Update README.md

README.md

@@ -69,7 +69,7 @@ print(predictions)
 
 ```
 
-**Training Details**
+**Training Details**  
 **Training Data**
 
 The model was trained on a dataset of manually annotated Telegram posts from radical and extremist groups. The dataset includes posts related to six crisis-specific topics: COVID-19, Conspiracy Narratives, Russian Invasion of Ukraine (RioU), Energy Crisis, Inflation, and Migration.
@@ -91,4 +91,17 @@ Results
 
 Summary
 
-The model demonstrated robust performance with balanced precision and recall metrics above 0.76.
+The model demonstrated robust performance with balanced precision and recall metrics above 0.76.
+
+
+**BibTex**  
+```
+@article{Greipl2024,
+  title={"You are doomed!" Crisis-specific and Dynamic Use of Fear Speech in Protest and Extremist Radical Social Movements},
+  author={Simon Greipl, Julian Hohner, Heidi Schulze, Patrick Schwabl, Diana Rieger},
+  journal={Journal of Quantitative Description: Digital Media},
+  volume={4},
+  year={2024},
+  doi={10.51685/jqd.2024.icwsm.8}
+}
+```