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Blockchain technology is the most cutting-edge technology in the field of financial technology, which has attracted extensive attention from governments, financial institutions and investors of various countries. Blockchain and finance, as an interdisciplinary, cross-technology and cross-field topic, has certain limitations in both theory and application. Based on the bibliometrics data of Web of Science, this paper conducts data mining on 759 papers related to blockchain technology in the financial field by means of co-word analysis, bi-clustering algorithm and strategic coordinate analysis, so as to explore hot topics in this field and predict the future development trend. The experimental results found ten research topics in the field of blockchain combined with finance, including blockchain crowdfunding, Fintech, encryption currency, consensus mechanism, the Internet of Things, digital financial, medical insurance, supply chain finance, intelligent contract and financial innovation. Among them, blockchain crowdfunding, Fintech, encryption currency and supply chain finance are the key research directions in this research field. Finally, this paper also analyzes the opportunities and risks of blockchain development in the financial field and puts forward targeted suggestions for the government and financial institutions.
Yunmei Liu; Shuai Zhang; Min Chen; Yenchun Wu; Zhengxian Chen. The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining. Sustainability 2021, 13, 7585 .
AMA StyleYunmei Liu, Shuai Zhang, Min Chen, Yenchun Wu, Zhengxian Chen. The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining. Sustainability. 2021; 13 (14):7585.
Chicago/Turabian StyleYunmei Liu; Shuai Zhang; Min Chen; Yenchun Wu; Zhengxian Chen. 2021. "The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining." Sustainability 13, no. 14: 7585.
Background The COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people’s health and governance systems. Objective This study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic. Methods We collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time. Results The daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed (r=0.672, P<.01) and newly suspected (r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07%), video-sharing platforms (642/2745, 23.39%), news-sharing platforms (607/2745, 22.11%), health care platforms (239/2745, 8.71%), and Q&A platforms (157/2745, 5.72%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: “conspiracy theories” (648/2745, 23.61%), “government response” (544/2745, 19.82%), “prevention action” (411/2745, 14.97%), “new cases” (365/2745, 13.30%), “transmission routes” (244/2745, 8.89%), “origin and nomenclature” (228/2745, 8.30%), “vaccines and medicines” (154/2745, 5.61%), and “symptoms and detection” (151/2745, 5.50%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations. Conclusions Our study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic.
Shuai Zhang; Wenjing Pian; Feicheng Ma; Zhenni Ni; Yunmei Liu. Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study. JMIR Public Health and Surveillance 2021, 7, e26090 .
AMA StyleShuai Zhang, Wenjing Pian, Feicheng Ma, Zhenni Ni, Yunmei Liu. Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study. JMIR Public Health and Surveillance. 2021; 7 (2):e26090.
Chicago/Turabian StyleShuai Zhang; Wenjing Pian; Feicheng Ma; Zhenni Ni; Yunmei Liu. 2021. "Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study." JMIR Public Health and Surveillance 7, no. 2: e26090.
Abidan Ainiwaer; Shuai Zhang; Xiayiabasi Ainiwaer; Feicheng Ma. Effects of message framing on cancer prevention and detection behaviors, intentions and attitudes: Systematic Review and Meta-Analysis (Preprint). Journal of Medical Internet Research 2021, 1 .
AMA StyleAbidan Ainiwaer, Shuai Zhang, Xiayiabasi Ainiwaer, Feicheng Ma. Effects of message framing on cancer prevention and detection behaviors, intentions and attitudes: Systematic Review and Meta-Analysis (Preprint). Journal of Medical Internet Research. 2021; ():1.
Chicago/Turabian StyleAbidan Ainiwaer; Shuai Zhang; Xiayiabasi Ainiwaer; Feicheng Ma. 2021. "Effects of message framing on cancer prevention and detection behaviors, intentions and attitudes: Systematic Review and Meta-Analysis (Preprint)." Journal of Medical Internet Research , no. : 1.
BACKGROUND The COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people’s health and governance systems. OBJECTIVE This study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic. METHODS We collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time. RESULTS The daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed (r=0.672, P<.01) and newly suspected (r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07%), video-sharing platforms (642/2745, 23.39%), news-sharing platforms (607/2745, 22.11%), health care platforms (239/2745, 8.71%), and Q&A platforms (157/2745, 5.72%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: “conspiracy theories” (648/2745, 23.61%), “government response” (544/2745, 19.82%), “prevention action” (411/2745, 14.97%), “new cases” (365/2745, 13.30%), “transmission routes” (244/2745, 8.89%), “origin and nomenclature” (228/2745, 8.30%), “vaccines and medicines” (154/2745, 5.61%), and “symptoms and detection” (151/2745, 5.50%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations. CONCLUSIONS Our study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic.
Shuai Zhang; Wenjing Pian; Feicheng Ma; Zhenni Ni; Yunmei Liu. Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study (Preprint). 2020, 1 .
AMA StyleShuai Zhang, Wenjing Pian, Feicheng Ma, Zhenni Ni, Yunmei Liu. Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study (Preprint). . 2020; ():1.
Chicago/Turabian StyleShuai Zhang; Wenjing Pian; Feicheng Ma; Zhenni Ni; Yunmei Liu. 2020. "Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study (Preprint)." , no. : 1.