Skip to main content

Readability of Posts and User Engagement in Online Communities of Government Executive Bodies

  • Conference paper
  • First Online:
Social Computing and Social Media: Experience Design and Social Network Analysis (HCII 2021)

Abstract

The article deals with the question of the link between readability and engagement rates on social media. On one hand, easy-to-read texts can be useful to attract and involve broader audience, but on the other hand, texts which draw attention and spark discussions often tend to be complex, controversial, even sophisticated, and consequentially less readable. Our database consisted of 115245 posts retrieved from social networking site VKontakte, the most popular SNS in Russia. The sample included all publicly available posts in online communities of 47 Russian state bodies: ministries, federal services and federal agencies published from 01.01.2017 to 16.09.2020. For each post, engagement rate (ER) and 79 other metrics of the texts were calculated. Gradient Boosted Decision Trees were used to build the regression model which took into account all the features including 10 different readability metrics and other measures, such as topics, linguistic characteristics, sentiment and so on. As a result, the most significant factors were the variables determining the presence of certain topics. All readability scores were weak predictors of engagement rate. And furthermore, our data provided no evidence that topics can help to increase ER, but only the topics causing lowering of ER. Using correlation analysis, we showed that in the case of communication strategies in online communities in social network VKontakte, the readability of posts is not directly related to engagement rates.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Machiavelli, N.: Concerning the politician and the media. In: The Politician, pp. 97–102. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39091-4_19

  2. Tolochko, P., Boomgaarden, H.G.: Determining political text complexity: conceptualizations, measurements, and application. Int. J. Commun. 13, 1784–1804 (2019)

    Google Scholar 

  3. Dalecki, L., Lasorsa, D.L., Lewis, S.C.: The news readability problem. Journal. Pract. 3(1), 1–12 (2009)

    MathSciNet  Google Scholar 

  4. Bigi, A.: Viral political communication and readability: an analysis of an Italian political blog. J. Public Aff. 13(2), 209–217 (2013)

    Article  Google Scholar 

  5. Kayam, O.: The readability and simplicity of Donald Trump’s language. Polit. Stud. Rev. 16(1), 73–88 (2018)

    Article  Google Scholar 

  6. Graefe, A., Haim, M., Haarmann, B., Brosius, H.B.: Readers’ perception of computer-generated news: credibility, expertise, and readability. Journalism 19(5), 595–610 (2018)

    Article  Google Scholar 

  7. Aldous, K.K., An, J., Jansen, B.J.: View, like, comment, post: analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 47–57 (2019)

    Google Scholar 

  8. Balmas, M., Sheafer, T.: Candidate image in election campaigns: attribute agenda setting, affective priming, and voting intentions. Int. J, Public Opin. Res. 22(2), 204–229 (2010)

    Article  Google Scholar 

  9. Funk, M.J., McCombs, M.: Strangers on a theoretical train: inter-media agenda setting, community structure, and local news coverage. Journal. Stud. 18(7), 845–865 (2017)

    Google Scholar 

  10. Pancer, E., Chandler, V., Poole, M., Noseworthy, T.J.: How readability shapes social media engagement. J. Consum. Psychol. 29(2), 262–270 (2019)

    Article  Google Scholar 

  11. Leonhardt, J.M., Makienko, I.: Keep it simple, readability increases engagement on twitter: an abstract. In: Krey, N., Rossi, Patricia (eds.) AMSAC 2017. DMSPAMS, pp. 333–334. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-66023-3_116

    Chapter  Google Scholar 

  12. Xu, Z., Ellis, L., Umphrey, L.R.: The easier the better? Comparing the readability and engagement of online pro-and anti-vaccination articles. Health Educ. Behav. 46(5), 790–797 (2019)

    Article  Google Scholar 

  13. Tolochko, P., Song, H., Boomgaarden, H.: “That looks hard!”: effects of objective and perceived textual complexity on factual and structural political knowledge. Polit. Commun. 36(4), 609–628 (2019)

    Article  Google Scholar 

  14. Melloni, G., Caglio, A., Perego, P.: Saying more with less? Disclosure conciseness, completeness and balance in integrated reports. J. Account. Public Policy 36(3), 220–238 (2017)

    Article  Google Scholar 

  15. Hassan, M.K., Abbas, B.A., Garas, S.N.: Readability, governance and performance: a test of the obfuscation hypothesis in Qatari listed firms. Corporate Governance. Int. J. Bus. Soc. 19(2), 270–298 (2019)

    Google Scholar 

  16. Benoit, K., Munger, K., Spirling, A.: Measuring and explaining political sophistication through textual complexity. Am., J. Polit. Sci. 63(2), 491–508 (2019)

    Article  Google Scholar 

  17. Al Qundus, J., Paschke, A., Gupta, S., Alzouby, A.M., Yousef, M.: Exploring the impact of short-text complexity and structure on its quality in social media. J. Enterp. Inf. Manag. 33(6), 1443–1466 (2020)

    Article  Google Scholar 

  18. Temnikova, I., Vieweg, S., Castillo, C.: The case for readability of crisis communications in social media. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1245–1250 (2015)

    Google Scholar 

  19. Michalski, K.B., Guile, M.N.: Readability of simulated state question ballots affects voting behavior. Bull. Psychon. Soc. 28(3), 239–240 (1990). https://doi.org/10.3758/BF03334014

    Article  Google Scholar 

  20. King, B.A., Youngblood, N.E.: E-government in Alabama: an analysis of county voting and election website content, usability, accessibility, and mobile readiness. Gov. Inf. Q. 33(4), 715–726 (2016)

    Article  Google Scholar 

  21. Gyasi, W.K.: Readability and political discourse: an analysis of press releases of Ghanaian political parties. J. Media Commun. Stud. 9(6), 42–50 (2017)

    Article  Google Scholar 

  22. Göksu, G.G., Dumlupinar, S.: Readability analysis of laws related to public financial responsibility and state budget: a comparison of selected countries. In: Contemporary Studies in Economic and Financial Analysis, vol. 105, pp. 91–112. Emerald Publishing Limited (2021)

    Google Scholar 

  23. Bischof, D., Senninger, R.: Simple politics for the people? Complexity in campaign messages and political knowledge. Eur. J. Polit. Res. 57(2), 473–495 (2018)

    Article  Google Scholar 

  24. Schoonvelde, M., Brosius, A., Schumacher, G., Bakker, B.N.: Liberals lecture, conservatives communicate: analyzing complexity and ideology in 381,609 political speeches. PLoS ONE 14(2), e0208450 (2019)

    Article  Google Scholar 

  25. Grimmer, J., Stewart, B.M.: Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Polit. Anal. 21(3), 267–297 (2013)

    Article  Google Scholar 

  26. Berger, J., Milkman, K.L.: What makes online content viral? J. Mark. Res. 49, 192–205 (2012)

    Article  Google Scholar 

  27. Firouzjaei, H.A., Ozdemir, S.F.: Effect of readability of political tweets on positive user engagement. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2020, preprint)

    Google Scholar 

  28. Pancer, E., Poole, M.: The popularity and virality of political social media: hashtags, mentions, and links predict likes and retweets of 2016 US presidential nominees’ tweets. Soc. Influ. 11(4), 259–270 (2016)

    Article  Google Scholar 

  29. Noguti, V.: Post language and user engagement in online content communities. Eur. J. Mark. 50(5/6), 695–723 (2016)

    Article  Google Scholar 

  30. Eberl, J.M., Tolochko, P., Jost, P., Heidenreich, T., Boomgaarden, H.G.: What’s in a post? How sentiment and issue salience affect users’ emotional reactions on Facebook. J. Inform. Tech. Polit. 17(1), 48–65 (2020)

    Article  Google Scholar 

  31. Gerbaudo, P., Marogna, F., Alzetta, C.: When “positive posting” attracts voters: user engagement and emotions in the 2017 UK Election Campaign on Facebook. Soc. Media+ Soc. 5(4), 2056305119881695 (2019)

    Google Scholar 

  32. Preoţiuc-Pietro, D., Liu, Y., Hopkins, D., Ungar, L.: Beyond binary labels: political ideology prediction of Twitter users. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 729–740 (2017)

    Google Scholar 

  33. Shukrun-Nagar, P.: Constructed general truths against specific political rivals in politicians’ Facebook posts. J. Pragmat. 172, 79–88 (2020)

    Article  Google Scholar 

  34. Furini, M., Montangero, M.: On predicting the success of political tweets using psycho-linguistic categories. In: 2019 28th International Conference on Computer Communication and Networks, pp. 1–6. IEEE (2019)

    Google Scholar 

  35. Shugars, S., Beauchamp, N.: Why keep arguing? Predicting engagement in political conversations online. SAGE Open 9(1), 2158244019828850 (2019)

    Article  Google Scholar 

  36. Liebeskind, C., Nahon, K., HaCohen-Kerner, Y., Manor, Y.: Comparing sentiment analysis models to classify attitudes of political comments on Facebook. Polibits 55, 17–23 (2017)

    Google Scholar 

  37. Heiss, R., Schmuck, D., Matthes, J.: What drives interaction in political actors’ Facebook posts? Profile and content predictors of user engagement and political actors’ reactions. Inf. Commun. Soc. 22(10), 1497–1513 (2019)

    Article  Google Scholar 

  38. Dutceac Segesten, A., Bossetta, M., Holmberg, N., Niehorster, D.: The cueing power of comments on social media: how disagreement in Facebook comments affects user engagement with news. Inf. Commun. Soc. 1–20 (2020, ahead of print). https://doi.org/10.1080/1369118X.2020.1850836

  39. Mystem on Github. https://github.com/nlpub/pymystem3. Accessed 01 Feb 2021

  40. François, T., Miltsakaki, E.: Do NLP and machine learning improve traditional readability formulas? In: Proceedings of the First Workshop on Predicting and Improving Text Readability for Target Reader Populations, pp. 49–57 (2012)

    Google Scholar 

  41. Laposhina, N., Veselovskaya, V., Lebedeva, M.U., Kupreshchenko, O.F.: Automated text readability assessment for Russian second language learners. In: Conference: Proceedings of the International Conference on Computational Linguistics and Intellectual Technologies “Dialogue”, vol. 24, pp. 403–413 (2018)

    Google Scholar 

  42. Begtin, I.V.: What is “Clear Russian” in terms of technology. Let’s take a look at the metrics for the readability of texts: the blog of the company “Information Culture”. (in Russian). http://habrahabr.ru/company/infoculture/blog/238875/. Accessed 01 Feb 2021

  43. Solnyshkina, M., Ivanov, V., Solovyev, V.: Readability formula for Russian texts: a modified version. In: Batyrshin, I., Martínez-Villaseñor, M. de L., Ponce Espinosa, H.E. (eds.) MICAI 2018. LNCS (LNAI), vol. 11289, pp. 132–145. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04497-8_11

    Chapter  Google Scholar 

  44. Vorontsov, K., Potapenko, A.: Tutorial on probabilistic topic modeling: additive regularization for stochastic matrix factorization. In: Ignatov, D.I., Khachay, M.Yu., Panchenko, A., Konstantinova, N., Yavorskiy, R.E. (eds.) AIST 2014. CCIS, vol. 436, pp. 29–46. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12580-0_3

    Chapter  Google Scholar 

  45. Ianina, A., Golitsyn, L., Vorontsov, K.: Multi-objective topic modeling for exploratory search in tech news. In: Filchenkov, A., Pivovarova, L., Žižka, J. (eds.) AINL 2017. CCIS, vol. 789, pp. 181–193. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-71746-3_16

    Chapter  Google Scholar 

  46. Golovin, B.: Language and Statistics. Enlightenment, Moscow (1971). (in Russian)

    Google Scholar 

  47. Dostoevsky library. https://pypi.org/project/dostoevsky/. Accessed 01 Feb 2021

  48. Rogers, A., Romanov, A., Rumshisky, A., Volkova, S., Gronas, M., Gribov, A.: RuSentiment: an enriched sentiment analysis dataset for social media in Russian. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 755–763 (2018)

    Google Scholar 

  49. Alsmadi, I., Gan, K.H.: Review of short-text classification. Int. J. Web Inf. Syst. 15(2), 155–182 (2019)

    Article  Google Scholar 

  50. Santos, R., et al.: Measuring the impact of readability features in fake news detection. In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 1404–1413 (2020)

    Google Scholar 

  51. Hadden, K.B., Prince, L.Y., Moore, T.D., James, L.P., Holland, J.R., Trudeau, C.R.: Improving readability of informed consents for research at an academic medical institution. J. Clin. Transl. Sci. 1(6), 361–365 (2017)

    Article  Google Scholar 

Download references

Acknowledgement

The reported study was funded by RFBR and EISR according to the research project № 20-011-31318.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Platonov, K., Svetlov, K. (2021). Readability of Posts and User Engagement in Online Communities of Government Executive Bodies. In: Meiselwitz, G. (eds) Social Computing and Social Media: Experience Design and Social Network Analysis . HCII 2021. Lecture Notes in Computer Science(), vol 12774. Springer, Cham. https://doi.org/10.1007/978-3-030-77626-8_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77626-8_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77625-1

  • Online ISBN: 978-3-030-77626-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics