This article studies the effect of online social media on knowledge diffusion. In this article, an SIS (Susceptible–Infectious–Susceptible) model was established to chase the knowledge diffusion and identify the proper model for knowledge diffusion of individual literature from the perspective of Altmetrics. Based on the data collected from the PLoS ONE database, the transition probabilities of each paper were calculated, and the papers were divided into six groups according to the transition probabilities using a K-means algorithm. This article explores the impacts of transition probabilities and summarises the similarities and differences of the patterns of knowledge diffusion of each group from the perspective of Altmetrics. Research results showed that the SIS model could be used to describe the knowledge diffusion of individual literature from the perspective of Altmetrics. Besides, the classification method proposed in this article could also be applied in future informetric research. In addition, this article also contributes to the practitioners of knowledge diffusion and online platforms.
HethcoteHW. The mathematics of infectious diseases. SIAM Rev2000; 42: 599–653.
2.
CostasRde RijckeSMarresN. ‘Heterogeneous couplings’: operationalizing network perspectives to study science-society interactions through social media metrics. J Assoc Inf Sci Technol2021; 72: 595–610.
3.
LehaneDJBlackCS. Can Altmetrics predict future citation counts in critical care medicine publications?J Intensive Care Soc2021; 22: 60–66.
4.
NadiniMRizzoAPorfiriM. Epidemic spreading in temporal and adaptive networks with static backbone. IEEE Trans Netw Sci Eng2020; 7: 549–561.
5.
PriemJGrothPTaraborelliD. The Altmetrics collection. PLoS ONE2012; 7: e48753.
6.
HavakhorTSororAASabherwalR. Diffusion of knowledge in social media networks: effects of reputation mechanisms and distribution of knowledge roles. Inf Syst J2018; 28: 104–141.
7.
AlperinJPGomezCJHausteinS. Identifying diffusion patterns of research articles on Twitter: a case study of online engagement with open access articles. Public Underst Sci2019; 28: 2–18.
8.
BoliciFAcciariniCMarchegianiL, et al. Innovation diffusion in tourism: how information about blockchain is exchanged and characterized on twitter. TQM J. Epub ahead of print 1 January 2020. DOI: 10.1108/TQM-01-2020-0016.
9.
MoukarzelSRehmMFresnoM, et al. Diffusing science through social networks: the case of breastfeeding communication on Twitter. PLoS ONE2020; 15: e0237471.
10.
Van NoordenR. Online collaboration: scientists and the social network. Nat News2014; 512: 126.
11.
LazerDPentlandAAdamicL, et al. Computational social science. Science2009; 323: 721–723.
12.
LiQLiuY. Exploring the diversity of retweeting behavior patterns in Chinese microblogging platform. Inf Process Manag2017; 53: 945–962.
13.
RowlandsINicholasDRussellB, et al. Social media use in the research workflow. Learn Publ2011; 24: 183–195.
14.
BuckarmaEHThielsCAGasBL, et al. Influence of social media on the dissemination of a traditional surgical research article. J Surg Educ2017; 74: 79–83.
15.
HouJMaD. How the high-impact papers formed? A study using data from social media and citation. Scientometrics2020; 125: 2597–2615.
16.
TimilsinaMd’AquinMYangH. Heat diffusion approach for scientific impact analysis in social media. Soc Netw Anal Min2019; 9: 16.
17.
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: e2012.
18.
TimilsinaMDavisBTaylorM, et al. Predicting citations from mainstream news, weblogs and discussion forums. In: Proceedings of the international conference on web intelligence, 23–26 August 2017, Leipzig, pp. 237–244. New York: Association for Computing Machinery.
19.
CostasRZahediZWoutersP. Do ‘altmetrics’ correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective. J Assoc Inf Sci Technol2015; 66: 2003–2019.
20.
HausteinSPetersISugimotoCR, et al. Tweeting biomedicine: an analysis of tweets and citations in the biomedical literature. J Assoc Inf Sci Technol2014; 65: 656–669.
21.
BornmannLHaunschildRAdamsJ. Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK research excellence framework (REF). J Informetr2019; 13: 325–340.
22.
EzemaIJUgwuCI. Correlating research impact of library and information science journals using citation counts and altmetrics attention. Inf Discov Deliv2019; 47: 143–153.
23.
MullinsCHBoydCJCoreyBL. Examining the correlation between altmetric score and citations in the general surgery literature. J Surg Res2020; 248: 159–164.
24.
NuzzoleseAGCiancariniPGangemiA, et al. Do altmetrics work for assessing research quality?Scientometrics2019; 118: 539–562.
25.
WarrenVTPatelBBoydCJ. Analyzing the relationship between Altmetric score and literature citations in the Implantology literature. Clin Implant Dent Relat Res2020; 22: 54–58.
26.
GroverPKarAKJanssenM. Diffusion of blockchain technology: insights from academic literature and social media analytics. J Enterp Inf Manag2019; 32: 735–757.
27.
Ladeiras-LopesRClarkeSVidal-PerezR, et al. Twitter promotion predicts citation rates of cardiovascular articles: a preliminary analysis from the ESC journals randomized study. Eur Heart J2020; 41: 3222–3225.
28.
SugimotoCRWorkSLarivièreV, et al. Scholarly use of social media and altmetrics: a review of the literature. J Assoc Inf Sci Technol2017; 68: 2037–2062.
29.
MohammadiEThelwallM. Mendeley readership altmetrics for the social sciences and humanities: research evaluation and knowledge flows. J Assoc Inf Sci Technol2014; 65: 1627–1638.
30.
KrakerPKörnerCJackK, et al. Harnessing user library statistics for research evaluation and knowledge domain visualization. In: Proceedings of the 21st international conference on world wide web, 16–20 April 2012, Lyon, pp. 1017–1024. New York: Association for Computing Machinery.
31.
NusairK. Developing a comprehensive life cycle framework for social media research in hospitality and tourism: a bibliometric method 2002-2018. Int J Contemp Hosp Manag2020; 32: 1041–1066.
32.
CooperA. The Use of online strategies and social media for research dissemination in education. Educ Policy Anal Arch2014; 22: 88.
33.
BettencourtLMACintrón-AriasAKaiserDI, et al. The power of a good idea: quantitative modeling of the spread of ideas from epidemiological models. Phys Stat Mech Its Appl2006; 364: 513–536.
34.
JiafuSXuefengZJiaquanY, et al. Modelling and simulating knowledge diffusion in knowledge collaboration organisations using improved cellular automata. J Simul2019; 13: 181–194.
35.
KissIZBroomMCrazePG, et al. Can epidemic models describe the diffusion of topics across disciplines?J Informetr2010; 4: 74–82.
36.
LiuYRousseauR. Towards a representation of diffusion and interaction of scientific ideas: the case of fiber optics communication. Inf Process Manag2012; 48: 791–801.
37.
WangHWangJSmallM. Knowledge transmission model with differing initial transmission and retransmission process. Phys Stat Mech Its Appl2018; 507: 478–488.
38.
BettencourtLKaiserDKaurJ, et al. Population modeling of the emergence and development of scientific fields. Scientometrics2008; 75: 495–518.
39.
YueZXuHYuanG, et al. Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: a case study in graphene field. Phys Stat Mech Its Appl2019; 524: 375–391.
40.
DuanMChangLJinZ. Turing patterns of an SI epidemic model with cross-diffusion on complex networks. Phys Stat Mech Its Appl2019; 533: 122023.
41.
LiH-LZhangLTengZ, et al. Global stability of an SI epidemic model with feedback controls in a patchy environment. Appl Math Comput2018; 321: 372–384.
42.
MoreJSLingamC. A SI model for social media influencer maximization. Appl Comput Inform2019; 15: 102–108.
43.
AlexandruRDragottiPL. Rumour source detection in social networks using partial observations. In: 2018 IEEE global conference on signal and information processing (GlobalSIP), Anaheim, CA, 26–29 November 2018, pp. 730–734. New York: IEEE.
44.
AllenLJS. Some discrete-time SI, SIR, and SIS epidemic models. Math Biosci1994; 124: 83–105.
45.
LuoWTayWP. Finding an infection source under the SIS model. In: 2013 IEEE international conference on acoustics, speech and signal processing, Vancouver, BC, Canada, 26–31 May 2013, pp. 2930–2934. New York: IEEE.
46.
KesavareddigariHSpencerSEryilmazA, et al. Identification and asymptotic localization of rumor sources using the method of types. IEEE Trans Netw Sci Eng2020; 7: 1145–1157.
47.
LiuCZhouNZhanX-X, et al. Markov-based solution for information diffusion on adaptive social networks. Appl Math Comput2020; 380: 125286.
48.
ZhuLHuangX. SIS model of rumor spreading in social network with time delay and nonlinear functions. Commun Theor Phys2019; 72: 015002.
49.
MaLKangYJHanH, et al. Research on virus diffusion prevention method for computer singularity in complex sensor networks. Int J Internet Manuf Serv2020; 7: 64–82.
50.
KunpengYJiafuSHuiH. Simulation of collaborative product development knowledge diffusion using a new cellular automata approach. Adv Prod Eng Manag2017; 12: 265–273.
51.
XiaoYZhangLLiQ, et al. MM-SIS: model for multiple information spreading in multiplex network. Phys Stat Mech Its Appl2019; 513: 135–146.
52.
ZhuHMaJ. Knowledge diffusion in complex networks by considering time-varying information channels. Phys Stat Mech Its Appl2018; 494: 225–235.
53.
JainADharJGuptaV. Stochastic model of rumor propagation dynamics on homogeneous social network with expert interaction and fluctuations in contact transmissions. Phys Stat Mech Its Appl2019; 519: 227–236.
54.
ZhaoYJiangD. The threshold of a stochastic SIS epidemic model with vaccination. Appl Math Comput2014; 243: 718–727.
55.
ZhouYYuanSZhaoD. Threshold behavior of a stochastic SIS model with Lévy jumps. Appl Math Comput2016; 275: 255–267.
56.
GoyalMManjunathD. Opinion control competition in a social network. In: 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), Bengaluru, India, 7–11 January 2020, pp. 306–313. New York: IEEE.
57.
KandhwayKKuriJ. How to run a campaign: optimal control of SIS and SIR information epidemics. Appl Math Comput2014; 231: 79–92.
58.
PazokiMSamarghandiH. Word-Of-Mouth and estimating demand based on network structure and epidemic models. Eur J Oper Res2021; 291: 323–334.
59.
WooJChenH. Epidemic model for information diffusion in web forums: experiments in marketing exchange and political dialog. SpringerPlus2016; 5: 66.
