References

Albaugh, Q., Sevenans, J., Soroka, S., & Loewen, P. J. (2013). The Automated Coding of Policy Agendas: A Dictionary-based Approach. 6th Annual Comparative Agendas Conference, Antwerp, Belgium.

Bakker, R., Vries, C. de, Edwards, E., Hooghe, L., Jolly, S., Marks, G., Polk, J., Rovny, J., Steenbergen, M. R., & Vachudova, M. A. (2012). Measuring party positions in europe: The chapel hill expert survey trend file, 1999-2010. Party Politics, 21(1), 1–15. https://doi.org/10.1177/1354068812462931

Benoit, K., Laver, M., & Mikhaylov, S. (2009). Treating words as data with error: Uncertainty in text statements of policy positions. American Journal of Political Science, 53(2), 495–513. https://doi.org/10.1111/j.1540-5907.2009.00383.x

Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018). Quanteda: An r package for the quantitative analysis of textual data. Journal of Open Source Software, 3(30), 774. https://doi.org/10.21105/joss.00774

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.

Bruns, A. (2019). After the ‘APIcalypse’: Social media platforms and their fight against critical scholarly research. Information, Communication & Society, 22(11), 1544–1566. https://doi.org/10.1080/1369118X.2019.1637447

Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. Sage. https://doi.org/10.4135/9781412985642

Clarke, I., & Grieve, J. (2019). Stylistic variation on the donald trump twitter account: A linguistic analysis of tweets posted between 2009 and 2018. PLOS ONE, 14(9), 1–27. https://doi.org/10.1371/journal.pone.0222062

Freelon, D. (2018). Computational Research in the Post-API Age. Political Communication, 35(4), 665–668. https://doi.org/10.1080/10584609.2018.1477506

Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(suppl 1), 5228–5235. https://doi.org/10.1073/pnas.0307752101

Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as Data: A New Framework for Machine Learning and the Social Sciences. Princeton University Press.

Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfals of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297. https://doi.org/10.1093/pan/mps028

Haselmayer, M., & Jenny, M. (2017). Sentiment analysis of political communication: Combining a dictionary approach with crowdcoding. Quality & Quantity, 51(6), 2623–2646. https://doi.org/10.1007/s11135-016-0412-4

Hayes, A. F., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1(1), 77–89. https://doi.org/10.1080/19312450709336664

Hutto, C., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550

Krippendorff, K. (2018). Content Analysis - an Introduction to Its Methodology (4th ed.). Sage.

Lamprianou, I. (2020). Measuring and visualizing coders’ reliability: New approaches and guidelines from experimental data. Sociological Methods & Research. https://doi.org/10.1177/0049124120926198

Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. The American Political Science Review, 97(2), 311–331. https://doi.org/10.1017/S0003055403000698

Laver, M., & Garry, J. (2000). Estimating policy positions from political texts. American Journal of Political Science, 44(3), 619–634. https://doi.org/10.2307/2669268

Lê, S., Josse, J., & Husson, F. (2008). Factominer: An r package for multivariate analysis. Journal of Statistical Software, 25(1). https://doi.org/10.18637/jss.v025.i01

Lin, L. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45, 255–268. https://doi.org/10.2307/2532051

Lind, F., Eberl, J.-M., Heidenreich, T., & Boomgaarden, H. G. (2019). When the journey is as important as the goal: A roadmap to multilingual dictionary construction. International Journal of Communication, 13, 4000–4020.

Lowe, W. (2011). JFreq: Count Words, Quickly. http://www.conjugateprior.org/software/jfreq/

Lowe, W., & Benoit, K. (2011). Estimating uncertainty in quantitative text analysis. Annual Meeting of the Midwest Political Science Association.

Martin, L. W., & Vanberg, G. (2008). Reply to benoit and laver. Political Analysis, 16(1), 112–114. https://doi.org/10.1093/pan/mpm018

Merz, N., Regel, S., & Lewandowski, J. (2016). The manifesto corpus: A new resource for research on political parties and quantitative text analysis. Research & Politics, 3(2), 2053168016643346. https://doi.org/10.1177/2053168016643346

Mikhaylov, S., Laver, M., & Benoit, K. (2012). Coder reliability and misclassification in the human coding of party manifestos. Political Analysis, 20(1), 78–91. https://doi.org/10.1093/pan/mpr047

Munzert, S., Rubba, C., Meißner, P., & Nyhuis, D. (2014). Automated data collection with r: A practical guide to web scraping and text mining. John Wiley & Sons.

Perriam, J., Birkbak, A., & Freeman, A. (2020). Digital Methods in a Post-API Environment. International Journal of Social Research Methodology, 23(3), 277–290. https://doi.org/10.1080/13645579.2019.1682840

Roberts, M. E., Stewart, B. M., & Tingley, D. (2019). stm: An R Package for Structural Topic Models. Journal of Statistical Software, 91(2). https://doi.org/10.18637/jss.v091.i02

Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B., & Rand, D. G. (2014). Structural topic models for open-ended survey responses. American Journal of Political Science, 58(4), 1064–1082. https://doi.org/10.1111/ajps.12103

Slapin, J. B., & Proksch, S.-O. (2008). A scaling model for estimating time-series party positions from texts. American Journal of Political Science, 52(3), 705–722. https://doi.org/10.1111/j.1540-5907.2008.00338.x

Volkens, A., Krause, W., Lehmann, P., Matthieß, T., Merz, N., Regel, S., & Weßels, B. (2019). The Manifesto Data Collection. Manifesto Project (MRG/CMP/MARPOR). Berlin: Wissenschaftszentrum Berlin für Sozialforschung (WZB). https://doi.org/10.25522/manifesto.mpds.2019b

Welbers, K., Van Atteveldt, W., & Benoit, K. (2017). Text Analysis in R. Communication Methods and Measures, 11(4), 245–265. https://doi.org/10.1080/19312458.2017.1387238

Young, L., & Soroka, S. (2012). Lexicoder sentiment dictionary. http://www.snsoroka.com/data-lexicoder/