Towards a Bibliometric Map** of Network Public Opinion Studies
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source
2.2. Method
2.2.1. Theoretical Basis
2.2.2. Research Methodology
3. Results and Discussion
3.1. Temporal Distribution Map of the Literature
3.1.1. Temporal Distribution of World Literature
3.1.2. Temporal Distribution of Active National Literature
3.2. Spatial Distribution Map of the Literature
3.2.1. Country/Region Distribution
3.2.2. Disciplinary Distribution of Literature
3.2.3. Institute Distribution of Literature
3.2.4. Journal Distribution
3.3. High-Cited Literature ANALYSIS
3.4. Co-Authorship Analysis
3.5. Research Knowledge Base
3.5.1. The Reference Co-Citation Analysis
3.5.2. The Journal Co-Citation Analysis
3.6. Research Hotspots and Frontier Analysis
3.6.1. Research Hotspot Analysis
3.6.2. Research Frontier Identification
4. Conclusions and Future Work
- (1)
- The development history of the field of NPO is roughly divided into three phases: the initial phase (1990–2006), the rapid development phase (2007–2013) and the stable growth phase (2014–2020). In terms of the distribution of articles by country, the United States and China topped the list, indicating that these countries are the development centers and active regions of NPO. In terms of research institutions, the Chinese Academy of Sciences, Bei**g University of Posts and Telecommunications and Huazhong University of Science and Technology have the most scientific achievements. In terms of disciplinary distribution, NPO is based on “computer science,” “engineering,” “information systems” and “theoretical methods. The distribution of internet opinion is based on “computer science”, “engineering”, “information system” and “theory and methodology”. “Communication”, “Engineering, Electrical and Electronics”, “Government and Law” and “Political Science” are the external environment that ensures the healthy development of NPO. The external environment for the healthy development of public opinion. “Artificial intelligence”, “telecommunications”, and “interdisciplinary applications” are important tools and methods for improving online opinion analysis. IEEE Access, International Journal of Communication and Physica A-statistical Mechanics and Its Applications are the main carriers of literature in this research area.
- (2)
- The knowledge base in the field of NPO research includes social media, user influence, and user influence related to opinion dynamic modeling and sentiment analysis. The vectors of co-cited literature can be roughly divided into four categories: social and computer sciences, statistics, social media, and information. In addition, the core journals in this field are IEEE Access, International Journal of Communication, Physica A-statistical Mechanics and Its Applications, and International Journal of Public Opinion Research. It was found that the authors of the five articles with the highest co-citation frequency (Fraser, 2007; Shaw and Gant, 2002; Iyengar and Simon, 1993; Esrock and Leichty, 1998; Huckfeldt, 1995) are experts who have made outstanding contributions to the field of network opinion research.
- (3)
- There are four hot spots in the study of NPO: analysis of public opinion, analysis of NPO dissemination channels, technical means of NPO, and challenges of NPO. By using CiteSpace’s keyword time zone diagram, we found that there were no hot keywords generated before 1996, and the hot keywords “public opinion” and “internet” appeared in 2002. The development of NPO entered a stable development period in 2005, and the research hotspots include “media”, “perception”, “management”, “policy”, and “management”. “After 2008, the hot keywords include “opinion”, “sentiment analysis”, “sentiment analysis”, “dynamics” and “public sphere”, which are all important elements of NPO research. These are all important elements of NPO, such as “media”, “perception”, “management”, “policy” and “online”. “sentiment analysis”, “dynamics” and “public sphere”, which are all important elements of NPO research. These are all important elements of NPO research.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Nan, F.; Suo, Y.N.; Jia, X.Y.; Wu, Y.Y.; Shan, S.J. Real-Time Monitoring of Smart Campus and Construction of Weibo Public Opinion Platform. IEEE Access 2018, 6, 76502–76515. [Google Scholar] [CrossRef]
- Albert, R.; Barabasi, A.L. Statistical mechanics of complex networks. Rev. Mod. Phys. 2002, 74, 47–97. [Google Scholar] [CrossRef] [Green Version]
- Chen, T.G.; Peng, L.J.; Yang, J.J.; Cong, G.D. Modeling, simulation, and case analysis of COVID-19 over network public opinion formation with individual internal factors and external information characteristics. Concurr. Comput.-Pract. Exp. 2021, 33, e6201. [Google Scholar] [CrossRef]
- Clark, R.; Maynard, M. Research methodology-Using online technology for secondary analysis of survey research data-“Act globally, think locally”. Soc. Sci. Comput. Rev. 1998, 16, 58–71. [Google Scholar] [CrossRef]
- Huang, C.Y.; Sun, C.T.; Hsieh, J.L.; Lin, H. Simulating SARS: Small-world epidemiological modeling and public health policy assessments. JASSS 2004, 7, 32. [Google Scholar]
- Greaves, F. What are the most appropriate methods of surveillance for monitoring an emerging respiratory infection such as SARS? J. Public Health 2004, 26, 288–292. [Google Scholar] [CrossRef]
- Ng, T.W.; Turinici, G.; Danchin, A. A double epidemic model for the SARS propagation. BMC Infect. Dis. 2003, 3, 16. [Google Scholar] [CrossRef] [PubMed]
- Alfonseca, M.; Martinez-Bravo, M.T.; Torrea, J.L. Mathematical models for the analysis of hepatitis B and AIDS epidemics. Simulation 2000, 74, 219–226. [Google Scholar] [CrossRef] [Green Version]
- Huang, C.Y.; Tsai, Y.S.; Wen, T.H. A Network-based Simulation Architecture for Studying Epidemic Dynamics. Simul. Trans. Soc. Model. Simul. Int. 2010, 86, 351–368. [Google Scholar] [CrossRef]
- Wang, H.Q.; Zhu, H.L.; Zheng, C.H.; Yip, T.T.C.; Cho, W.C.S.; Law, S.C.K. Mining protein regulatory relationships using neural network methods for early prediction of sars. J. Circuits Syst. Comput. 2009, 18, 1397–1407. [Google Scholar] [CrossRef]
- Entman, R.M. Framing public life: Perspectives on media and our understanding of the social world. Polit. Commun. 2006, 23, 121–122. [Google Scholar] [CrossRef]
- Zhou, Y.Q.; Moy, P. Parsing framing processes: The interplay between online public opinion and media coverage. J. Commun. 2007, 57, 79–98. [Google Scholar] [CrossRef]
- Liu, D.L.; Zhang, H.; Yu, H.; Zhao, X.H.; Wang, W.T.; Liu, X.; Ma, L. Research on Network Public Opinion Analysis and Monitor Method Based on Big Data Technology. In Proceedings of the 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication, Bei**g, China, 17–19 July 2020; Wenzheng, L., Xuefei, Z., Eds.; IEEE: New York, NY, USA, 2020; pp. 195–199. [Google Scholar]
- Silva, A.; Prado, J.W.; Alcantara, V.C.; Tonelli, D.F.; Pereira, J.R. Public opinion: Bibliometric analysis for the systematization of trends. Holos 2018, 34, 2–30. [Google Scholar] [CrossRef]
- Li, Y.H.; Tu, Y.; Li, X.F. Study on Enterprises’ Internet Public Opinion Area Hotspots Based on Social Network Analysis; Association for Information Systems: Atlanta, GA, USA, 2018; pp. 350–357. [Google Scholar]
- Wang, Z.K.; Deng, Z.H.; Wu, X. Status Quo of Professional-Patient Relations in the Internet Era: Bibliometric and Co-Word Analyses. Int. J. Environ. Res. Public Health 2019, 16, 1183. [Google Scholar] [CrossRef] [Green Version]
- Liu, H.; Hong, R.; ** knowledge domains for spontaneous combustion studies. Fuel 2020, 262, 13. [Google Scholar] [CrossRef]
- Gou, X.Q.; Liu, H.; Qiang, Y.J.; Lang, Z.H.; Wang, H.N.; Ye, D.; Wang, Z.W.; Wang, H. In-depth analysis on safety and security research based on system dynamics: A bibliometric map** approach-based study. Saf. Sci. 2022, 147, 105617. [Google Scholar] [CrossRef]
- van Eck, N.J.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric map**. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [Green Version]
- Chen, C.M.; Assoc Comp, M. Visualizing and Exploring Scientific Literature with CiteSpace; Assoc Computing Machinery: New York, NY, USA, 2018; pp. 369–370. [Google Scholar] [CrossRef]
- van Eck, N.J.; Waltman, L. VOSviewer: A Computer Program for Bibliometric Map**. In Proceedings of the ISSI 2009-12th International Conference of the International Society for Scientometrics and Informetrics, Rio de Janeiro, Brazil, 14–17 July 2009; Volume 2, pp. 886–897. [Google Scholar]
- Chen, C.M. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 2006, 57, 359–377. [Google Scholar] [CrossRef] [Green Version]
- Chen, C.M.; Song, M. Visualizing a field of research: A methodology of systematic scientometric reviews. PLoS ONE 2019, 14, e0223994. [Google Scholar] [CrossRef] [Green Version]
- Lang, Z.H.; Liu, H.; Meng, N.; Wang, H.N.; Wang, H.; Kong, F.Y. Map** the knowledge domains of research on fire safety—An informetrics analysis. Tunn. Undergr. Space Technol. 2021, 108, 103676. [Google Scholar] [CrossRef]
- yengar, S.; Simon, A. News coverage of the gulf crisis and public-opinion-a study of agenda-setting, priming, and framing. Commun. Res. 1993, 20, 365–383. [Google Scholar] [CrossRef]
- Huckfeldt, R.; Beck, P.A.; Dalton, R.J.; Levine, J. Political environments, cohesive social-groups, and the communication of public-opinion. Am. J. Political Sci. 1995, 39, 1025–1054. [Google Scholar] [CrossRef]
- Katz, J.; Aspden, P. Motivations for and barriers to Internet usage: Results of a national public opinion survey. Internet Res. Electron. Netw. Appl. Policy 1997, 7, 170–188. [Google Scholar] [CrossRef]
- Cheng, Y. Collaborative planning in the network: Consensus seeking in urban planning issues on the Internet-the case of China. Plan. Theory 2013, 12, 351–368. [Google Scholar] [CrossRef]
- ** the knowledge domains of new energy vehicle safety: Informetrics analysis-based studies. J. Energy Storage 2021, 35, 102275. [Google Scholar] [CrossRef]
- Torres-Prunonosa, J.; Plaza-Navas, M.A.; Diez-Martin, F.; Prado-Roman, C. The Sources of Knowledge of the Economic and Social Value in Sport Industry Research: A Co-citation Analysis. Front. Psychol. 2020, 11, 17. [Google Scholar] [CrossRef] [PubMed]
- Hong, R.; Liu, H.; ** knowledge domain of oxidation studies of sulfide ores. Environ. Sci. Pollut. Res. 2020, 27, 5809–5824. [Google Scholar] [CrossRef]
- Yu, J.X.; Zhou, J. Chinese Civil Society Research in Recent Years: A Critical Review. China Rev. 2012, 12, 111–139. [Google Scholar]
- Gong, K.; Tang, M.; Shang, M.S.; Zhou, T. Empirical study on spatiotemporal evolution of online public opinion. Acta Phys. Sin. 2012, 61, 526–532. [Google Scholar] [CrossRef]
- Chen, X.G.; Duan, S.; Wang, L.D. Research on trend prediction and evaluation of network public opinion. Concurr. Comput.-Pract. Exp. 2017, 29, e4212. [Google Scholar] [CrossRef]
- Sun, G.X.; Bin, S.; Jiang, M.; Cao, N.; Zheng, Z.Y.; Zhao, H.Y.; Wang, D.B.; Xu, L.N. Research on Public Opinion Propagation Model in Social Network Based on Blockchain. Comput. Mater. Contin. 2019, 60, 1015–1027. [Google Scholar] [CrossRef] [Green Version]
- Yang, Y.X. Research and realization of internet public opinion analysis based on improved TF-IDF algorithm. In Proceedings of the 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, Anyang, China, 13–16 October 2017. [Google Scholar]
- Liu, L.; Jiang, Z.T.; Destech Publicat, I. Research on Topic Detection of Network Public Opinion Based on Hierarchical Clustering. In Proceedings of the International Conference on Simulation, Modelling and Mathematical Statistics, Chiang Mai, Thailand, 22–23 November 2015; pp. 291–295. [Google Scholar]
- Fang, S.W.; Zhao, N.; Chen, N.; **ong, F.; Yi, Y.H. Analyzing and predicting network public opinion evolution based on group persuasion force of populism. Phys. A Stat. Mech. Appl. 2019, 525, 809–824. [Google Scholar] [CrossRef]
- Zhang, W.; He, M.S. Influence of Opinion Leaders on Dynamics and Diffusion of Network Public Opinion. In Proceedings of the 2013 International Conference on Management Science and Engineering 20th Annual Conference Proceedings, Harbin, China, 17–19 July 2013; pp. 139–144. [Google Scholar]
- Li, Y.Z.; Zhou, H.L.; Lin, Z.L.; Wang, Y.F.; Chen, S.J.; Liu, C.; Wang, Z.Y.; Gifu, D.; **a, J.B. Investigation in the influences of public opinion indicators on vegetable prices by corpora construction and WeChat article analysis. Future Gener. Comput. Syst. Int. J. Esci. 2020, 102, 876–888. [Google Scholar] [CrossRef]
- Xu, G.X.; Wu, X.; Yao, H.S.; Li, F.; Yu, Z.H. Research on Topic Recognition of Network Sensitive information Based on SW-LDA Model. IEEE Access 2019, 7, 21527–21538. [Google Scholar] [CrossRef]
- Zhang, W.S.; Lu, J.Z. An Online Water Army Detection Method Based on Network Hot Events. In Proceedings of the 2018 10th International Conference on Measuring Technology and Mechatronics Automation, Changsha, China, 10–11 February 2018; pp. 191–193. [Google Scholar] [CrossRef]
- Xu, G.X.; Meng, Y.T.; Qiu, X.Y.; Yu, Z.H.; Wu, X. Sentiment Analysis of Comment Texts Based on BiLSTM. IEEE Access 2019, 7, 51522–51532. [Google Scholar] [CrossRef]
- Hu, X.; Zhang, Y.; An, B.W. The Mechanism and Influencing Factors of Herding Effect of College Students’ Network Public Opinion. Anthropologist 2016, 23, 226–230. [Google Scholar]
- Househ, M. Communicating Ebola through social media and electronic news media outlets: A cross-sectional study. Health Inform. J. 2016, 22, 470–478. [Google Scholar] [CrossRef]
- Wang, Y.Y.; Huang, X.L.; Li, B.Q.; Liu, X.Q.; Ma, Y.Y.; Huang, X.J. Spreading mechanism of Weibo public opinion phonetic representation based on the epidemic model. Int. J. Speech Technol. 2021, 1–11. [Google Scholar] [CrossRef]
Rank | Type of Document | TP | SOTC | CA | Proportion/% | h-Index |
---|---|---|---|---|---|---|
1 | Article | 868 | 13,049 | 11,724 | 62.67 | 54 |
2 | Proceedings Paper | 495 | 1334 | 1294 | 35.74 | 14 |
3 | Review | 28 | 617 | 617 | 2.02 | 13 |
4 | Early Access | 14 | 8 | 8 | 1.88 | 2 |
5 | Editorial Material | 8 | 108 | 108 | 0.58 | 4 |
6 | Meeting Abstract | 6 | 0 | 0 | 0.43 | 0 |
Rank | Country | Region | Quantity | Percentage/% | ACI | h-Index | Total Link Strength |
---|---|---|---|---|---|---|---|
1 | China | East Asia North America | 604 | 43.61 | 3.14 | 20 | 71 |
2 | USA | North America | 355 | 25.63 | 21.02 | 44 | 116 |
3 | England | Western Europe | 66 | 4.77 | 14.61 | 16 | 67 |
4 | Canada | North America | 43 | 3.11 | 14.47 | 12 | 35 |
5 | Germany | Central Europe | 34 | 2.46 | 25.35 | 14 | 13 |
6 | Spain | Southern Europe | 33 | 2.38 | 13.91 | 10 | 12 |
7 | Australia | Oceania | 32 | 2.31 | 15.97 | 11 | 37 |
8 | Italy | Southern Europe | 31 | 2.24 | 18.87 | 10 | 25 |
9 | South Korea | East Asia | 29 | 2.10 | 14.34 | 10 | 17 |
10 | India | South Asia | 23 | 1.66 | 1 | 3 | 5 |
Rank | Quantity | Centrality | WOS Categories | Percent/% |
---|---|---|---|---|
1 | 455 | 0.41 | Computer Science | 32.85 |
2 | 271 | 0.24 | Engineering | 19.57 |
3 | 222 | 0.51 | Computer Science, Information Systems | 16.03 |
4 | 210 | 0.32 | Computer Science, Theory and Methods | 15.16 |
5 | 188 | 0.16 | Communication | 13.57 |
6 | 186 | 0.31 | Engineering, Electrical and Electronic | 13.43 |
7 | 135 | 0 | Government and Law | 9.75 |
8 | 126 | 0.04 | Political Science | 9.10 |
9 | 126 | 0.24 | Computer Science, Artificial Intelligence | 9.10 |
10 | 78 | 0.17 | Telecommunications | 5.63 |
Rank | Institution | Country | Quantity | Total Link Strength | STC | ACI |
---|---|---|---|---|---|---|
1 | Chinese Acad Sci | China | 36 | 30 | 215 | 5.97 |
2 | Bei**g Univ Posts and Telecommun | China | 23 | 6 | 27 | 1.17 |
3 | Huazhong Univ Sci and Technol | China | 18 | 2 | 84 | 4.67 |
4 | Univ Chinese Acad Sci | China | 18 | 23 | 129 | 7.17 |
5 | Bei**g Jiaotong Univ | China | 16 | 6 | 71 | 4.44 |
6 | Natl Univ Def Technol | China | 15 | 5 | 15 | 1 |
7 | Ohio State Univ | Europe | 14 | 6 | 397 | 28.36 |
8 | Univ Michigan | USA | 14 | 8 | 653 | 46.64 |
9 | Univ Wisconsin | USA | 14 | 10 | 417 | 29.79 |
10 | Harvard Univ | USA | 13 | 6 | 346 | 26.62 |
Rank | STC | Title | Authors | Journal | Year | IN | CN |
---|---|---|---|---|---|---|---|
1 | 309 | Transnationalizing the public sphere—On the legitimacy and efficacy of public opinion in a post-Westphalian world | Fraser [39] | Theory Culture and Society | 2007 | 1 | 1 |
2 | 302 | In Defense of the internet: The relationship between Internet communication and depression, loneliness, self-esteem, and perceived social support | Shaw and Gant [40] | Journal of Communication | 2002 | 2 | 1 |
3 | 297 | News coverage of the gulf crisis and public-opinion-a study of agenda-setting, priming, and framing | Iyengar and Simon [27] | Communication Research | 1993 | 2 | 1 |
4 | 276 | Social responsibility and corporate web pages: Self-presentation or agenda-setting? | Esrock and Leichty [41] | Public Relations Review | 1998 | 2 | 1 |
5 | 216 | Political, environment, cohesive social-groups, and the communication of public-opinion | Huckfeldt [28] | American Journal of Political Science | 1995 | 4 | 1 |
6 | 192 | Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens political preferences with an application to Italy and France | Ceron [42] | New Media and Society | 2014 | 4 | 1 |
7 | 184 | Analyzing the representativeness of internet political participation | Best and Krueger [44] | Political Behavior | 2005 | 2 | 1 |
8 | 177 | Exploring the nature of the best:International relations theory and comparative policy analysis meet the European Union | RisseKappen [45] | Journal of Common Market Studies | 1996 | 3 | 1 |
9 | 144 | Social-psychological influences on opinion expression in face-to-face and computer-mediated communication | Shirley and Douglas [46] | Communication Research | 2008 | 2 | 1 |
10 | 141 | Assessing the democratic debate: How the news media frame elite policy discourse | Callaghan and Schnell [47] | Political Communication | 2001 | 1 | 1 |
Rank | Author | Organization | Country | Links | Quantities | ACI |
---|---|---|---|---|---|---|
1 | Liu, Yijun | University of Insubria | China | 55 | 12 | 10.08 |
2 | Xu, Lingyu | United Technologies Corporation | China | 40 | 7 | 1.71 |
3 | Zhang, Gaowei | Jiaxing University | China | 40 | 7 | 1.71 |
4 | Bolouki, Sadegh | European Commission Joint Research Centre | Netherlands | 23 | 6 | 1.83 |
5 | Chen, Tinggui | Yokohama National University | China | 47 | 6 | 9.33 |
6 | Lian, Ying | Yokohama National University | China | 42 | 6 | 3.67 |
7 | Wang, Lei | Japan Automobile Res Inst | China | 39 | 6 | 0.5 |
8 | Chen, Bin | Lawrence Livermore National Laboratory | China | 37 | 5 | 7.8 |
9 | Cong, Guodong | European Commission Joint Research Centre | China | 47 | 5 | 10.2 |
10 | Dong, Xuefan | European Commission Joint Research Centre | China | 37 | 5 | 3 |
Rank | Keywords | Occurrences | Total Link Strength | Rank | Keywords | Occurrences | Total Link Strength |
---|---|---|---|---|---|---|---|
1 | public opinion | 209 | 642 | 11 | social network | 55 | 92 |
2 | internet | 154 | 617 | 12 | news | 54 | 252 |
3 | social media | 110 | 497 | 13 | NPO | 52 | 28 |
4 | media | 93 | 339 | 14 | attitudes | 51 | 182 |
5 | information | 68 | 278 | 15 | social networks | 48 | 165 |
6 | communication | 67 | 307 | 16 | internet public opinion | 46 | 41 |
7 | sentiment analysis | 60 | 108 | 17 | online | 45 | 245 |
8 | 60 | 259 | 18 | behavior | 41 | 148 | |
9 | model | 56 | 177 | 19 | impact | 41 | 159 |
10 | dynamics | 55 | 160 | 20 | policy | 40 | 157 |
Keywords | Strength | Begin | End | 1990–2020 |
---|---|---|---|---|
public opinion | 6.324 | 1996 | 2005 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
internet | 7.0708 | 2002 | 2007 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
television | 3.7893 | 2006 | 2011 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
web | 3.8174 | 2006 | 2009 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
internet public opinion | 6.2529 | 2009 | 2013 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
selective exposure | 3.739 | 2014 | 2017 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
NPO | 4.7567 | 2015 | 2017 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
big data | 4.703 | 2017 | 2020 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
machine learning | 3.8496 | 2018 | 2020 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
deep learning | 4.4979 | 2018 | 2020 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
centrality | 3.5947 | 2018 | 2020 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
sentiment analysis | 3.5003 | 2018 | 2020 | ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Qiang, Y.; Tao, X.; Gou, X.; Lang, Z.; Liu, H. Towards a Bibliometric Map** of Network Public Opinion Studies. Information 2022, 13, 17. https://doi.org/10.3390/info13010017
Qiang Y, Tao X, Gou X, Lang Z, Liu H. Towards a Bibliometric Map** of Network Public Opinion Studies. Information. 2022; 13(1):17. https://doi.org/10.3390/info13010017
Chicago/Turabian StyleQiang, Yujie, Xuewen Tao, **aoqing Gou, Zhihui Lang, and Hui Liu. 2022. "Towards a Bibliometric Map** of Network Public Opinion Studies" Information 13, no. 1: 17. https://doi.org/10.3390/info13010017