Merge branch 'main' of https://github.com/baunef/gcnews

README.md

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-# gcnews
- Code and Data repository for a master's thesis @ Hochschule für Politik, München.
+# GC News: Content Analysis with regard to Populism of German News Media during the Covid-19 pandemic
+
+This repository holds the data, code, and results for a master thesis with the Hochschule für Politik at the Technical University of Munich. It includes all ground truth labels and a fully labeled version of the _GC News_ data. To comply with copyright laws, the journalistic contents were removed from the \textit{GC News} data and are available for replication upon request. It further includes the _Python_ and _R_ code used for model creation, fine-tuning, and evaluation, as well as for regression analysis and data visualization. 
+
+## Data
+
+A labeled version of the entire corpus of German news paragraphs is included at `gcnews/gc_news_labeled.csv`. To comply with copyright laws, the journalistic contents were removed from this file. With the labels and other meta information, this file can be used to reproduce the regression and data analysis conducted in this study. A second `gcnews/gc_news_unlabeled_sample1000.csv` file contains 1000 random sample paragraphs from the _GC News_ dataset and can be used to understand, track and reproduce the labeling process. 
+
+The `ground_truth/labeled_data.csv` and `ground_truth/labeled_data_unbalanced.csv` files contain a stratified and a balanced version of the ground truth labels that were used to train, fine-tune and evaluate the five implemented classification methods. 
+
+The `external` data includes the daily 7-day incidence and casualty numbers of Covid-19 in Germany, as recorded by the Robert Koch Institute. The data was initially published @
+- Robert Koch-Institut (2023): 7-Tage-Inzidenz der COVID-19-Fälle in Deutschland, Berlin: Zenodo. DOI: 10.5281/zenodo.10207072
+- Robert Koch-Institut (2023): COVID-19-Todesfälle in Deutschland, Berlin: Zenodo. DOI: 10.5281/zenodo.10207073
+
+## Text Classification
+
+### Dictionaries
+
+This publication applies three dictionaries via the [_multidictR_](https://github.com/jogrue/multidictR) and [_popdictR_](https://github.com/jogrue/popdictR) packages. Together with Gründl`s dictionary on populism, these were published in 
+- Gründl, J. (2022). Populist ideas on social media: A dictionary-based measurement of populist communication. New Media & Society, 24(6), 1481–1499. https://doi.org/10.1177/1461444820976970
+
+The `thiele_covid_terms.csv` and `thiele_econ_terms.csv` dictionaries were originally published @
+- Thiele, D. (2022). Pandemic Populism? How Covid-19 Triggered Populist Facebook User Comments in Germany and Austria. Politics and Governance, 10(1), 185–196. https://doi.org/10.17645/pag.v10i1.4712
+
+### Compressor-based Classification
+
+The code in the `text_classification/gzip` folder implements a compressor-based _k_-NN-classifier and relies on Jiang _et al._`s [npc_gzip](https://github.com/bazingagin/npc_gzip) library. The Python codebase required for reproduction is available on pypi.org via
+```
+pip install npc-gzip
+```
+and was published @ 
+- Jiang, Z., Yang, M., Tsirlin, M., Tang, R., Dai, Y., & Lin, J. (2023). “Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors. Findings of the Association for Computational Linguistics: ACL 2023, 6810–6828. https://aclanthology.org/2023.findings-acl.426
+
+### SetFit Classifier
+The exact model specifications and usage instructions for the _SetFit_ classifier are available at https://huggingface.co/baunef/PopFit. 
+
+
+### PopBERT
+The `text_classification/zero_shot/zero_shot.ipynb` Jupyter notebook contains an inference pipeline for 
+
+
+
+
+
+
+
+## Citing GC News
+
+If you use parts of this repository in your research, please consider citing