Polarization of public opinion is a major issue for societies, as high levels can promote adverse effects such as hostility. The present paper focuses on the polarization of opinions regarding COVID-19 prevention measures in survey data and on Twitter in the German-speaking regions of Germany, Austria, and Switzerland. The level of polarization is measured by dispersion and bimodality in the opinions based on the sentiment in Twitter data and the agreement in the survey data. Our paper, however, goes beyond existing research as we consider data from both sources separately and comparatively. For this purpose, we matched individuals’ survey responses and tweets for those respondents who shared their Twitter account information. The analyses show that vaccination is more polarizing compared to mask wearing and contact tracing in both sources, that polarization of opinions is more pronounced in the survey data compared to the Twitter data, but also that individuals’ opinions about the COVID-19 measures are consistent in both sources. We believe our findings will provide valuable insights for integrating survey data and Twitter data to investigate opinion polarization.
AbrilE. P. (2018). Subduing attitude polarization?: How partisan news may not affect attitude polarization for online publics. Politics and the Life Sciences, 37(1), 68–77. https://doi.org/10.1017/pls.2017.1
2.
AdamicL. A.GlanceN. (2005). The political blogosphere and the 2004 US election: Divided they blog. In Proceedings of the 3rd international workshop on Link discovery, Chicago, IL (pp. 36–43). https://doi.org/10.1145/1134271.1134277
3.
Al BaghalT.SloanL.JessopC.WilliamsM. L.BurnapP. (2019). Linking Twitter and survey data: The impact of survey mode and demographics on consent rates across three uk studies. Social Science Computer Review, 38(5), 517–532. https://doi.org/10.1177/0894439319828011
AlamsyahA.AdityawarmanF. (2017, May 17–19). Hybrid sentiment and network analysis of social opinion polarization. In 2017 5th International Conference on Information and Communication Technology (ICoIC7), Melaka, Malaysia (pp. 1–6). https://doi.org/10.1109/ICoICT.2017.8074650
6.
AllcottH.BoxellL.ConwayJ.GentzkowM.ThalerM.YangD. (2020). Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic. Journal of Public Economics, 191(2020), 1–11. https://doi.org/10.1016/j.jpubeco.2020.104254
7.
AnJ.QuerciaD.CrowcroftJ. (2013). Fragmented social media: A look into selective exposure to political news. In Proceedings of the 22nd International Conference on World Wide Web (pp. 51–52). https://doi.org/10.1145/2487788.2487807
8.
BachR. L.KernC.AmayaA.KeuschF.KreuterF.HechtJ.HeinemannJ. (2019). Predicting voting behavior using digital trace data. Social Science Computer Review, 39, 862–883. https://doi.org/10.1177/0894439319882896
9.
BailC. A.ArgyleL. P.BrownT. W.BumpusJ. P.ChenH.HunzakerM. F.LeeJ.MannM.MerhoutF.VolfovskyA. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216–9221. https://doi.org/10.1073/pnas.1804840115
10.
BakshyE.MessingS.AdamicL. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. https://doi.org/10.1126/science.aaa1160
BessiA.CaldarelliG.Del VicarioM.ScalaA.QuattrociocchiW. (2014). Social determinants of content selection in the age of (mis) information. In International conference on social informatics (pp. 259–268). https://doi.org/10.1007/978-3-319-13734-618
13.
BessiA.PetroniF.Del VicarioM.ZolloF.AnagnostopoulosA.ScalaA.CaldarelliG.QuattrociocchiW. (2015, May 18–22). Viral misinformation: The role of homophily and polarization. In Proceedings of the 24th International Conference on World Wide Web, Florence (pp. 355–356). https://doi.org/10.1145/2740908.2745939
14.
Borge-HolthoeferJ.MagdyW.DarwishK.WeberI. (2015, March 14 - 18). Content and network dynamics behind Egyptian political polarization on Twitter. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, Vancouver BC (pp. 700–711). https://doi.org/10.1145/2675133.2675163
15.
BosancianuC. M. (2017). A growing rift in values? Income and educational inequality and their impact on mass attitude polarization. Social Science Quarterly, 98(5), 1587–1602. https://doi.org/10.1111/ssqu.12371
16.
BracciA.NadiniM.AliapouliosM.McCoyD.GrayI.TeytelboymA.GalloA.BaronchelliA. (2021). Dark web marketplaces and COVID-19: Before the vaccine. EPJ Data Science, 10(1), 6. https://doi.org/10.1140/epjds/s13688-021-00259-w
17.
BramsonA.GrimP.SingerD. J.BergerW. J.SackG.FisherS.FlockenC.HolmanB. (2017). Understanding polarization: Meanings, measures, and model evaluation. Philosophy of Science, 84(1), 115–159. https://doi.org/10.1086/688938
18.
Bruine de BruinW.SawH.-W.GoldmanD. P. (2020). Political polarization in US residents’ COVID-19 risk perceptions, policy preferences, and protective behaviors. Journal of Risk and Uncertainty, 61(2), 177–194. https://doi.org/10.1007/s11166-020-09336-3
ChenE.LermanK.FerraraE. (2020). Tracking social media discourse about the COVID-19 pandemic: Development of a public coronavirus Twitter data set. JMIR Public Health and Surveillance, 6(2), Article e19273. https://doi.org/10.2196/19273
21.
ConoverM. D.RatkiewiczJ.FranciscoM.GonçalvesB.MenczerF.FlamminiA. (2011). Political polarization on Twitter. Fifth International AAAI Conference on Weblogs and Social Media.
22.
CotaW.FerreiraS. C.Pastor-SatorrasR.StarniniM. (2019). Quantifying echo chamber effects in information spreading over political communication networks. EPJ Data Science, 8(1), 35. https://doi.org/10.1140/epjds/s13688-019-0213-9
23.
DarwishK. (2019). Quantifying polarization on Twitter: The Kavanaugh nomination. In International conference on social informatics (pp. 188–201). https://doi.org/10.1007/978-3-030-34971-4_13
24.
DeitelhoffN.Groh-SambergO.MiddellM.(Eds.), (2020). Gesellschaftlicher Zusammenhalt. Ein interdisziplinärer Dialog. Campus Verlag.
25.
Del VicarioM.BessiA.ZolloF.PetroniF.ScalaA.CaldarelliG.StanleyH. E.&QuattrociocchiW. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113(3), 554–559. https://doi.org/10.1073/pnas.1517441113
26.
DemszkyD.GargN.VoigtR.ZouJ.GentzkowM.ShapiroJ.JurafskyD. (2019). Analyzing polarization in social media: Method and application to tweets on 21 mass shootings. arXiv preprint arXiv:1904.01596.
27.
DiMaggioP.EvansJ.BrysonB. (1996). Have American’s social attitudes become more polarized?American journal of Sociology, 102(3), 690–755. https://doi.org/10.1086/230995
28.
DohleS.WingenT.SchreiberM. (2020). Acceptance and adoption of protective measures during the COVID-19 pandemic: The role of trust in politics and trust in science. Social Psychological Bulletin, 15(4), 1–23. https://doi.org/10.32872/spb.4315
29.
EllisonA. M. (1987). Effect of seed dimorphism on the density-dependent dynamics of experimental populations of atriplex triangularis (chenopodiaceae). American Journal of Botany, 74(8), 1280–1288. https://doi.org/10.1002/j.1537-2197.1987.tb08741.x
30.
Esteve Del ValleM.BroersmaM.PonsioenA. (2021). Political interaction beyond party lines: Communication ties and party polarization in parliamentary Twitter networks. Social Science Computer Review. Advance online publication. https://doi.org/10.1177/0894439320987569
GarciaD.MendezF.SerdültU.SchweitzerF. (2012). Political polarization and popularity in online participatory media: An integrated approach. Proceedings of the first edition workshop on Politics, elections and data, PLEAD’12(2012), 3–10. https://doi.org/10.1145/2389661.2389665
34.
GarimellaK. (2018). Polarization on social media. [Doctoral dissertation]. Aalto University. Aalto University.
35.
GarimellaK.MoralesG. D. F.GionisA.MathioudakisM. (2018). Quantifying controversy on social media. ACM Transactions on Social Computing, 1(1), 1–27. https://doi.org/10.1145/3140565
36.
GarimellaK.WeberI. (2017). A long-term analysis of polarization on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 528–531. https://doi.org/10.48550/arXiv.1703.02769
37.
GarrettR. K. (2009). Echo chambers online?: Politically motivated selective exposure among internet news users. Journal of Computer-mediated Communication, 14(2), 265–285. https://doi.org/10.1111/j.1083-6101.2009.01440.x
38.
HagenL.NeelyS.KellerT. E.ScharfR.VasquezF. E. (2020). Rise of the machines? Examining the influence of social bots on a political discussion network. Social Science Computer Review, 40(2), 264–287. https://doi.org/10.1177/0894439320908190
39.
HartP. S.ChinnS.SorokaS. (2020). Politicization and polarization in COVID-19 news coverage. Science Communication, 42(5), 679–697. https://doi.org/10.1177/1075547020950735
40.
HetheringtonM. J. (2001). Resurgent mass partisanship: The role of elite polarization. American Political Science Review, 95(3), 619–631. https://doi.org/10.1017/S0003055401003045
41.
JiangJ.ChenE.YanS.LermanK.FerraraE. (2020). Political polarization drives online conversations about COVID-19 in the United States. Human Behavior and Emerging Technologies, 2(3), 200–211. https://doi.org/10.1002/hbe2.202
42.
JiangX.SuM.-H.HwangJ.LianR.BrauerM.KimS.ShahD. (2021). Polarization over vaccination: Ideological differences in Twitter expression about COVID-19 vaccine favorability and specific hesitancy concerns. Social Media+ Society, 7(3), Article 205630512110484. https://doi.org/10.1177/20563051211048413
43.
JosephK.Swire-ThompsonB.MasugaH.BaumM. A.LazerD. (2019). Polarized, together: Comparing partisan support for trump’s tweets using survey and platform-based measures. Proceedings of the International AAAI Conference on Web and Social Media, 13(01), 290–301.
44.
Knobloch-WesterwickS.MengJ. (2009). Looking the other way: Selective exposure to attitudeconsistent and counterattitudinal political information. Communication Research, 36(3), 426–448. https://doi.org/10.1177/0093650209333030
45.
LeeD.HahnK. S.YookS.-H.ParkJ. (2015). Quantifying discrepancies in opinion spectra from online and offline networks. PloS one, 10(4), Article e0124722. https://doi.org/10.1371/journal.pone.0124722
46.
LeeF. L. (2016). Impact of social media on opinion polarization in varying times. Communication and the Public, 1(1), 56–71. https://doi.org/10.1177/2057047315617763
47.
LiuB. (2010). Sentiment analysis and subjectivity. Handbook of natural language processing, 2(2010), 627–666.
48.
MatakosA.TerziE.TsaparasP. (2017). Measuring and moderating opinion polarization in social networks. Data Mining and Knowledge Discovery, 31(5), 1480–1505. https://doi.org/10.1007/s10618-017-0527-9
49.
MayringP. (2015). Qualitative Inhaltsanalyse (12th ed.). Beltz Verlagsgruppe.
50.
NewmanN.FletcherR.SchulzA.AndıS.RobertsonC. T.NielsenR. K. (2021). Reuters institute digital news report 2021.
51.
PasekJ.McClainC. A.NewportF.MarkenS. (2020a). Who’s tweeting about the president? What big survey data can tell us about digital traces?. Social Science Computer Review, 38(5), 633–650. https://doi.org/10.1177/0894439318822007
52.
PasekJ.SinghL. O.WeiY.SorokaS. N.LaddJ. M.TraugottM. W.BudakC.BodeL.NewportF. (2020b). Attention to campaign events: Do Twitter and self-report metrics tell the same story? In Big data meets survey science: a collection of innovative methods (pp. 193–216). https://doi.org/10.1002/9781118976357.ch6
PfisterR.SchwarzK. A.JanczykM.DaleR.FreemanJ. (2013). Good things peak in pairs: A note on the bimodality coefficient. Frontiers in Psychology, 4(2013), 700. https://doi.org/10.3389/fpsyg.2013.00700
Reiter-HaasM.KloeschB.HadlerM.LexE. (2020). Bridging the gap of polarization in public opinion on misinformed topics. Challenging Misinformation: Exploring Limits and Approaches, workshop co-located with Social Informatics’20.
58.
RitchieH.MathieuE.Rod´es-GuiraoL.AppelC.GiattinoC.Ortiz-OspinaE.HasellJ.MacdonaldB.BeltekianD.RoserM. (2020). Policy responses to the coronavirus pandemic. Ourworldindata. https://ourworldindata.org/policy-responses-covid
59.
RobinsonW. S. (2009). Ecological correlations and the behavior of individuals. International journal of epidemiology, 38(2), 337–341. https://doi.org/10.1093/ije/dyn357
60.
SASS. (2012). Stat 12.1: User’s guide cary. SAS Institute Inc.
61.
SchmidtA. L.ZolloF.ScalaA.BetschC.QuattrociocchiW. (2018). Polarization of the vaccination debate on Facebook.. Polarization of the vaccination debate on Facebook. Vaccine, 36(25), 3606–3612. https://doi.org/10.1016/j.vaccine.2018.05.040
62.
SloanL. (2017). Who tweets in the United Kingdom? Profiling the Twitter population using the British social attitudes survey 2015. Social Media+ Society, 3(1), Article 205630511769898. https://doi.org/10.1177/2056305117698981
StierS.BreuerJ.SiegersP.ThorsonK. (2020). Integrating survey data and digital trace data: Key issues in developing an emerging field. Social Science Computer Review, 38(5), 503–516. https://doi.org/10.1177/0894439319843669
SunsteinC. R. (1999). The law of group polarization (p. 91). University of Chicago Law School, John M. Olin Law & Economics Working Paper. https://doi.org/10.2139/ssrn.199668
68.
TuckerJ. A.GuessA.BarberáP.VaccariC.SiegelA.SanovichS.StukalD.NyhanB. (2018). Social media, political polarization, and political disinformation: A review of the scientific literature. SSRN Electronic Journal, 2018. https://doi.org/10.2139/ssrn.3144139
69.
Van BavelJ. J.BaickerK.BoggioP. S.CapraroV.CichockaA.CikaraM.CrockettM. J.CrumA. J.DouglasK. M.DruckmanJ. N.DruryJ.DubeO.EllemersN.FinkelE. J.FowlerJ. H.GelfandM.HanS.HaslamS. A.JettenJ.WillerR. (2020). Using social and behavioural science to support COVID-19 pandemic response. Nature Human Behaviour, 4(5), 460–471. https://doi.org/10.1038/s41562-020-0884-z
70.
YarchiM.BadenC.Kligler-VilenchikN. (2020). Political polarization on the digital sphere: A cross-platform, over-time analysis of interactional, positional, and affective polarization on social media. Political Communication, 30(1-2), 98–139. https://doi.org/10.1080/10584609.2020.1785067
71.
ZhangX.HoJ. C. (2020). Exploring the fragmentation of the representation of data-driven journalism in the Twittersphere: A network analytics approach. Social Science Computer Review, 40(1), 42–60. https://doi.org/10.1177/0894439320905522
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.
