2004
Volume 3, Issue 2
  • ISSN: 2665-9085
  • E-ISSN: 2665-9085

Abstract

Abstract

Party competition in Western Europe is increasingly focused on “issue competition”, which is the selective emphasis on issues by parties. The aim of this paper is to contribute methodologically to the increasing number of studies that deal with different aspects of parties’ issue competition and communication. We systematically compare the value and shortcomings of three exploratory text representation approaches to study the issue communication of parties on Twitter. More specifically, we analyze which issues separate the online communication of one party from that of the other parties and how consistent party communication is. Our analysis was performed on two years of Twitter data from six Belgian political parties, comprising of over 56,000 political tweets. The results indicate that our exploratory approach is useful to study how political parties profile themselves on Twitter and which strategies are at play. Second, our method allows to analyze communication of individual politicians which contributes to classical literature on party unity and party discipline. A comparison of our three methods shows a clear trade-off between interpretability and discriminative power, where a combination of all three simultaneously provides the best insights.

Loading

Article metrics loading...

/content/journals/10.5117/CCR2021.2.004.PRAE
2021-10-01
2021-10-27
Loading full text...

Full text loading...

/deliver/fulltext/26659085/3/2/CCR2021.2.004.PRAE.html?itemId=/content/journals/10.5117/CCR2021.2.004.PRAE&mimeType=html&fmt=ahah

References

  1. Andeweg, R. B., & Thomassen, J. (2011). Pathways to party unity: Sanctions, loyalty, homogeneity and division of labour in the dutch parliament. Party Politics17 (5), 655–672.
    [Google Scholar]
  2. Barberá, P., Boydstun, A. E., Linn, S., McMahon, R., & Nagler, J. (2019). Automated text classification of news articles: A practical guide. Political Analysis1–24.
    [Google Scholar]
  3. Blei, D. M., & Lafferty, J. D. (2006). Dynamic topic models. In Proceedings of the 23rd international conference on machine learning (pp. 113–120).
  4. Chang, J., Boyd-Graber, J., Wang, C., Gerrish, S., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. In Neural information processing systems (Vol. 22, pp. 288–296).
    [Google Scholar]
  5. Conover, M., Ratkiewicz, J., Francisco, M. R., Gonçalves, B., Menczer, F., & Flammini, A. (2011). Political polarization on twitter. ICWSM, 133, 89–96.
    [Google Scholar]
  6. Damore, D. F. (2005). Issue convergence in presidential campaigns. Political Behavior, 27 (1), 71–97.
    [Google Scholar]
  7. Depauw, S., & Martin, S. (2009). Legislative party discipline and cohesion in comparative perspective. Intra-party politics and coalition governments103120, 103–120.
    [Google Scholar]
  8. De Sio, L., & Lachat, R. (2020). Making sense of party strategy innovation: challenge to ideology and conflict-mobilisation as dimensions of party competition. West European Politics43 (3), 688–719.
    [Google Scholar]
  9. Dun, L., Soroka, S., & Wlezien, C. (2020). Dictionaries, supervised learning, and media coverage of public policy. Political Communication1–19.
    [Google Scholar]
  10. Flach, P. A., Hernández-Orallo, J., & Ramirez, C. F. (2011). A coherent interpretation of auc as a measure of aggregated classification performance. In Icml.
    [Google Scholar]
  11. Gentzkow, M., Shapiro, J., Taddy, M., . (2016). Measuring polarization in high- dimensional data: Method and application to congressional speech.
    [Google Scholar]
  12. Green-Pedersen, C. (2007). The growing importance of issue competition: The changing nature of party competition in western europe. Political studies55 (3), 607–628.
    [Google Scholar]
  13. Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis21 (3), 267–297.
    [Google Scholar]
  14. Gupta, V., & Hewett, R. (2020). Real-time tweet analytics using hybrid hashtags on twitter big data streams. Information11 (7), 341.
    [Google Scholar]
  15. Han, B., & Baldwin, T. (2011). Lexical normalisation of short text messages: Makn sens a #twitter. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies - volume 1 (pp. 368–378). Stroudsburg, PA, USA: Association for Computational Linguistics. Retrieved from http://dl.acm.org/ citation.cfm?id=2002472.2002520
    [Google Scholar]
  16. Hasan, M., Agu, E., & Rundensteiner, E. (2014). Using hashtags as labels for supervised learning of emotions in twitter messages. In Acm sigkdd workshop on health informatics, new york, usa.
    [Google Scholar]
  17. Hopkins, D. J., & King, G. (2010). A method of automated nonparametric content analysis for social science. American Journal of Political Science54 (1), 229–247.
    [Google Scholar]
  18. Jungherr, A. (2016). Twitter use in election campaigns: A systematic literature review. Journal of information technology & politics13 (1), 72–91.
    [Google Scholar]
  19. Kreiss, D. (2016). Seizing the moment: The presidential campaignsâ use of twitter during the 2012 electoral cycle. New media & society18 (8), 1473–1490.
    [Google Scholar]
  20. Kuang, D., Brantingham, P. J., & Bertozzi, A. L. (2017). Crime topic modeling. Crime Science6 (1), 1–20.
    [Google Scholar]
  21. Lai, S., Xu, L., Liu, K., & Zhao, J. (2015). Recurrent convolutional neural networks for text classification. In Aaai (Vol. 333, pp. 2267–2273).
    [Google Scholar]
  22. O’callaghan, D., Greene, D., Carthy, J., & Cunningham, P. (2015). An analysis of the coherence of descriptors in topic modeling. Expert Systems with Applications42 (13), 5645–5657.
    [Google Scholar]
  23. Parmelee, J. H., & Bichard, S. L. (2011). Politics and the twitter revolution: How tweets influence the relationship between political leaders and the public. Lexington Books.
  24. Paul, D., Li, F., Teja, M. K., Yu, X., & Frost, R. (2017). Compass: Spatio temporal sentiment analysis of us election what twitter says! In Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining (pp. 1585–1594).
    [Google Scholar]
  25. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... others (2011). Scikit-learn: Machine learning in python. the Journal of machine Learning research12, 2825–2830.
    [Google Scholar]
  26. Peeters, J., Van Aelst, P., & Praet, S. (2019). Party ownership or individual specialization? a comparison of politicians’ individual issue attention across three different agendas. Party Politics 1354068819881639.
    [Google Scholar]
  27. Petrocik, J. R. (1996). Issue ownership in presidential elections, with a 1980 case study. American journal of political science825–850.
    [Google Scholar]
  28. Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. "O’Reilly Media, Inc.".
  29. Sevenans, J., Albaugh, Q., Shahaf, T., Soroka, S., & Walgrave, S. (2014). The automated coding of policy agendas: A dictionary based approach (v. 2.0.). In 7th annual comparative agendas project (cap) conference, konstanz, june (pp. 12–14).
    [Google Scholar]
  30. Terechshenko, Z., Linder, F., Padmakumar, V., Liu, M., Nagler, J., Tucker, J. A., & Bonneau, R. (2020). A comparison of methods in political science text classification: Transfer learning language models for politics. Available at SSRN.
    [Google Scholar]
  31. Thomassen, J., & Schmitt, H. (1997). Policy representation. European Journal of Political Research32 (2), 165–184.
    [Google Scholar]
  32. Tresch, A., Lefevere, J., & Walgrave, S. (2017). How parties’ issue emphasis strategies vary across communication channels: The 2009 regional election campaign in belgium. Acta Politica1–23.
    [Google Scholar]
  33. Van Dalen, A., Fazekas, Z., Klemmensen, R., & Hansen, K. M. (2015). Policy considerations on facebook: Agendas, coherence, and communication patterns in the 2011 danish parliamentary elections. Journal of Information Technology & Politics12 (3), 303–324.
    [Google Scholar]
  34. Van Ditmars, M. M., Maggini, N., & van Spanje, J. (2020). Small winners and big losers: strategic party behaviour in the 2017 dutch general election. West European Politics43 (3), 543–564.
    [Google Scholar]
  35. Van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning109 (2), 373–440.
    [Google Scholar]
  36. Van Erkel, P., Thijssen, P., & Van Aelst, P. (2014). Vier ideologische dimensies, één breuklijn. Stichting Gerrit Kreveld: Samenleving en Politiek1–12.
    [Google Scholar]
  37. Van Santen, R., Helfer, L., & Van Aelst, P. (2015). When politics becomes news: An analysis of parliamentary questions and press coverage in three west european countries. Acta Politica50 (1), 45–63.
    [Google Scholar]
  38. Vargo, C. J., Guo, L., McCombs, M., & Shaw, D. L. (2014). Network issue agendas on twitter during the 2012 us presidential election. Journal of Communication64 (2), 296–316.
    [Google Scholar]
  39. Wagner, M., & Meyer, T. M. (2014). Which issues do parties emphasise? salience strategies and party organisation in multiparty systems. West European Politics37 (5), 1019–1045.
    [Google Scholar]
  40. Walgrave, S., Tresch, A., & Lefevere, J. (2015). The conceptualisation and measurement of issue ownership. West European Politics38 (4), 778–796.
    [Google Scholar]
  41. Wu, L., Morstatter, F., & Liu, H. (2018). Slangsd: building, expanding and using a sentiment dictionary of slang words for short-text sentiment classification. Language Resources and Evaluation52 (3), 839–852.
    [Google Scholar]
  42. Zirn, C., Glavaš, G., Nanni, F., Eichorts, J., & Stuckenschmidt, H. (2016). Classifying topics and detecting topic shifts in political manifestos.
http://instance.metastore.ingenta.com/content/journals/10.5117/CCR2021.2.004.PRAE
Loading
/content/journals/10.5117/CCR2021.2.004.PRAE
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error