Modeling and Analyzing of Research Topic Evolution Associated with Social Networks of Researchers

2016 ◽  
Vol 7 (3) ◽  
pp. 42-62
Author(s):  
Wei Liang ◽  
Zixian Lu ◽  
Qun Jin ◽  
Yonghua Xiong ◽  
Min Wu
Keyword(s):  
Social Networks ◽  
Research Topic ◽  
Research Trends ◽  
Major Research ◽  
Research Topics ◽  
Academic Literature ◽  
Topic Evolution ◽  
Academic Topic ◽  
Evolution Modeling ◽  
Scientific Collaborations

Research trends keep evolving along the time with certain trackable patterns. Mining academic literature and discovering the latent research trends evolution is an interesting and important problem. Few of previous studies focusing on academic topic evolution modeling have addressed the temporal topic evolution patterns. In addition, researchers' profile and their social networks are valuable complementary to the research trends tracking. In this study, to analyze the underlying research trends evolution along with the scientific collaborations of researchers, a novel temporal research trends evolution model associated with researchers' social networks is proposed and built. Specifically, the detected research topics are classified into different clusters in each timeslot, and the evolution patterns are deduced among these topic clusters. The effectiveness of our approach is evaluated based on a real academic dataset. The experimental results can help users to discover the major research trends for specific fields. Besides, the tracked statuses of the corresponding scientific groups are helpful for searching research trends or finding collaboration opportunities according to researchers' different requirements.

scholarly journals Technological forecasting related to the energy sector: a scientometric overview

2020 ◽  
Vol 209 ◽  
pp. 02022
Author(s):  
Alexey V. Mikheev
Keyword(s):  
Energy Sector ◽  
Research Topic ◽  
Energy Technology ◽  
Knowledge Domain ◽  
Technology Knowledge ◽  
Technological Forecasting ◽  
Topic Evolution ◽  
Key Points ◽  
Scopus Database ◽  
Oriented Research

Scientometric review of trends and key points of technological forecasting related to the energy sector is carried out in this study. Using co-keyword, co-citation techniques to analyze a set of research and review articles indexed in the Scopus database, clustered networks were built to understand content relationships and research topic evolution within the 2000-2019 period. This study provides an overview of future-oriented research efforts and trends in the energy technology knowledge domain.

scholarly journals Social Networks in Modern Political Communications: a Concept Analysis of Foreign Periodicals

2021 ◽  
Vol 2021 (3) ◽  
pp. 289-303
Author(s):  
Aleksandra Pisareva
Keyword(s):  
Social Networks ◽  
Mass Media ◽  
New Media ◽  
Political Communication ◽  
Research Topic ◽  
The Internet ◽  
Similar Amount ◽  
Protest Movements ◽  
Role Of The State

Over the past two decades, Russian and foreign researchers have documented the growing influence of social networks on political communication. The Internet has become a new mass media. In Russia, bloggers with more than 3,000 subscribers acquire a mass media status. Internet users are not passive recipients of messages: they distribute them and generate their own content. The Internet is a different kind of reality, where anything is possible. Traditional mass media are less efficient than the Internet in providing news. As a result, the Internet and social networks have become a new means of political interaction. The COVID-19 pandemic boosted the digitalization of mass communications and made this process irreversible. The present article reviews 250 foreign research papers published by Taylor and Francis, Oxford University Press, and SAGE Publications in 2020–2021. The objective was to determine the attractiveness of political communication in social networks as a research topic. 12 % of the articles featured the role of social nets in political communication, Facebook being the most popular research material. A similar amount of papers focused on the behavior of network users and the role of the state in the management of social networks. Foreign terms used to describe the research topic appeared to be different from those used by Russian linguists. For instance, foreign authors use "social media" as a synonym for "sites of social networks", while Russian scientists prefer a much broader interpretation. Some terms and acronyms, such as SNSa, are absent from Russian works. Foreign authors exploit classical political science theories to study the issues of political content, the effect of social networks on protest movements and racial conflicts, and the use of new media in election campaigns. They are unfamiliar with Russian approaches to empirical data analysis, e.g. theory of "weak ties", "close world", two-stage flow of communication, the concept of "third place", etc.

scholarly journals Evaluation of research topic evolution in psychiatry using co-word analysis

Medicine ◽  
2017 ◽  
Vol 96 (25) ◽  
pp. e7349 ◽  
Author(s):  
Ying Wu ◽  
Xing Jin ◽  
Yunzhen Xue
Keyword(s):  
Research Topic ◽  
Topic Evolution ◽  
Word Analysis

Joint Embedding of Words and Labels for Sentiment Classification

2021 ◽  
Vol 31 (01) ◽  
Author(s):  
Yingwei Sheng ◽  
Inui Takashi
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Research Topic ◽  
Sentiment Classification ◽  
Word Embeddings ◽  
Baseline Model ◽  
Traditional Methods ◽  
Joint Embedding ◽  
The Web

With the fast growth of social networks, sentiment analysis on the web has been a popular research topic. Recently, word embedding-based sentiment analysis methods have reached outstanding performance compared to the traditional methods. However, word embeddings always ignore information from dataset’s labels. Inspired by the LEAM model proposed by Wang et al. [Joint embedding of words and labels for text classification (2018), arXiv:1805.04174], we propose a method that jointly learns information of words and sentiment labels, which can improve the performance of the label embedding model. We defined a set of sentiment lexicons and used it to represent sentiment labels in the proposed method. We finally conducted experiments on the Yelp dataset, which reached 65.03% accuracy when using the same setup as the baseline model, and 65.22% accuracy when using optional window sizes.

Considering social information in constructing research topic maps

2018 ◽  
Vol 36 (2) ◽  
pp. 220-236 ◽  
Author(s):  
Hei Chia Wang ◽  
Yu Hung Chiang ◽  
Yen Tzu Huang
Keyword(s):  
Social Networks ◽  
Data Collection ◽  
Social Information ◽  
Research Topic ◽  
Theory And Practice ◽  
Specific Domain ◽  
Data Set ◽  
Content Type ◽  
Topic Maps ◽  
The Future

Purpose In academic work, it is important to identify a specific domain of research. Many researchers may look to conference issues to determine interesting or new topics. Furthermore, conference issues can help researchers identify current research trends in their field and learn about cutting-edge developments in their area of specialization. However, so much conference information is published online that it can be difficult to navigate and analyze in a meaningful or productive way. Hence, the use of knowledge management (KM) could be a way to resolve these issues. In KM, ontology is widely adopted, but most ontology construction methods do not consider social information between target users. Therefore, this study aims to propose a novel method of constructing research topic maps using an open directory project (ODP) and social information. Design/methodology/approach The approach is to incorporate conference information (i.e. title, keywords and abstract) as sources and to consider the ways in which social information automatically produces research topic maps. The methodology can be divided into four modules: data collection, element extraction, social information analysis and visualization. The data collection module collects the required conference data from the internet and performs pre-processing. Then, the element extraction module extracts topics, associations and other basic elements of topic maps while considering social information. Finally, the results will be shown in the visualization module for researchers to browse and search. Findings The results of this study propose three main findings. First, creating topic maps with the ODP category information can help capture a richer set of classification associations. Second, social information should be considered when constructing topic maps. This study includes the relationship among different authors and topics to support information in social networks. By considering social information, such as co-authorship/collaborator, this method helps researchers find research topics that are unfamiliar but interesting or potential cooperative opportunities in the future. Third, this study presents topic maps that show a clear and simple pathway in interested domain knowledge. Research limitations implications First, this study analyzes and collects conference information, including the titles, keywords and abstracts of conference papers, so the data set must include all of the abovementioned information. Second, social information only analyzes co-authorship associations (collabship associations); other social information could be extracted in the future study. Third, this study only analyzes the associations between topics. The intensity of associations is not discussed in the study. Originality/value The study will have a great impact on learned societies because it bridges the gap between theory and practice. The study is useful for researchers who want to know which conferences are related to their research. Moreover, social networks can help researchers expand and diversify their research.

scholarly journals The Use of Manipulative Technologies to Promote the Services of the Tourism and Hospitality Industry

2021 ◽  
Vol 20 (2) ◽  
pp. 32-40
Author(s):  
Elena M. Kryukova ◽  
◽  
Valeria Sh. Khetagurova ◽  
Keyword(s):  
Social Networks ◽  
Hospitality Industry ◽  
Research Topic ◽  
Goods And Services ◽  
Tourism Sector ◽  
Travel Agencies ◽  
The World ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality ◽  
Automation Tools

The tourism and hospitality industry has been actively developing recently. And the high competition among travel agencies leads to the search for new ways to promote tourist services. Therefore, most commercial companies, including the tourism sector, use the Internet to promote their goods and services. The relevance of the research topic is due to the fact that currently, more and more people around the world use social networks. Therefore, in comparison with other platforms, social networks not only have a better global reach, but also contain more information about users. This makes them a popular tool for manipulation. Manipulation in social networks consists in the application of a number of methods that use automation tools and certain algorithms of social networks.

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