Stylistic Features Usage: Similarities and Differences Using Multiple Social Networks

  • Kholoud Khalil AldousEmail author
  • Jisun An
  • Bernard J. Jansen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11864)


User engagement on social networks is essential for news outlets where they often distribute online content. News outlets simultaneously leverage multiple social media platforms to reach their overall audience and to increase marketshare. In this research, we analyze ten common stylistic features indicative of user engagement for news postings on multiple social media platforms. We display the stylistic features usage differences of news posts from various news sources. Results show that there are differences in the usage of stylistic features across social media platforms (Facebook, Instagram, Twitter, and YouTube). Online news outlets can benefit from these findings in building guidelines for content editors and creators to create more users engaging postings.


Stylistic features User engagement News outlets 

Supplementary material


  1. 1.
    An, J., Kwak, H., Jansen, B.J.: Automatic generation of personas using youtube social media data. In: HICSS (2017)Google Scholar
  2. 2.
    Arapakis, I., Lalmas, M., Cambazoglu, B.B., Marcos, M.C., Jose, J.M.: User engagement in online news: under the scope of sentiment, interest, affect, and gaze. J. Assoc. Inf. Sci. Technol. 65(10), 1988–2005 (2014)CrossRefGoogle Scholar
  3. 3.
    Banhawi, F., Ali, N.M.: Measuring user engagement attributes in social networking application. In: STAIR. IEEE (2011)Google Scholar
  4. 4.
    Brems, C., Temmerman, M., Graham, T., Broersma, M.: Personal branding on twitter. Dig, J. 5(4), 443–459 (2017)Google Scholar
  5. 5.
    Burney, K.: How to outperform fortune 500 brands on instagram (2016).
  6. 6.
    Hoiles, W., Aprem, A., Krishnamurthy, V.: Engagement and popularity dynamics of youtube videos and sensitivity to meta-data. Trans. Knowl. Data Eng. 29(7), 1426–1437 (2017)CrossRefGoogle Scholar
  7. 7.
    Hong, L., Yang, W., Resnik, P., Frias-Martinez, V.: Uncovering topic dynamics of social media and news: the case of ferguson. In: Spiro, E., Ahn, Y.-Y. (eds.) SocInfo 2016. LNCS, vol. 10046, pp. 240–256. Springer, Cham (2016). Scholar
  8. 8.
    Hua, T., Ning, Y., Chen, F., Lu, C.T., Ramakrishnan, N.: Topical analysis of interactions between news and social media. In: AAAI (2016)Google Scholar
  9. 9.
    Huotari, L., Ulkuniemi, P., Saraniemi, S., Mäläskä, M.: Analysis of content creation in social media by B2B companies. J. Bus. Ind. Mark. 30(6), 761–770 (2015)CrossRefGoogle Scholar
  10. 10.
    Hutto, C.J., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: ICWSM (2014)Google Scholar
  11. 11.
    Jaakonmäki, R., Müller, O., Vom Brocke, J.: The impact of content, context, and creator on user engagement in social media marketing. In: HICSS (2017)Google Scholar
  12. 12.
    Khatua, A., Khatua, A., Cambria, E.: A tale of two epidemics: contextual word2vec for classifying twitter streams during outbreaks. Info. Process. Manag. 56(1), 247–257 (2019)CrossRefGoogle Scholar
  13. 13.
    Lotan, G., Gaffney, D., Meyer, C.: Audience analysis of major news accounts on twitter. Soc. Flow 3, 211 (2011)Google Scholar
  14. 14.
    Manikonda, L., Meduri, V.V., Kambhampati, S.: Tweeting the mind and instagramming the heart: exploring differentiated content sharing on social media. In: ICWSM (2016)Google Scholar
  15. 15.
    Naveed, N., Gottron, T., Kunegis, J., Alhadi, A.C.: Bad news travel fast: a content-based analysis of interestingness on twitter. In: WebSci. ACM (2011)Google Scholar
  16. 16.
    Pletikosa Cvijikj, I., Michahelles, F.: Online engagement factors on facebook brand pages. Soc. Netw. Anal. Min. 3(4), 843–861 (2013)CrossRefGoogle Scholar
  17. 17.
    Sayre, B., Bode, L., Shah, D., Wilcox, D., Shah, C.: Agenda setting in a digital age: tracking attention to california proposition 8 in social media, online news and conventional news. Pol. Internet 2(2), 7–32 (2010)CrossRefGoogle Scholar
  18. 18.
    Schlagwein, D., Hu, M.: How and why organisations use social media: five use types and their relation to absorptive capacity. J. Inf. Technol. 32(2), 194–209 (2017)CrossRefGoogle Scholar
  19. 19.
    Shearer, E., Gottfried, J.: News use across social media platforms 2017. Pew Research Center (2017)Google Scholar
  20. 20.
    Stocking, G.: Digital news fact sheet. State of the News Media, pp. 1–2 (2017)Google Scholar
  21. 21.
    Thelwall, M., Stuart, E.: She’s reddit: a source of statistically significant gendered interest information? Inf. Process. Manag. 56(4), 1543–1558 (2019)CrossRefGoogle Scholar
  22. 22.
    Wu, B., Shen, H.: Analyzing and predicting news popularity on twitter. Int. J. Inf. Manag. 35(6), 702–711 (2015)CrossRefGoogle Scholar
  23. 23.
    Xing, F.Z., Pallucchini, F., Cambria, E.: Cognitive-inspired domain adaptation of sentiment lexicons. Inf. Process. Manag. 56(3), 554–564 (2019)CrossRefGoogle Scholar
  24. 24.
    Yu, B., Chen, M., Kwok, L.: Toward predicting popularity of social marketing messages. In: Salerno, J., Yang, S.J., Nau, D., Chai, S.-K. (eds.) SBP 2011. LNCS, vol. 6589, pp. 317–324. Springer, Heidelberg (2011). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kholoud Khalil Aldous
    • 1
    Email author
  • Jisun An
    • 2
  • Bernard J. Jansen
    • 2
  1. 1.College of Science and EngineeringHamad Bin Khalifa UniversityDohaQatar
  2. 2.Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar

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