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Open AccessJournal ArticleDOI

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

TLDR
Web of Science core collection was taken as the data source, and traditional statistical methods and CiteSpace software were used to carry out the scientometrics analysis of SMBD, which showed the research status, hotspots and trends in this field.
Abstract
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.

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Journal ArticleDOI

Using Content Analysis to Probe the Cognitive Image of Intangible Cultural Heritage Tourism: An Exploration of Chinese Social Media

TL;DR: Through content analysis, this paper confirms the effectiveness of social media in providing a richer understanding of ICH tourism and offers insight for policymakers regarding ways to better balance the relationships among heritage protection, the business economy, and people’s well-being.
Journal ArticleDOI

Trends of global health literacy research (1995-2020): Analysis of mapping knowledge domains based on citation data mining.

TL;DR: Wang et al. as discussed by the authors used CiteSpace to carry out a metric analysis of 9,492 health literacy papers included in Web of Science through mapping knowledge domains, and the results of the BC measures of the global health literacy research collaboration network show that United States, Australia and the United Kingdom are the major forces in the current international collaboration network on health literacy.
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Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects

TL;DR: Zhang et al. as discussed by the authors investigated the variation between individual and interactive influences of built environment factors on occupation mixture index (OMI) with a novel GeoDetector-based indicator.
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Bibliometric evaluation of research on political risks in construction projects

TL;DR: In this article, the authors provide an overview of the research on political risk using the Web of Science (WoS) database as well as summarize research results and put forward some suggestions for research directions of political risk in international construction projects.
Journal ArticleDOI

Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review

Chenghao Yang, +1 more
- 14 Oct 2022 - 
TL;DR: The literature review shows that the deep learning framework has great research potential for text emotional analysis, image classification, object detection, image segmentation, and the expression classification of social media data.
References
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Proceedings Article

Distributed Representations of Words and Phrases and their Compositionality

TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
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Deep learning in neural networks

TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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An index to quantify an individual's scientific research output

TL;DR: The index h, defined as the number of papers with citation number ≥h, is proposed as a useful index to characterize the scientific output of a researcher.
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Twitter mood predicts the stock market.

TL;DR: This work investigates whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time and indicates that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others.
Journal ArticleDOI

Detecting influenza epidemics using search engine query data

TL;DR: A method of analysing large numbers of Google search queries to track influenza-like illness in a population and accurately estimate the current level of weekly influenza activity in each region of the United States with a reporting lag of about one day is presented.
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