machine learning technique is employed to construct measures for the characteristics of free content, and fixed-effects estimation is presented to confirm which factors have a significant influence on the sales of paid content.

The empirical results show that the quality, diversity and expertness of free content have a significant positive impact on the sales of the ICP-paid content, with the brand popularity of ICP playing a mediating role.

This study is the first attempt to demystify the relationship between content contribution and ICPs’ content monetization from the perspective of human brand. The findings validate the effectiveness of the “Selling by Contribution” strategy and provide valuable insights for ICPs and social media platforms.

The purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for lack of sufficient data.

Based on GM(1,1) and neural networks, a co-training model for topic tendency prediction is proposed in this paper. The interpolation based on GM(1,1) is employed to generate fine-grained prediction values of topic popularity time series and two neural network models are considered to achieve convergence by transmitting training parameters via their loss functions.

The experiment results indicate that the integrated model can effectively predict dense sequence with higher performance than other algorithms, such as NN and RBF_LSSVM. Furthermore, the Markov chain state transition probability matrix model is used to improve the prediction results.

Fine-grained and long-term topic popularity prediction, further improvement could be made by predicting any interpolation in the time interval of popularity data points.

The paper succeeds in constructing a co-training model with GM(1,1) and neural networks. Markov chain state transition probability matrix is deployed for further improvement of popularity tendency prediction.

Paying to view others' answers is a new mode for question and answer (Q&A) platforms. The purpose is to build a model to explore the determinants of the number of listeners and further explore certain meaningful characteristics of the model in the context of different types of questions and answerers.

The authors develop an empirical model and use real panel data to test the hypothesis. Specifically, cues from the answerer and from the question elicit the listener's trust in the answerer (including direct and indirect trust) and perceived value in the question (including intrinsic and extrinsic attributes), respectively.

The authors find that cues from answerers (experience for paid Q&As and popularity for free Q&As) and questions (length, sentence structure, value and number of likes) all have positive effects on the number of listeners. The impact of answerer authentication is more significant than the popularity of free Q&As. Moreover, the length of the question matters only for subjective questions, while sentence structure matters only for objective questions. In addition, the answerer's own attributes and the behavior and feedback of others have greater impacts when the answerer is below average in popularity.

The authors summarize the unique features of the mode of paying to view others' answers in contrast with the traditional mode of paid Q&As. In addition, the authors focus on the characteristics of the question (including the subjectivity and the sentence structure of the question), a topic which has not been studied previously. Our research provides a reference for exploring user behavior patterns. The practical implications for knowledge platforms are also concretely described.

This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research longitudinally from 1965 to 2015.

The study employs a quantitative intellectual content analysis of articles published in 30+ scholarly LIS journals, following the design by Tuomaala et al. (2014). In the content analysis, we classify articles along eight dimensions covering topical content and methodology.

The topical findings indicate that the earlier strong LIS emphasis on L&I services has declined notably, while scientific and professional communication has become the most popular topic. Information storage and retrieval has given up its earlier strong position towards the end of the years analyzed. Individuals are increasingly the units of observation. End-user's and developer's viewpoints have strengthened at the cost of intermediaries' viewpoint. LIS research is methodologically increasingly scattered since survey, scientometric methods, experiment, case studies and qualitative studies have all gained in popularity. Consequently, LIS may have become more versatile in the analysis of its research objects during the years analyzed.

Among quantitative intellectual content analyses of LIS research, the study is unique in its scope: length of analysis period (50 years), width (8 dimensions covering topical content and methodology) and depth (the annual batch of 30+ scholarly journals).

The purpose of this study is to comprehensively review the human resource management (HRM) and employment relations (ERs) field and explore the knowledge map, knowledge evolution trends and paths and paradigm shifts within this field.

The Structural Topic Model in combination with Word2vec is proposed and applied in this work. First, this paper detects and interprets the research topics by reviewing 23,786 papers from 29 important journals in this field from 1990 to 2021. Then, this research explores popularity trends by aggregating topic proportions from a temporal perspective. Finally, this work explores the research topic evolution from the semantic perspective.

This paper obtains the following findings: (1) Sixteen research topics are identified, which provide the basic research overview of the whole field. (2) The changes in topic popularity over time map the tendency for employee benefits to be valued. (3) The evolutionary trajectories of temporal local topics are provided, which reflect the mechanisms of the paradigm and ideological migration and fusion.

This work adopts state-of-the-art textual as well as semantic mining techniques to establish a comprehensive knowledge map for HRM and ER research. Furthermore, these results uniquely demonstrate the pluralistic ideological orientation at the social level is gradually integrated into more micro levels, such as enterprises and individuals. These are the contents that were mentioned from previous studies by scholars, but not meticulously verified and interpreted.

This paper aims to identify the intellectual structure of four leading hospitality journals over 40 years by applying mixed-method approach, using both machine learning and traditional statistical analyses.

Abstracts from all 4,139 articles published in four top hospitality journals were analyzed using the structured topic modeling and inferential statistics. Topic correlation and community detection were applied to identify strengths of correlations and sub-groups of topics. Trend visualization and regression analysis were used to quantify the effects of the metadata (i.e. year of publication and journal) on topic proportions.

The authors found 50 topics and eight subgroups in the hospitality journals. Different evolutionary patterns in topic popularity were demonstrated, thereby providing the insights for popular research topics over time. The significant differences in topical proportions were found across the four leading hospitality journals, suggesting different foci in research topics in each journal.

Combining machine learning techniques with traditional statistics demonstrated potential for discovering valuable insights from big text data in hospitality and tourism research contexts. The findings of this study may serve as a guide to understand the trends in the research field as well as the progress of specific areas or subfields.

It is the first attempt to apply topic modeling to academic publications and explore the effects of article metadata with the hospitality literature.

This paper aims to showcase the trends in the research topics and their contributors over a time period of 30 years in the International Journal of Contemporary Hospitality Management (IJCHM). To be specific, this paper uncovers IJCHM’s latent topics and hidden patterns in published research and highlights the differences across three decades and before and after Social Sciences Citation indexing.

In total, 1,573 documents published over 199 issues of IJCHM were analyzed using two computational tools, i.e. metaknowledge and structural topic modeling (STM), as the basis of the mixed method. STM was used to discover the evolution of topics over time. Moreover, bibliometrics (and network analysis) were used to highlight IJCHM’s top researchers, top-cited references, the geographical networks of the researchers and differences in the collaborative networks.

The number of papers published continually increased over time with changes of key researchers publishing in IJCHM. The co-authorship networks have also changed and revealed an increasing diversity of authorship and collaborations among authors in different countries. Moreover, the variety of topics and the relative weight of each topic have also changed.

Based on the findings of this study, theoretical and practical implications for hospitality and tourism researchers are provided.

It is the first attempt to apply topic modeling to a leading academic journal in hospitality and tourism and explore the diversity in contemporary hospitality management research (topics and contributors) from 30 years of published research.

Special education is the education segment that deals with the students facing hurdles in the traditional education system. Research data have evolved in the domain of special education due to scientific advances. The present study aims to employ text mining to extract the latent patterns from the scientific data.

This study examined the 12,781 Scopus-indexed titles, abstracts and keywords published from 1987 to 2021 through an integrated text-mining and topic modeling approach. It combines dynamic topic models with highly cited reviews of this domain. It facilitates the extraction of topic clusters and communities in the topic network.

This methodology discovered children’s communication and speech using gaming techniques, mental retardation, cost effect on infant birth, involvement of special education children and their families, assistive technology information for special education, syndrome epilepsy and the impact of group study on skill development peers or self as the hottest topic of research in this domain. In addition to finding research hotspots, it further explores annual topic proportion trends, topic correlations and intertopic research areas.

The results provide a comprehensive summary of the popularity of research topics in special education in the past 34 years, and the results can provide useful insights and implications, and it could be used as a guide for contributors in special education form a structured view of past research and plan future research directions.

Previous research concerning automatic extraction of research topics mostly used rule-based or topic modeling methods, which were challenged due to the limited rules, the interpretability issue and the heavy dependence on human judgment. This study aims to address these issues with the proposal of a new method that integrates machine learning models with linguistic features for the identification of research topics.

First, dependency relations were used to extract noun phrases from research article texts. Second, the extracted noun phrases were classified into topics and non-topics via machine learning models and linguistic and bibliometric features. Lastly, a trend analysis was performed to identify hot research topics, i.e. topics with increasing popularity.

The new method was experimented on a large dataset of COVID-19 research articles and achieved satisfactory results in terms of f-measures, accuracy and AUC values. Hot topics of COVID-19 research were also detected based on the classification results.

This study demonstrates that information retrieval methods can help researchers gain a better understanding of the latest trends in both COVID-19 and other research areas. The findings are significant to both researchers and policymakers.