Challenges of E-Participation: Can the Opinions of Netizens Represent and Affect Mass Opinions?

  • Chungpin LeeEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 947)


This paper aims to understand the representativeness of online public opinion and the influence of online public-issue discussions on mass opinion. By analyzing three survey datasets from Taiwan, the findings show that online civic participants are not representative of the general population; moreover, online discussions of public issues do not directly affect general public opinion. According to these findings, this paper recommends that online public opinions are used with caution as they are not necessarily representative of general public opinion.


Sentiment analysis Public opinion poll E-participation Representativeness 


  1. Beauchamp, N.: Predicting and interpolating state-level polls using Twitter textual data. Am. J. Polit. Sci. 61(2), 490–503 (2017)CrossRefGoogle Scholar
  2. Bermingham, A., Smeaton, A.: On using Twitter to monitor political sentiment and predict election results. Paper presented at the Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011) (2011)Google Scholar
  3. Ceron, A., Curini, L., Iacus, S.M., Porro, G.: Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media Soc. 16(2), 340–358 (2014)CrossRefGoogle Scholar
  4. Ceron, A., Curini, L., Iacus, S.M.: Using sentiment analysis to monitor electoral campaigns: method matters—evidence from the United States and Italy. Soc. Sci. Comput. Rev. 33(1), 3–20 (2015)CrossRefGoogle Scholar
  5. Conover, M., Ratkiewicz, J., Francisco, M.R., Gonçalves, B., Menczer, F., Flammini, A.: Political polarization on Twitter. ICWSM 133, 89–96 (2011)Google Scholar
  6. DiGrazia, J., McKelvey, K., Bollen, J., Rojas, F.: More tweets, more votes: social media as a quantitative indicator of political behavior. PLoS One 8(11), e79449 (2013)CrossRefGoogle Scholar
  7. Gayo-Avello, D.: Don’t turn social media into another ‘Literary Digest’ poll. Commun. ACM 54(10), 121–128 (2011)CrossRefGoogle Scholar
  8. Gayo-Avello, D.: A meta-analysis of state-of-the-art electoral prediction from Twitter data. Soc. Sci. Comput. Rev. 31(6), 649–679 (2013)CrossRefGoogle Scholar
  9. Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P., Rosenquist, J.N.: Understanding the demographics of Twitter users. In: ICWSM 2011, no. 5, p. 25 (2011)Google Scholar
  10. O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. ICWSM 11(122–129), 1–2 (2010)Google Scholar
  11. Prichard, J., Watters, P., Krone, T., Spiranovic, C., Cockburn, H.: Social media sentiment analysis: a new empirical tool for assessing public opinion on crime. Curr. Issues Crim. Just. 27, 217 (2015)CrossRefGoogle Scholar
  12. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Election forecasts with Twitter: how 140 characters reflect the political landscape. Soc. Sci. Comput. Rev. 29(4), 402–418 (2011)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Public Administration and PolicyNational Taipei UniversityNew Taipei CityTaiwan

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