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This study employs bibliometric analysis to map the research landscape of social media trending topics during the COVID-19 pandemic. The authors aim to offer a comprehensive review of the predominant research organisations and countries, key themes and favoured research methodologies pertinent to this subject.

The authors extracted data on social media trending topics from the Web of Science Core Collection database, spanning from 2009 to 2022. A total of 1,504 publications were subjected to bibliometric analysis, utilising the VOSviewer tool. The study analytical process encompassed co-occurrence, co-authorship, citation analysis, field mapping, bibliographic coupling and co-citation analysis.

Interest in social media research, particularly on trending topics during the COVID-19 pandemic, remains high despite signs of the pandemic stabilising globally. The study predominantly addresses misinformation and public health communication, with notable focus on interactions between governments and the public. Recent studies have concentrated on analysing Twitter user data through text mining, sentiment analysis and topic modelling. The authors also identify key leading organisations, countries and journals that are central to this research area.

Diverging from the narrow focus of previous literature reviews on social media, which are often confined to particular fields or sectors, this study offers a broad view of social media's role, emphasising trending topics. The authors demonstrate a significant link between social media trends and public events, such as the COVID-19 pandemic. The paper discusses research priorities that emerged during the pandemic and outlines potential methodologies for future studies, advocating for a greater emphasis on qualitative approaches.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2023-0194.

The purpose of this paper is to propose a novel collaborative trend prediction method to estimate the status of trending topics by crowdsourcing the wisdom in web search engines. Government officials and decision makers can take advantage of the proposed method to effectively analyze various trending topics and make appropriate decisions in response to fast-changing national and international situations or popular opinions.

In this study, a crowdsourced-wisdom-based feature selection method was designed to select representative indicators showing trending topics and concerns of the general public. The authors also designed a novel prediction method to estimate the trending topic statuses by crowdsourcing public opinion in web search engines.

The authors’ proposed method achieved better results than traditional trend prediction methods and successfully predict trending topic statuses by using the crowdsourced wisdom of web search engines.

This paper proposes a novel collaborative trend prediction method and applied it to various trending topics. The experimental results show that the authors’ method can successfully estimate the trending topic statuses and outperform other baseline methods. To the best of the authors’ knowledge, this is the first such attempt to predict trending topic statuses by using the crowdsourced wisdom of web search engines.

The purpose of this paper is to identify and analyse the news coverage and sentiment of real estate-related trends in Germany. Trends are considered as being stable and long-term. If the news coverage and sentiment of trends underlie cyclicity, this could impact investors’ behaviour. For instance, in the case of increased reporting on sustainability issues, investors may be inclined to invest more in sustainable buildings, assuming that this is of growing importance to their clients. Hence, investors could expect higher returns when a trend topic goes viral.

With the help of topic modelling, incorporating seed words partially generated via word embeddings, almost 170,000 newspaper articles published between 1999 and 2019 by a major German real estate news provider are analysed and assigned to real estate-related trends. Through applying a dictionary-based approach, this dataset is then analysed based on whether the tone of the news coverage of a specific trend is subject to change.

The articles concerning urbanisation and globalisation account for the largest shares of reporting. However, the shares are subject to change over time, both in terms of news coverage and sentiment. In particular, the topic of sustainability illustrates a clearly increasing trend with cyclical movements throughout the examined period. Overall, the digitalisation trend has a highly positive connotation within the analysed articles, while regulation displays the most negative sentiment.

To the best of the authors’ knowledge, this is the first application to explore German real estate newspaper articles regarding the methodologies of word representation and seeded topic modelling. The integration of topic modelling into real estate analysis provides a means through which to extract information in a standardised and replicable way. The methodology can be applied to several further fields like analysing market reports, company statements or social media comments on real estate topics. Finally, this is also the first study to measure the cyclicity of real estate-related trends by means of textual analysis.

Virtually all contemporary scientific papers studying careers emphasize its changing nature. Indeed, careers have been changing during recent decades, for example becoming more complex and unpredictable. Furthermore, hallmarks of the new career – such as individual agency – are clearly increasing in importance in today’s labor market. This led the authors to ask the question of whether these changes are actually visible in the topics that career scholars research. In other words, the purpose of this paper is to discover the trending topics in careers.

To achieve this goal, the authors analyzed all published papers from four core career journals (i.e. Career Development International, Career Development Quarterly, Journal of Career Assessment, and Journal of Career Development) between 2012 and 2016. Using a five-step procedure involving three researchers, the authors formulated the 16 most trending topics.

Some traditional career topics are still quite popular today (e.g. career success as the #1 trending topic), whereas other topics have emerged during recent years (e.g. employability as the #3 trending topic). In addition, some topics that are closely related to career research – such as unemployment and job search – surprisingly turned out not to be a trending topic.

In reviewing all published papers in CDI, CDQ, JCA, and JCD between 2012 and 2016, the authors provide a unique overview of currently trending topics, and the authors compare this to the overall discourse on careers. In addition, the authors formulate key questions for future research.

The purpose of this paper is to present a novel approach for mining scientific trends using topics from Call for Papers (CFP). The work contributes a valuable input for researchers, academics, funding institutes and research administration departments by sharing the trends to set directions of research path.

The authors procure an innovative CFP data set to analyse scientific evolution and prestige of conferences that set scientific trends using scientific publications indexed in DBLP. Using the Field of Research code 804 from Australian Research Council, the authors identify 146 conferences (from 2006 to 2015) into different thematic areas by matching the terms extracted from publication titles with the Association for Computing Machinery Computing Classification System. Furthermore, the authors enrich the vocabulary of terms from the WordNet dictionary and Growbag data set. To measure the significance of terms, the authors adopt the following weighting schemas: probabilistic, gram, relative, accumulative and hierarchal.

The results indicate the rise of “big data analytics” from CFP topics in the last few years. Whereas the topics related to “privacy and security” show an exponential increase, the topics related to “semantic web” show a downfall in recent years. While analysing publication output in DBLP that matches CFP indexed in ERA Core A* to C rank conference, the authors identified that A* and A tier conferences not merely set publication trends, since B or C tier conferences target similar CFP.

Overall, the analyses presented in this research are prolific for the scientific community and research administrators to study research trends and better data management of digital libraries pertaining to the scientific literature.

This study aims to identify the trends that have changed in the field of construction management over the last 20 years.

In this study, 3,335 journal articles published in the years 2000–2020 were collected from the Web of Science database in construction management. The authors applied bibliometric analysis first and then detected topics with the latent Dirichlet allocation (LDA) topic detection method.

In this context, 20 clusters from cluster analysis were found and the topics were extracted in clusters with the LDA topic detection method. The results show “building information modeling” and “information management” are the most studied subjects, even though they have emerged in the last 15 years “building information modeling,” “information management,” “scheduling and cost optimization,” “lean construction,” “agile approach” and “megaprojects” are the trend topics in the construction management literature.

This study uses bibliometric analysis. The authors accept that the co-citation and co-authorship relationship in the data is ethical. They accept that honorary authorship, self-citation or honorary citation do not change the pattern of the construction management research domain.

There has been no study conducted in the last 20 years to examine research trends in construction management. Although bibliometric analysis, systematic literature reviews and text mining methods are used separately as a methodology for extracting research trends, no study has used enhanced bibliometric analysis and the LDA topic detection text mining method.

The purpose of this study is to discover the distribution and trends of existing Offsite construction (OSC) literature with an intention to highlight research niches and propose the future outline.

The paper adopted literature reviews methodology involving 1,057 relevant documents published in 2008-2017 from 15 journals. The selected documents were empirically analyzed through a topic-modeling technique. A latent Dirichlet allocation model was applied to each document to infer 50 key topics. A machine learning for language toolkit was used to get topic posterior word distribution and word composition.

This is an exploratory study, which identifies the distribution of topics and themes; the trend of topics and themes; journal distribution trends; and comparative topic, themes and journal distribution trend. The distribution and trends show an increase in researcher’s interest and the journal’s priority on OSC research. Nevertheless, OSC existing literature is faced with; under-researched topics such as building information modeling, smart construction and marketing. The under-researched themes include organizational management, supply chain and context. The authors also found an overload of similar information in prefabrication and concrete topics. Furthermore, the innovative methods and constraints themes were found to be overloaded with similar information.

The naming of the themes was based on our own interpretation; hence, the research results may lack generalizability. Therefore, a comparative study using different data processing is proposed. The study also provides future research outline as follows: studying OSC topics from dynamic evolution perspective and identifying the new emerging topics; searching for effective strategies to enhance OSC research; identifying the contribution of countries, affiliation and funding agency; and studying the impact of these themes to the adoption of OSC.

This study is of values to the scholars, as it could stimulate research to under-researched areas.

This paper justifies a need to have a broad understanding of the nature and structure of existing OSC literature.

The purpose of this article is to track the emergence of topics and research trends in environmental accounting research by using a machine learning method for literature reviews. The article shows how the method can track the emergence of topics and research trends over time.

The analysis of the emergence of topics and shifts in research trends was based on a machine learning approach that allowed the authors to identify “topic bursts” in publication data. The data set of this study contained, 2,502 records published between 1972 and 2019, both within and outside of accounting journals. The data set was assembled through a systematic keyword search of the literature.

Findings indicated that research studies within accounting journals have addressed sustainability concerns in a general fashion, with a recent focus on broad topics such as corporate social responsibility (CSR) and stakeholder theory. Research studies published outside of accounting journals have focussed on more specific topics (e.g. the shift to a low-carbon or circular economy, the attainment of the sustainable development goals [SDGs], etc.) and new methodologies (e.g. accounting for ecosystem services).

The method provides an approach for identifying “trending” topics within accounting and non-accounting journals and allows to identify topics and areas that could benefit from a greater exchange of ideas between accounting and non-accounting journals.

The authors provide a much needed review of research on the vitally important topic of environmental accounting not only in accounting journals but also in the broader research community.

This study aims to explore knowledge structure and research trends in the domain of digital humanities (DH) in the recent decade. The study identified prevailing topics and then, analyzed trends of such topics over time in the DH field.

Research bibliographic data in the area of DH were collected from scholarly databases. Multiple text mining techniques were used to identify prevailing research topics and trends, such as keyword co-occurrences, bigram analysis, structural topic models and bi-term topic models.

Term-level analysis revealed that cultural heritage, geographic information, semantic web, linked data and digital media were among the most popular topics in the recent decade. Structural topic models identified that linked open data, text mining, semantic web and ontology, text digitization and social network analysis received increased attention in the DH field.

This study applied existent text mining techniques to understand the research domain in DH. The study collected a large set of bibliographic text, representing the area of DH from multiple academic databases and explored research trends based on structural topic models.

Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest.

In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count.

The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data.

The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems.

Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts.

This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.