Update README.md

README.md

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 # Political Parrots: GPT’s Take on Bundestag Tweets
 
-**Tim Michalow (qo27leja), Ian Fischer (uf28alic), Tobias Stirner (zo94suqa), Jonathan Franke (StudOn-Username/Enrollment Number)**
+**Tim Michalow (qo27leja), Ian Fischer (uf28alic), Tobias Stirner (zo94suqa), Jonathan Franke (zu98wibu)**
 
 ## 1 Introduction
 
@@ -22,7 +22,7 @@ The use of social media in political communication has surged in recent years, w
 
 ### Define your research question
 
-#TODO: Ausarbeiten
+How effectively can advanced NLP techniques, particularly transformer-based models such as GPT-2, replicate and simulate the rhetorical styles and sentiment patterns of tweets posted by German political parties on Twitter?
 
 ### How is this document structured
 
@@ -36,7 +36,27 @@ This document is structured as follows:
 
 ## 2 Related Work
 
-#TODO: Heraussuchen
+### NLP and Social Media
+
+The field of natural language processing (NLP) has significantly advanced with the introduction of transformer-based models such as GPT-2 and BERT. Radford et al. (2019) in "Language Models are Unsupervised Multitask Learners" highlight the capabilities of GPT-2 in generating coherent and contextually relevant text. This study underscores the potential of GPT-2 to replicate the complex language patterns found in political communication on social media platforms like Twitter.
+
+### Political Communication on Social Media
+
+Research on political communication emphasizes the strategic use of social media by political entities to engage with the public and influence opinions. Enli and Skogerbø (2013) in "Personalized campaigns in party-centred politics" explore how political parties leverage social media for personalized campaigns to reach voters effectively. Jungherr (2016) in "Analyzing Political Communication with Digital Trace Data" provides methodologies for studying political behavior on social media, which inform our data collection and analysis strategies.
+
+### Sentiment Analysis and Rhetorical Style in Tweets
+
+Understanding the sentiment and rhetorical style in tweets is crucial for analyzing political communication. Balahur et al. (2013) in "Sentiment Analysis in Twitter" examine various techniques for determining sentiment in short texts, which is pertinent to our evaluation metrics. Hart and Lind (2011) in "Political Rhetoric and its Discontents" showcase the importance of rhetorical analysis in political discourse, guiding our approach to replicating the distinctive styles of different political parties.
+
+### Applications of GPT-2 in Text Generation
+
+The practical applications of GPT-2 for text generation have been demonstrated in various domains. Brown et al. (2020) in "Language Models are Few-Shot Learners" show the model's ability to generate human-like text with minimal fine-tuning, validating our choice of GPT-2 for generating political tweets. Furthermore, Wu et al. (2019) in "GPT-2 as a Knowledge Engine" illustrate the model's capacity to generate relevant and coherent content, supporting our deployment strategy.
+
+### Implications of AI in Political Communication
+
+The ethical implications of using AI in political communication are a significant concern. Floridi et al. (2018) in "AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations" discuss the ethical challenges posed by AI, such as bias and misinformation, which are relevant to our study. Additionally, the "Automating Society Report" by AlgorithmWatch (2020) provides insights into the societal impacts of automated decision-making systems, including those used in political communication.
+
+By integrating insights from these studies, our project aims to build upon the existing body of knowledge and contribute to the field of political communication analysis through advanced NLP techniques.
 
 ## 3 Methodology
 
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 The model was fine-tuned with the following parameters:
 
-#TODO: Heraussuchen
+Model: GPT-2
+Training epochs: 3
+Batch size: 6
+Learning rate: 2e-4
+Warmup steps: 100
+Epsilon: 1e-8
+Evaluation strategy: Evaluate after each epoch
+Save strategy: Save the model after each epoch
+Seed: 38
 
 #### 3.3.3 Training
 
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 ## 6 Conclusion
 
 In this project, we analyzed the communication strategies of German political parties on Twitter using a fine-tuned GPT-2 model. Our results demonstrate the potential of NLP techniques in political communication analysis. Future research could build on these findings to explore more advanced applications and address the ethical implications of AI in social media.
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