scholarly journals Research Progress and Development Trend of Social Media Big Data (SMBD): Knowledge Mapping Analysis Based on CiteSpace

2020 ◽  
Vol 9 (11) ◽  
pp. 632
Author(s):  
Ziyi Wang ◽  
Debin Ma ◽  
Ru Pang ◽  
Fan Xie ◽  
Jingxiang Zhang ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Development ◽  
Rapid Development ◽  
The United States ◽  
Research Progress ◽  
High Yield ◽  
Knowledge Mapping ◽  
Human Beings ◽  
Research Status

Social Media Big Data (SMBD) is widely used to serve the economic and social development of human beings. However, as a young research and practice field, the understanding of SMBD in academia is not enough and needs to be supplemented. This paper took Web of Science (WoS) core collection as the data source, and used traditional statistical methods and CiteSpace software to carry out the scientometrics analysis of SMBD, which showed the research status, hotspots and trends in this field. The results showed that: (1) More and more attention has been paid to SMBD research in academia, and the number of journals published has been increased in recent years, mainly in subjects such as Computer Science Engineering and Telecommunications. The results were published primarily in IEEE Access Sustainability and Future Generation Computer Systems the International Journal of eScience and so on; (2) In terms of contributions, China, the United States, the United Kingdom and other countries (regions) have published the most papers in SMBD, high-yield institutions also mainly from these countries (regions). There were already some excellent teams in the field, such as the Wanggen Wan team at Shanghai University and Haoran Xie team from City University of Hong Kong; (3) we studied the hotspots of SMBD in recent years, and realized the summary of the frontier of SMBD based on the keywords and co-citation literature, including the deep excavation and construction of social media technology, the reflection and concerns about the rapid development of social media, and the role of SMBD in solving human social development problems. These studies could provide values and references for SMBD researchers to understand the research status, hotspots and trends in this field.

Social media use by Academic Nuclear Medicine Programs – a value proposition in waiting (Preprint)

2020 ◽  
Author(s):  
Ananya Panda ◽  
Akash Sharma ◽  
Ayca Dundar ◽  
Ann Packard ◽  
Lee Aase ◽  
...  
Keyword(s):  
Social Media ◽  
Nuclear Medicine ◽  
Media Use ◽  
Perceived Value ◽  
The United States ◽  
High Yield ◽  
Social Media Use ◽  
A Value ◽  
Radiology Residency ◽  
Insight Into

BACKGROUND Despite the increasing trend for social media use at large, particularly Twitter by radiologists in recent years, there is little insight into the presence of Nuclear Medicine (NM) and Nuclear Radiology (NR) programs on social media. OBJECTIVE There is scant insight into the presence of Nuclear Medicine (NM) and Nuclear Radiology (NR) programs on social media. Our purpose was to assess Twitter engagement by academic NM/NR programs in the United States. METHODS We measured Twitter engagement by the academic NM/NR community, accounting for various NM/NR certification pathways. The Twitter presence of NM/NR programs at both department and program director (PD) level was identified. Tweets by programs were cross-referenced against potential high-yield NM/NR related hashtags, and tabulated at a binary level. A brief survey was done to identify obstacles and benefits to Twitter use by academic NM/NR faculty. RESULTS For 2019-2020, 88 unique programs offered NM/NR certification pathways. Of these, 52% (46/88) had Twitter accounts and 24% (21/88) had at least one post related to NM/NR. Only three radiology departments had unique Twitter accounts for the NM/Molecular Imaging division. Of remaining 103 radiology residency programs, only 16% (16/103) had presence on Twitter and 5% (5/103) had tweets about NM/NR. Only 9% (8/88) NM/NR PDs were on Twitter, and three PDs tweeted about NM/NR. The survey revealed a lack of clarity and resources for using Twitter, although the respondents acknowledged a perceived value of Twitter engagement for attracting younger trainees. CONCLUSIONS Currently, there is minimal Twitter engagement by the academic NM/NR community. The identifiable obstacles are balanced by perceived value in engagement. Not increasing the social media presence is a missed opportunity for trainees, colleagues, and the public with respect to the value of this subspecialty. CLINICALTRIAL None

New progress in international nanotechnology research in the past ten years – visual analysis based on CitesSpace

2021 ◽  
pp. 1-13
Author(s):  
Yadong Ma
Keyword(s):  
Visual Analysis ◽  
Development Trend ◽  
The United States ◽  
Polymer Particle ◽  
High Yield ◽  
High Tech ◽  
Human Beings ◽  
Main Research ◽  
The Core ◽  
Nanotechnology Research

With the continuous development of high-tech industry, Moore’s law is close to the limit. People urgently need nano science and technology to trigger a new scientific and technological revolution to meet the needs of life, military and so on. Nanotechnology covers almost all industries and has made achievements in the industries such as medical, materials, manufacturing, and information technology. It has changed the production and life of human beings and subverted many industries. In recent years, more and more people have conducted data mining on nanotechnology research. By combing the literature, this paper summarizes the core authors, keyword changes, important authors and emergent words of the existing literature. Contributing to analyzing the research status of this field and revealing research hotspots in this field. It is of great significance for scholars to sort out the development process of nano field and predict the future development trend. Using CitesSpace bibliometric analysis software, 44002 pieces of literature about nanotechnology in SCI and SSCI journals in the core collection of the Web of Science database were analyzed in this paper. The results indicated that countries such as the United States, Germany, China, and Japan have issued more articles; However, the centrality of articles published in European countries such as the UK, Germany, and France was relatively strong; High-yield units mainly included Chinese Acad Sci and Russian Acad Sci; The main research scholars were Wei Wang, Peixuan Guo, Thomas J Webster, Hao Yan; Research emergent words primarily included polymer, particle, dynamics, mechanical properties and silver nanoparticle. On this basis, countermeasure suggestions and prospects are proposed.

scholarly journals Career Adaptability Research: A Literature Review with Scientific Knowledge Mapping in Web of Science

2020 ◽  
Vol 17 (16) ◽  
pp. 5986
Author(s):  
Huaruo Chen ◽  
Tingting Fang ◽  
Fan Liu ◽  
Liman Pang ◽  
Ya Wen ◽  
...  
Keyword(s):  
Scientific Knowledge ◽  
Meaning In Life ◽  
Web Of Science ◽  
Rapid Development ◽  
Research Progress ◽  
Structure Evolution ◽  
Knowledge Mapping ◽  
Career Adaptability ◽  
Psychological Resources ◽  
Main Concept

With the rapid development of society and technology, personal adaptability is becoming more and more important. Learning how to adapt to a changing world is becoming one of the necessary conditions for success. Career adaptability can help individuals to smoothly adapt to changes when coping with their career roles, and maintain their ability to balance their career roles, which will affect their important psychological resources for career development and achieve more meaning in life. In recent years, career adaptability has gradually attracted the attention of researchers. Therefore, in order to explore the main factors, such as research focus, the main researchers, its evolution, and the important results of career adaptability in the last ten years, this study used the scientific knowledge mapping software CiteSpace as a research tool, and select related articles from the Web of Science between 2010 to 2020 under the theme of “career adaptability” for data analysis, which can help future researchers to understand current and future career adaptability research and control the research direction of career adaptability. The results of this research indicate that there are direct or indirect connections between different themes, such as the career adaptability scale, career construction, positive personalities, and so on, but few articles integrate multiple research topics. At the same time, the main researchers, research frontiers and network relationships were also obtained. Based on the above findings, the correlative main concept, theoretical structure, evolution, and research progress of career adaptability in the past ten years are discussed.

scholarly journals Big Data, artificial intelligence and the geography of entrepreneurship in the United States

2018 ◽  
Author(s):  
Martin Obschonka ◽  
Neil Lee ◽  
Andrés Rodríguez-Pose ◽  
johannes Christopher Eichstaedt ◽  
Tobias Ebert
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Big Data ◽  
Big Five ◽  
The United States ◽  
Economic Outcomes ◽  
Entrepreneurial Activity ◽  
Personality Profile ◽  
Artificial Intelligence Methods ◽  
Wide Range

There is increasing interest in the potential of artificial intelligence and Big Data (e.g., generated via social media) to help understand economic outcomes and processes. But can artificial intelligence models, solely based on publicly available Big Data (e.g., language patterns left on social media), reliably identify geographical differences in entrepreneurial personality/culture that are associated with entrepreneurial activity? Using a machine learning model processing 1.5 billion tweets by 5.25 million users, we estimate the Big Five personality traits and an entrepreneurial personality profile for 1,772 U.S. counties. We find that these Twitter-based personality estimates show substantial relationships to county-level entrepreneurship activity, accounting for 20% (entrepreneurial personality profile) and 32% (all Big Five trait as separate predictors in one model) of the variance in local entrepreneurship and are robust to the introduction in the model of conventional economic factors that affect entrepreneurship. We conclude that artificial intelligence methods, analysing publically available social media data, are indeed able to detect entrepreneurial patterns, by measuring territorial differences in entrepreneurial personality/culture that are valid markers of actual entrepreneurial behaviour. More importantly, such social media datasets and artificial intelligence methods are able to deliver similar (or even better) results than studies based on millions of personality tests (self-report studies). Our findings have a wide range of implications for research and practice concerned with entrepreneurial regions and eco-systems, and regional economic outcomes interacting with local culture.

scholarly journals Big Data Big Impact: How Firms are Using Social Media for Innovation and Better Performance

2019 ◽  
Vol 2 (1) ◽  
pp. 32-42
Author(s):  
Ali El Samra ◽  
Leonidas Anastasakis ◽  
Pavel Albores ◽  
Victoria Uren
Keyword(s):  
Social Media ◽  
Big Data ◽  
The United States ◽  
Superior Performance ◽  
Sources Of Information ◽  
Online Questionnaire ◽  
Mediator Role ◽  
Social Media Platforms ◽  
And Performance ◽  
Use Of Social Media

Firms continuously attempt to find new sources of information to innovate and achieve a superior performance. Big data present on social media platforms represents one of the new sources of information that firms are starting to rely on. This paper is an exploratory study to examine how firms are making use of social media and what kind of impact the social media use have. An online questionnaire was used to collect data from 75 firms in the United States. Our findings suggest that Big Data, in the form of social media data, has an impact on the firm’s innovativeness and performance, and that IT capability potentially plays a mediator role in this relation.

scholarly journals How internet users in Moscow, Saint-Petersburg and Sevastopol Reacted to the Covid-19 Pandemic (Based on Material from a Media-Analysis Study)

2020 ◽  
Vol 11 (3) ◽  
pp. 121-142
Author(s):  
Olga V. Yarmak ◽  
Ekaterina V. Strashko ◽  
Tatyana V. Shkayderova
Keyword(s):  
Social Media ◽  
Big Data ◽  
Media Analysis ◽  
The Internet ◽  
Human Beings ◽  
Analysis Study ◽  
Saint Petersburg ◽  
Post Conflict ◽  
Internet Users ◽  
At Home

This article presents the results of the authors’ media-analysis study of social media in central federal cities – Moscow, Saint Petersburg and Sevastopol – on search queries such as “coronavirus”, “covid 19”, “sitting at home” and “stay at home” which came up during the first three weeks of self-isolation – from March 23rd to April 12th 2020. This allowed for analyzing trends in social media threads that emerged due to the lockdown and the epidemiological crisis, and for understanding the specifics of how a certain response to common threats and challenges was formulated in regional online-communities. The cybermetric analysis of social media conducted by the authors, using a big data mining system for monitoring and analyzing social networks called “Medialogiya”, allowed for tracking the develpment of media and communication trends associated with an ambiguous evaluation on behalf of internet users of the situation with the coronavirus pandemic and the lockdown, as well as the emergence of new digital forms of interaction used by individuals in their day to day affairs. The study was carried out within the framework of a project called “Developing methods of agent modeling and big data for analyzing social media in post-conflict societies”. The research group defines the information attained from “Medialogiya’s” system as “big sociological data”, which allows for analyzing interactions between human beings and information, as well as their behavior in the internet. The research results prove the development of regional specifics when discussing the pandemic and the issues associated with the ensuing lockdown experienced by internet users from Moscow and Sevastopol, which speaks to the emergence of a sort of regional solidarity in the face of this new threat and the challenges it poses. Sevastopol’s segment of the internet displayed not only regional, but also “peninsula” solidarity. New conditions of everyday life brought us to view the new viral infection as a socio-political phenomenon, which in turn creates the grounds for new forms of consolidation within society, caused by various reactions to the crisis. One of the tasks currently faced by social sciences would be developing scenarios and outlines to explain the phenomenon in question.

scholarly journals Capturing and Characterizing Human Activities Using Building Locations in America

2019 ◽  
Vol 8 (5) ◽  
pp. 200 ◽  
Author(s):  
Ren ◽  
Jiang ◽  
Seipel
Keyword(s):  
Social Media ◽  
Big Data ◽  
Power Law ◽  
Human Activities ◽  
Statistical Distribution ◽  
Human Beings ◽  
Spatial Clusters ◽  
Social Media Data ◽  
Positioning Systems ◽  
Media Data

Capturing and characterizing collective human activities in a geographic space have become much easier than ever before in the big era. In the past few decades it has been difficult to acquire the spatiotemporal information of human beings. Thanks to the boom in the use of mobile devices integrated with positioning systems and location-based social media data, we can easily acquire the spatial and temporal information of social media users. Previous studies have successfully used street nodes and geo-tagged social media such as Twitter to predict users’ activities. However, whether human activities can be well represented by social media data remains uncertain. On the other hand, buildings or architectures are permanent and reliable representations of human activities collectively through historical footprints. This study aims to use the big data of US building footprints to investigate the reliability of social media users for human activity prediction. We created spatial clusters from 125 million buildings and 1.48 million Twitter points in the US. We further examined and compared the spatial and statistical distribution of clusters at both country and city levels. The result of this study shows that both building and Twitter data spatial clusters show the scaling pattern measured by the scale of spatial clusters, respectively, characterized by the number points inside clusters and the area of clusters. More specifically, at the country level, the statistical distribution of the building spatial clusters fits power law distribution. Inside the four largest cities, the hotspots are power-law-distributed with the power law exponent around 2.0, meaning that they also follow the Zipf’s law. The correlations between the number of buildings and the number of tweets are very plausible, with the r square ranging from 0.53 to 0.74. The high correlation and the similarity of two datasets in terms of spatial and statistical distribution suggest that, although social media users are only a proportion of the entire population, the spatial clusters from geographical big data is a good and accurate representation of overall human activities. This study also indicates that using an improved method for spatial clustering is more suitable for big data analysis than the conventional clustering methods based on Euclidean geometry.

scholarly journals A Scientometrics Review on Land Ecosystem Service Research

2020 ◽  
Vol 12 (7) ◽  
pp. 2959 ◽  
Author(s):  
Hualin Xie ◽  
Yanwei Zhang ◽  
Yongrok Choi ◽  
Fengqin Li
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Rapid Development ◽  
Well Being ◽  
The United States ◽  
Cultural Development ◽  
Service Research ◽  
Human Beings ◽  
Services Research ◽  
The Impact

Humans can derive the benefits from the ecosystem to satisfy human needs as well-being. Therefore, good ecosystem management is the intermediary between ecosystems and human well-being. The ecosystem services depend on the supply of nature, and also reflect the value orientation of human beings, as the basis for the realization of human survival and cultural development. Land ecosystem services are the core and hot topic of ecological research. Under the current severe depletion of land use, this research evaluates the sustainable governance on the natural resource shortage, serious environmental pollution and ecosystem degradation. Based on the Web of Science database, this paper analyzes the development characteristics and trends of global land ecosystem services research using the Bibliometrix software package. The results show that (1) the amount of literature on land ecosystem services research between 2000 and 2019 has generally increased significantly, and entered a stage of rapid development from 2015. (2) Developed countries are the main research force in the field of land ecosystem services, and the United States has the absolute leading position. Developing countries are dominated by China, Argentina, and Brazil. (3) The high-frequency keywords for land ecosystem services are land use change, land use, climate change, urbanization, carbon and water quality. This can be regarded as a research hotspot in the field of land ecosystem services to a certain extent. (4) Through cluster analysis on the big data, the research found the direction for the future land ecosystem services, mainly: (I) the restoration of degraded land and its impact on ecosystem services; (II) the environmental impact assessment of land use planning based on the ecosystem services value; (III) the tradeoff analysis of ecosystem services in sustainable land management; (IV) the impact of land cover change on ecosystem services; (V) through the historical analysis of citied papers, the research history and evolution path of land ecosystem services are explored. Based on all these arguments, a comprehensive study on the diverse facets of land ecosystem services and the practical application of land ecosystem services areas is proposed.

Detecting Expressional Anomie in Social Media via Fine-grained Content Mining

2020 ◽  
Vol 31 (1) ◽  
pp. 1-19
Author(s):  
Qingqing Zhou ◽  
Ming Jing
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Development ◽  
Interpersonal Communication ◽  
Rapid Development ◽  
The Internet ◽  
Social Stability ◽  
Empirical Results ◽  
Fine Grained ◽  
Content Mining

Expression plays an important role in language inheritance, interpersonal communication, and social stability. With the rapid development of the Internet, people are becoming frequently interested in expressing themselves on social media. Meanwhile, massive anomic expressions are generated, which pollute network environments and even hinder social development. Hence, the purpose of this article is detecting anomic expressions in social media automatically, so as to reveal fine-grained status of online expressional anomie. Specifically, the authors used machine learning to detect anomic expressions and identify anomic types. Then, impacts of different factors (e.g. gender, region, time) on expressional anomie were analyzed. Finally, distributions and characteristics of expressional anomie about online contents were obtained. Empirical results indicate that the current situation of expressional anomie is severe, and scientific and effective treatments for anomic expression are necessary and urgently. Meanwhile, gender, region, and time should be taken into consideration in the formulation of treatments.

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