oa Comparing automated content analysis methods to distinguish issue communication by political parties on Twitter

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).
   [Google Scholar]
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.
    [Google Scholar]
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.".
    [Google Scholar]
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.