Restricted accessResearch articleFirst published online 2023-10
Mapping the Political Landscape on Social Media Using Bibliometrics: A Longitudinal Co-Word Analysis on Twitter and Facebook Publications Published Between 2012 and 2021
Topics such as disinformation, misinformation, political polarization, and populism are frequently discussed in the social media literature. The purpose of this article is to investigate how the political emphasis on social media has evolved in the academic publications published in the last decade. Thus, using co-word analysis of the social science articles published between 2012 and 2021, which discuss politically about Facebook or Twitter (N = 3389), this article investigates whether certain major and unexpected political events—such as Donald Trump’s presidential victory and the Brexit referendum—have influenced in any way the knowledge field related to social media publications. Thus, the 2017–2021 map brings new and popular words, such as “Covid-19,” which is associated in similar clusters with words such as disinformation, fake news, and infodemic. Furthermore, the emergence of the word “Russia” places it in a common cluster with words such as bots, elections, and agenda-setting. Also, the Twitter map, unlike the Facebook one, brings a particular emphasis on Donald Trump’s activity, which appears in clusters that are similar to topics that brought him popularity on Twitter, such as: meme, migration, and refugees. Such bibliometric associations should increase policymakers’ attention to the potential use of social media as a political tool, along with designing the solutions to limit such intrusions into future political events.
AkkermanA.MuddeC.ZasloveA. (2014). How populist are the people? Measuring populist attitudes in voters. Comparative Political Studies, 47(9), 1324–1353. https://doi.org/10.1177/0010414013512600
2.
AlexandreI.Jai-sung YooJ.MurthyD. (2021). Make tweets great again: Who are opinion leaders, and what did they tweet about Donald Trump?Social Science Computer Review. https://doi.org/10.1177/08944393211008859
3.
AlvaresC.DahlgrenP. (2016). Populism, extremism and media: Mapping an uncertain terrain. European Journal of Communication, 31(1), 46–57. https://doi.org/10.1177/0267323115614485
4.
AlvesH.FernandesC.RaposoM. (2016). Social media marketing: A literature review and implications. Psychology & Marketing, 33(12), 1029–1038. https://doi.org/10.1002/mar.20936
5.
Arroyo-MachadoW.Torres-SalinasD.Robinson-GarciaN. (2021). Identifying and characterizing social media communities: A socio-semantic network approach to altmetrics. Scientometrics, 126(11), 9267–9289. https://doi.org/10.1007/s11192-021-04167-8
6.
BuccolieroL.BellioE.CrestiniG.ArkoudasA. (2018). Twitter and politics: Evidence from the US presidential elections 2016. Journal of Marketing Communications, 26(1), 88–114. https://doi.org/10.1080/13527266.2018.1504228
7.
CaianiM.GrazianoP. R. (2016). Varieties of populism: Insights from the Italian case. Italian Political Science Review/Rivista Italiana di Scienza Politica, 46(2), 243–267. https://doi.org/10.1017/ipo.2016.6
EllegaardO. (2018). The application of bibliometric analysis: Disciplinary and user aspects. Scientometrics, 116(1), 181–202. https://doi.org/10.1007/s11192-018-2765-z
10.
EngesserS.ErnstN.EsserF.BüchelF. (2017). Populism and social media: How politicians spread a fragmented ideology. Information, Communication & Society, 20(8), 1109–1126. https://doi.org/10.1080/1369118x.2016.1207697
GebhardtM. (2019). The populist moment: Affective orders, protest, and politics of belonging. Distinktion: Journal of Social Theory, 22(2), 129–151. https://doi.org/10.1080/1600910x.2019.1653346
GoldieD.LinickM.JabbarH.LubienskiC. (2014). Using bibliometric and social media analyses to explore the “echo chamber” hypothesis. Educational Policy, 28(2), 281–305. https://doi.org/10.1177/0895904813515330
15.
HochschildA. R. (2016). Strangers in their own land: Anger and mourning on the American right. The New Press.
16.
HuC. P.HuJ. M.DengS. L.LiuY. (2013). A co-word analysis of library and information science in China. Scientometrics, 97(2), 369–382. https://doi.org/10.1007/s11192-013-1076-7
17.
JudisJ. B. (2016). The populist explosion: How the great recession transformed American and European politics. Columbia Global Reports.
18.
KhassehA. A.SoheiliF.MoghaddamH. S.ChelakA. M. (2017). Intellectual structure of knowledge in iMetrics: A co-word analysis. Information Processing & Management, 53(3), 705–720. https://doi.org/10.1016/j.ipm.2017.02.001
19.
LaclauE. (2005). On populist reason. Verso.
20.
LeungX. Y.SunJ.BaiB. (2017). Bibliometrics of social media research: A co-citation and co-word analysis. International Journal of Hospitality Management, 66, 35–45. https://doi.org/10.1016/j.ijhm.2017.06.012
MazzoleniG.BraccialeR. (2018). Socially mediated populism: The communicative strategies of political leaders on Facebook. Palgrave Communications, 4(1), 1–10. https://doi.org/10.1057/s41599-018-0104-x
23.
MooreM. (2018). Democracy hacked: How technology is destabilizing global politics. Oneworld Publications.
MuddeC.Rovira KaltwasserC. (2017). Populism: A very short introduction. Oxford University Press.
27.
MullerJ. W. (2016). What is populism?University of Pennsylvania Press.
28.
NagleA. (2017). Kill all normies: Online culture wars from 4Chan and Tumblr to Trump and the alt-right. Zero Books.
29.
ObrejaD. M. (2022). Increasingly dense and connected field: A longitudinal co-word analysis of youth sociological articles from 1990 to 2019. Youth, 2(2), 150–164. https://doi.org/10.3390/youth2020012
30.
OttB. L. (2017). The age of Twitter: Donald J. Trump and the politics of debasement. Critical Studies in Media Communication, 34(1), 59–68. https://doi.org/10.1080/15295036.2016.1266686
PritchardA. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348–349.
34.
RavikumarS.AgrahariA.SinghS. N. (2015). Mapping the intellectual structure of scientometrics: A co-word analysis of the journal Scientometrics (2005–2010). Scientometrics, 102(1), 929–955. https://doi.org/10.1007/s11192-014-1402-8
35.
Reiter-HaasM.KlöschB.HadlerM.LexE. (2022). Polarization of opinions on COVID-19 measures: Integrating Twitter and survey data. Social Science Computer Review. https://doi.org/10.1177/08944393221087662
36.
Ronda-PupoG. A.Guerras-MartinL. Á. (2012). Dynamics of the evolution of the strategy concept 1962–2008: A co-word analysis. Strategic Management Journal, 33(2), 162–188. https://doi.org/10.1002/smj.948
37.
SalimiD.TavasoliK.GilaniE.JouyandehM.SadjadiS. (2019). The impact of social media on marketing using bibliometrics analysis. International Journal of Data and Network Science, 3(3), 165–184. https://doi.org/10.5267/j.ijdns.2019.2.006
SnyderT. (2018). The road to unfreedom. Russia, Europe, America. Tim Duggan Books.
40.
StieglitzS.Dang-XuanL. (2013). Emotions and information diffusion in social media – sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217–247. https://doi.org/10.2753/mis0742-1222290408
41.
SuH. N.LeeP. C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology foresight. Scientometrics, 85(1), 65–79. https://doi.org/10.1007/s11192-010-0259-8
42.
ThelwallM.BuckleyK.PaltoglouG. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406–418. https://doi.org/10.1002/asi.21462
43.
TranfieldD.DenyerD.SmartP. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375
44.
WaltmanL.Van EckN. J.NoyonsE. C. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635. https://doi.org/10.1016/j.joi.2010.07.002
45.
ZengB.GerritsenR. (2014). What do we know about social media in tourism? A review. Tourism Management Perspectives, 10, 27–36. https://doi.org/10.1016/j.tmp.2014.01.001
46.
ZubiagaA.SpinaD.MartinezR.FresnoV. (2015). Real-time classification of Twitter trends. Journal of the Association for Information Science and Technology, 66(3), 462–473. https://doi.org/10.1002/asi.23186
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
