Objective/Significance: To measure the international communication capacity of China’s English academic journal papers based on social media platforms.
Method/Process: Social network analysis, content analysis and emotion analysis were used to describe the diffusion structure of China’s English academic journal papers on Twitter from the aspects of network structure and identify important node users, explore the identity characteristics and emotion characteristics of users as well as diffusion capability of journals of different disciplines.
Conclusion/Discovery: Journal articles in the fields of natural sciences and engineering as well as humanities and social sciences exhibit a strong positive correlation between their diffusion structure and network size. Networks with a mixed diffusion structure typically have larger network sizes. The users occupying key positions in the diffusion network typically have profile information that is academically relevant. The retweets and comments are mainly statements without emotional inclination, and the comments are mainly summary descriptions of the papers. The distribution of diffusion width, diffusion strength and diffusion speed of each discipline is significantly different. There is a significant positive correlation between the diffusion structure of papers and their diffusion capabilities.
Innovation/Value: To reveal the diffusion structure and characteristics of China’s English academic journal papers on social media platforms, so as to provide reference for expanding the visibility of China’s English academic journals and improving the international dissemination capacity of China’s English journals.
WaithakaMWOnyanchaOB. Use of open access channels for scholarly publishing in Kenyan universities. Publish Res Q2021; 37(2): 293–306.
2.
LiuX. An analysis of the development of Chinese STM journals in the past 30 years. Quant Sci Stud2021; 2(1): 292–299.
3.
China Association for Science Technology. Blue Book on the Development of Chinese Scientific and Technological Journals. Beijing, China: Science Press, 2019.
4.
DingYXMiaoWS. The current situation, development, and challenges of Chinese English academic journals going global. Publishing Research2020; 6: 65–69.
5.
China Association for Science Technology. China Association for Science and Technology (CAST) and six other departments jointly implement the China science and technology journal excellence action plan. 2019, http://sage.cnpereading.com/paragraph/download/?doi=10.1177/0165551520917651 (accessed 1 July 2023).
HuangGXGongHZDuJY. Analysis on the relationship between the selected journals by Excellence Action Plan of China STM Journals and the construction of first-class disciplines. Acta Editologica2021; 33(5): 19–24.
8.
ChenXV. Importance of independent journal publishing mode for domestic English-Language academic journals. Acta Editologica2023; 35(1): 29–35.
9.
SunXCPangXHZhangJM, et al. Status quo, problems, and strategies of high-level English scientific journals in universities. Chin J Sci Tech Period2022; 33(12): 1711–1718.
10.
WangYJTianJLiuWX, et al. Survey and analysis on new English scientific journals funded by Excellence Action Plan for China STM Journals. Chin J Sci Tech Period2021; 31(5): 614–621.
11.
SongQDuanP. Exploration of the path to enhance the international influence and dissemination of English academic journals. Sci Technol Public2022(4): 71–78.
12.
LiCN. China english humanities and social sciences journals: retrospect and prospect in the new decade. Sci Technol Public2023; 11: 117–126.
13.
LiuYWenTXShenF. Evaluation of the internationalization of English journals of humanities and social sciences in China. Chin J Sci Tech Period2022; 33(10): 1444–1452.
14.
LiuYSunYM. Internationalization development contradictions and countermeasures of China’s English academic journals of social sciences. Chin J Sci Tech Period2020; 31(6): 644–650.
15.
WangGYLiuY. Development trend and countermeasures for China’s international academic journals in humanities and social sciences. Chin J Sci Tech Period2023; 34(6): 722–730.
16.
YuHXuSXiaoT. Is there Lingua Franca in informal scientific communication? Evidence from language distribution of scientific tweets. J Informetr2018; 12(3): 605–617.
17.
ImranMAkhtarASaidA, et al. Exploiting social networks of Twitter in altmetrics big data. In: STI 2018 Conference Proceedings, Leiden, 12–14 September 2018, pp. 1339–1344. Leiden: Centre for Science and Technology Studies (CWTS).
18.
SaidABowmanTDAbbasiRA, et al. Mining network-level properties of Twitter altmetrics data. Scientometrics2019; 120: 217–235.
19.
DidegahFMejlgaardNSørensenMP. Investigating the quality of interactions and public engagement around scientific papers on Twitter. J Informetr2018; 12(3): 960–971.
20.
SafderIHassanSU. Bibliometric-enhanced information retrieval: a novel deep feature engineering approach for algorithm searching from full-text publications. Scientometrics2019; 119: 257–277.
21.
MiaoWSHuangY. Politics matters: the power dynamics behind Chinese English-language humanities and social science journals. Learn Publ2021; 34(3): 331–338.
22.
RenSLYangJNingB, et al. Review of the development of English scientific and technological journals in China in 2022. Sci Technol Public2023(3): 50–57.
23.
TianXXueLMDongYJ, et al. The editorial characteristics and insights of Chinese engineering and technology English scientific journals – A case study of journals selected in the ‘China Science and Technology Journal Excellence Action Plan’. Commun Copyright2023; 1: 36–39.
24.
XuJWangJYZhouL, et al. Internationalization of China’s English-language academic journals: an overview and three approaches. Learn Publ2019; 32(2): 113–125.
25.
HeMM. Elevating the dissemination impact of academic journals from the perspective of integrated media. Commun Copyright2023; 9: 70–72.
26.
LiuYNYangHTangY. Publishing service cooperation between English scientific journals in China and international publishers. Chin J Sci Tech Period2019; 30(6): 642–648.
27.
LiuYZhouXYGuoHF, et al. Exploration of the internationalization path of Chinese scientific journals: a case study of the English edition of acta pharmaceutica sinic. Commun Copyright2019; 4: 3.
28.
LiUYWangB. Discussion of improving the quality of domestic English language geochemistry journal. Acta Editologica2017; 29(S2): 19–21.
29.
HuangY. Current situation, problems, and optimization strategies of Chinese English scientific journal publishing and communication platforms: a case study of five platforms supported by Excellence Action for China’s STM Journals. Chin J Sci Tech Period2023; 34(11): 1473–1478.
30.
China Association for Science Technology. Blue Book on the Development of Chinese Science and Technology Journals. Beijing, China: Science Press, 2021.
LiMYangR. English-medium journals as a bridge: a study of Frontiers of Education in China. Asia Pac Educ Rev2023; 1-14.
33.
LinSQZhanLJ. International publishing partnerships for China’s English language journals and financial security. Learn Publ2016; 29: 145–153.
34.
AbrizahAXuJNicholasD. Scholarly communication and matters of trust and authority: a comparative analysis of Malaysian and Chinese researchers. Malays J Libr Inf Sc2017; 22(3): 69–91.
35.
XianJ. The past, present and future of scientific and technical journals of China. Learn Publ2006; 19: 133–141.
36.
XuJWhalsM. The scholarly publishing industry in China: overview and opportunities. Learn Publ2012; 25(1): 63–74.
37.
LiH. The internationalization of Chinese English-language medicine journals in China. Sci Technol Public2021(3): 134–139.
38.
XuJHeCSuJ, et al. Chinese researchers’ perceptions and use of open access journals: results of an online questionnaire survey. Learn Publ2020; 33(3): 246–258.
39.
KimEYJ. Scholarly publishing in Korea: language, perception, practice of Korean university faculty. Publish Res Q2018; 34(4): 554–567.
40.
Lo pez-Navarroã IMorenoAIQuintanillaMA, et al. Why do I publish research articles in English instead of my own language? Differences in Spanish researchers’ motivations across scientific domains. Scientometrics2015; 103(3): 939–976.
41.
FanAMeJXiaoH, et al. Research on the evaluation of the influence of China’s English academic journals and development countermeasures. Sci Technol Public2017(6): 86–91.
42.
PengLHanY. Analysis and enlightenment of the data policy of Chinese scientific journals: taking the English journals sponsored by Chinese Academy of Sciences for example. Chin J Sci Tech Period2019; 30(8): 870–877.
43.
ZhaoRYZhuWJWangXY. From academic influence to academic discourse: theoretical framework for four-dimensional evaluation of academic journals. Chin J Sci Tech Period2023; 34(5): 576–583.
44.
XueCLWangYJYueRQ, et al. Media integration in scientific journals and strategies for communication: taking journals included in the excellence action plan for China STM Journals as examples. Chin J Sci Tech Period2023; 34(4): 461–472.
45.
GumpenbergerCGlanzelWGorraizJ, et al. The ecstasy and the agony of the altmetric score. Scientometrics2016; 108(2): 977–982.
46.
YuZLMaZJiaoYD, et al. Exploring marketing strategies for the establishment of new English scientific journals in China. Sci Technol Public2023; 2: 10.
47.
HeTWLiuW. The internationalization of Chinese scientific journals: a quantitative comparison of three chemical journals from China, England and Japan. Scientometics2009; 80(3): 583–593.
48.
MaZYuZLPanYT. The statistic analysis of Chinese journals in English. Chin J Sci Tech Period2014; 25(10): 1277–1281.
49.
LiuXLGuoJShenL. Dilemmas and thoughts on the internationalization of non-English scientific journals: empirical research based on SCI journals in 10 non-English speaking countries. Chin J Sci Tech Period2020; 31(4): 455–461.
50.
XuLZouQZhouY, et al. Advancing open science in the game: a survey of overseas scientific journal publishing and data publication in 2022. Sci Technol Public2023; 3: 46–49.
51.
WangZ. Navigating the dilemma and breakthroughs of English academic journals in traditional Chinese medicine. Sci Technol Public2023; 6: 33–40.
52.
YuYTJiangZHCaoGH. An empirical study on the funding effect of english sci-tech journals in china based on difference-in-difference model. Inform Sci2023; 41(6): 144–152.
53.
ZhangJZhengX. Construction of the international academic discourse power of Chinese English sci-tech journals. Sci Technol Public2017(6): 111–115.
54.
MengY. Discussion on media integration development of foreign academic journals. New Media Res2018(23): 98–99.
55.
WangXZhaoR. Characteristics and relationships of discourse influence indicators: a case study of English academic journals in China. Chin J Sci Tech Period2021; 32(4): 545–554.
56.
WangXZhaoR. Research on the relationship between discourse leading indicators and citation counts based on Altmetrics. Inform Stud: Theory & Appl2021; 44(7): 37–43.
57.
JamaliHRNicholasDHermanE, et al. National comparisons of early career researchers’ scholarly communication attitudes and behaviours. Learn Publ2020; 33(4): 370–384.
58.
LiMYangR. Enduring hardships in global knowledge asymmetries: a national scenario of China’s English-language academic journals in the humanities and social sciences. High Educ2020; 80(2): 237–254.
59.
YuZLMaZTianRQ, et al. Social impact analysis of China ’s STM journals based on mentions index of Altmetrics. Acta Editologica2021; 33(3): 349–354.
60.
ZhangYBaoFWuJ, et al. Reflections on the international impact of Chinese STM journals. Learn Publ2019; 32(2): 126–136.
61.
BikhchandaniSHirshleiferDWelchI. A theory of fads, fashion, custom, and cultural change as informational cascades. J Polit Econ1992; 100(5): 992–1026.
62.
AndersonLRHoltCA. Information cascades in the laboratory. Am Econ Rev1997; 87(5): 847–862.
63.
EasleyDKleinbergJ. Networks, crowds, and markets: reasoning about a highly connected world. Cambridge: Cambridge University Press, 2010.
64.
DongJChenBLiuL. The influence of information attractiveness and impact on the size distribution of cascades. J Syst Simul2018; 30(10): 3624–3631.
65.
LiDXuZMLiS. Information diffusion in online social networks. Chin J Comput2014; 37(1): 189–206.
66.
DengWHYiM. Research on the tree-like propagation of word-of-mouth information in online community based on information diffusion cascade theory. Chin J Manag2017(2): 254–260.
67.
GoelSAndersonAHofmanJ, et al. The structural virality of online diffusion. Manage Sci2016; 62(1): 180–196.
68.
KatzELazarsfeldPFRoperE. Personal influence: the part played by people in the flow of mass communications. London: Routledge, 2017.
69.
AkroufSMeriemLYahiaB, et al. Social network analysis and information propagation: a case study using Flickr and YouTube networks. Int J Future Comput Commun2013; 2(3): 246–252.
70.
Robinson-GarcíaNTorres-SalinasDZahediZ, et al. New data, new possibilities: exploring the insides of Altmetric.com. ArXiv preprint, 2014, https://arxiv.org/abs/1408.0135.
71.
AdieERoeW. Altmetric: enriching scholarly content with article-level discussion and metrics. Learn Publ2013; 26: 11–17.
72.
ThelwallMHausteinSLarivièreV, et al. Do altmetrics work? Twitter and ten other social web services. PLoS ONE2013; 8: e64841.
73.
HassanSUImranMGillaniU, et al. Measuring social media activity of scientific literature: an exhaustive comparison of scopus and novel altmetrics big data. Scientometrics2017; 113: 1037–1057.
74.
FangZCostasRTianW, et al. An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics. Scientometrics2020; 124(3): 2519–2549.
75.
FangZCostasRTianW, et al. How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications. J Assoc Inf Sci Technol2021; 72(7): 918–932.
76.
FangZDudekJCostasR. Facing the volatility of tweets in altmetric research. J Assoc Inf Sci Technol2022; 73(8): 1192–1195.
77.
BornmannLHaunschildR. How to normalize Twitter counts? A first attempt based on journals in the Twitter Index. Scientometrics2016; 107(3): 1405–1422.
78.
EysenbachG. Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact. J Med Internet Res2011; 13(4): e2012.
79.
ZhangLSivertsenGDuH, et al. Gender differences in the aims and impacts of research. Scientometrics2021; 126: 8861–8886.
80.
AndersonRMMayRM. Infectious diseases of humans: dynamics and control. Oxford: Oxford University Press, 1991.
ZhangYWangLZhuJJH, et al. Viral vs. Broadcast: characterizing the virality and growth of cascades. Europhys Lett2020; 131(2): 28002.
83.
LeeMKYoonHYSmithM, et al. Mapping a Twitter scholarly communication network: a case of the association of internet researchers’ conference. Scientometrics2017; 112(2): 767–797.
84.
HouJWangYZhangY, et al. How do scholars and non-scholars participate in dataset dissemination on Twitter. J Informetr2022; 16(1): 101223.
85.
YangCZ. Emotion diffusion, information cascades, and internet opinion deviation: a dynamic analysis based on emergency events panel data from 2015 to 2020. J China Soc Sci Tech Inform2021; 40(5): 448–461.
86.
LeskovecJSinghAKleinbergJ. Patterns of influence in a recommendation network. In: Pacific-Asia conference on knowledge discovery and data mining, Singapore, 9–12 April 2006, pp. 380–389. Berlin: Springer.
87.
MutzDC. The consequences of cross-cutting networks for political participation. Am J Polit Sci2002; 46(4): 838–855.
88.
LiangHFuKW. Network redundancy and information diffusion: the impacts of information redundancy, similarity, and tie strength. Commun Res2016; 1: 1–23.
89.
BarberáPJostJTNaglerJ, et al. Tweeting from left to right: is online political communication more than an echo chamber?Psychol Sci2015; 26(10): 1531–1542.
90.
MutzDCMartinPS. Facilitating communication across lines of political difference: the role of mass media. Am J Polit Sci2001; 95(1): 97–114.
91.
WeiJLZhuQH. An overseas review of the spread of opinions on social networks and information distortion based on information cascade. J China Soc Sci Tech Inform2019; 38(10): 1117–1128.
92.
KitsakMGallosLKHavlinS, et al. Identification of influential spreaders in complex networks. Nat Phys2010; 6(11): 888–893.
93.
Latorre-MartinezPOrive-SerranoVIniguezD. Measurement and analysis of the presence in Facebook and Twitter in the regional television broadcaster’s context in Spain. Profes De La Inform2018; 27(5): 1061–1070.
94.
XieQLuoT. Examining user participation and network structure via an analysis of a Twitter-supported conference backchannel. J Educ Comput Res2019; 57(5): 1160–1185.
95.
Carrasco-PolainoRVillar-CirujanoEMartín-CárdabaMÁ. Networks, tweets and engagement: analysis of Spanish university libraries on Twitter. Profes De La Inform2019; 28(4): 1699–2407.
96.
Del-Fresno-GarciaM. Making the invisible visible: visualizing the network structure of Twitter relationships using Social Network Analysis. Profes De La Inform2014; 23(3): 246–252.
97.
AkarE. Let’s Get United and# ClearTheShelters: the Factors Contributing to Users’ Network Centrality in Online Social Networks. J Inf Technol Res2022; 15(1): 1–18.
98.
VenkatesanSValechaRYaraghiN, et al. Influence in Social Media: an investigation of tweets spanning the 2011 Egyptian Revolution. Mis Quart2021; 45(4): 1679–1714.
99.
DaiZHiggsC. Social Network and Semantic Analysis of Roe v. Wade’s reversal on TwitterSoc Sci Comput Rev2024; 42(1): 186–200.
100.
RehmMNottenA. Twitter as an informal learning space for teachers!? The role of social capital in Twitter conversations among teachers. Teach Teach Educ2016; 60: 215–223.
101.
Díaz-FaesAABowmanTDCostasR. Towards a second generation of ‘social media metrics’: characterizing Twitter communities of attention around science. PLoS ONE2019; 14(5): e0216408.
102.
MoshkovitzKHayatT. The rich get richer: extroverts’ social capital on twitter. Technol Soc2021; 65: 101551.
103.
KoiranenIKoivulaAKeipiT, et al. Shared contexts, shared background, shared values–Homophily in Finnish parliament members’ social networks on Twitter. Telemat Inform2019; 36: 117–131.
104.
Robinson-GarciaNCostasRIsettK, et al. The unbearable emptiness of tweeting—About journal articles. PLoS ONE2017; 12(8): e0183551.
105.
ThelwallMTsouAWeingartS, et al. Tweeting links to academic articles. Int J Scientometric Inform Bibliometric2013; 17(1): 1–8.
106.
RiméB. Emotion elicits the social sharing of emotion: theory and empirical review. Emot Rev2009; 1(1): 60–85.
107.
FriedrichNBowmanTDStockWG, et al. Adapting sentiment analysis for tweets linking to scientific papers. arXiv preprint, 2015, https://arxiv.org/abs/1507.01967.
108.
LemayDJBasnetRBDoleckT, et al. Social network analysis of twitter use during the AERA 2017 annual conference. Educ Inf Technol2019; 24: 459–470.
109.
WangPPengTYShiY, et al. Reflections on the path to building brands for China’s English academic journals: internationalization practices illustrated with a case study of Water Science and Engineering. Public Commun Sci Tech2020; 258(9): 69–72.
110.
SongQFDuanPL. Exploring paths for enhancing the international influence and dissemination power of English academic journals. Sci Tech Publ2022; 4: 71–78.
111.
ChenXTKouGLiYY. Exploration and practice of enhancing the international influence of start-up English academic social science journals: taking financial innovation as an example. Chin J Sci Tech Period2021; 32(11): 1438–1444.
112.
BaiXJLiuJJWengYQ. A unified system of publishing and promotion for English academic journals: practical experience from Microsystems & Nanoengineering. Acta Editologica2020; 32(1): 92–96.
113.
PriemJGrothPTaraborelliD. The altmetrics collection. PLoS ONE2012; 7(11): ee48753.
114.
DidegahFMejlgaardNSørensenMP. Investigating the quality of interactions public engagement around scientific papers on Twitter. J Informetr2018; 12(3): 960–971.
115.
LiuJRenZGuoQ. Ranking the spreading influence in complex networks. Physica A2013; 392(18): 4154–4159.
116.
NamtirthaADuttaADuttaB. Identifying influential spreaders in complex networks based on kshell hybrid method. Physica A2018; 499: 310–324.
117.
MaLMaCZhangH, et al. Identifying influential spreaders in complex networks based on gravity formula. Physica A2016; 451: 205–212.
118.
HassanSUBowmanTDShabbirM, et al. Influential tweeters in relation to highly cited articles in altmetric big data. Scientometrics2019; 119: 481–493.
ZhangYChenFLukitoJ. Network amplification of politicized information and misinformation about COVID-19 by conservative media and partisan influencers on Twitter. Polit Commun2023; 40(1): 24–47.
122.
Carrasco-FarréC. The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions. Hum Soc Sci Commun2022; 9(1): 1–18.
123.
HofmanJMWattsDJAtheyS, et al. Integrating explanation and prediction in computational social science. Nature2021; 595(7866): 181–188.
124.
FangZCostasRWoutersP. User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis. Scientometrics2022; 127(8): 4523–4546.
125.
ZhangLGouZFangZ, et al. Who tweets scientific publications? A large-scale study of tweeting audiences in all areas of research. J Assoc Inf Sci Tech2023; 74: 1485–1497.
126.
LiaoQYuanJDongM, et al. Public engagement and government responsiveness in the communications about COVID-19 during the early epidemic stage in China: infodemiology study on social media data. J Med Internet Res2020; 22(5): e18796.
127.
HausteinSPetersISugimotoCR, et al. Tweeting biomedicine: an analysis of tweets and citations in the biomedical literature. J Assoc Inf Sci Tec2014; 65(4): 656–669.
128.
DumbrellDSteeleR. Twitter and its roles in health information dissemination: analysis of the micro-blog posts of health-related organisations. In: MadjidTGhapanchiAHTalaei-khoeiA (eds.) Healthcare informatics and analytics: emerging issues and trends: emerging issues and trends. Hershey, PA: IGI Global, 2017, pp. 372–388.
129.
BoydDGolderSLotanG. Tweet, tweet, retweet: conversational aspects of retweeting on Twitter. In: Proceedings of the 43Rd hawaii international conference on system sciences (HICSS), Kauai, HI, 5–8 January 2010.
130.
KimEHouJHanJY, et al. Predicting retweeting behavior on breast cancer social networks: network and content characteristics. J Health Commun2016; 21(4): 479e486.
131.
ZavattaroSMFrenchPEMohantySD. A sentiment analysis of US local government tweets: the connection between tone and citizen involvement. Gov Inform Q2015; 32(3): 333–341.
132.
Al-SamarraieHSarsamSMAlzahraniAI. Haptic technology in society: a sentiment analysis of public engagement. Comput Hum Behav2023; 147: 107862.
133.
KhanMLIttefaqMPantojaYIM, et al. Public engagement model to analyze digital diplomacy on Twitter: a social media analytics framework. Int J Commun-us2021; 15: 29.
134.
ZhangZBorsG. ‘Less is more’ Mining useful features from Twitter user profiles for Twitter user classification in the public health domain. Online Inform Rev2020; 44(1): 213–237.
135.
DurazziFMüllerMSalathéM, et al. Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic. Sci Rep-uk2021; 11(1): 19655.
136.
XuJChenHZhangM. Strategy and practice for promoting the international impact of scientific journals: a case study on Friction. Chin J Sci Tech Period2018; 29(8): 853–859.
137.
NicholasDRodríguez-BravoBWatkinsonA, et al. Earlycareer researchers and their publishing and authorship practices. Learn Pub2017; a30(3): 205–217.
138.
NicholasDWatkinsonABoukacem-ZeghmouriC, et al. Early career researchers: scholarly behaviour and the prospect of change. Learn Pub2017; b30(2): 157–166.
139.
BaiXJWengYQLiuC. Advices on strengthening public communication of Chinese scientific journals based on the analysis of public communication of Nature, Science, and PANS. Chin J Sci Tech Period2022; 33(7): 917–925.
