Update README.md

README.md

@@ -18,6 +18,10 @@ This repository contains parsed datasets used in the RuleFollower project.
 - **Article Bias Prediction**
 - **Politeness Wiki**
 - **Politeness Stack**
+- **Targeted vs. Untargeted Violence (Gohdes 2020DV)**
+- **Scientific Paper Criticism (Peng 2022)**
+- **BFRS Political Violence**
+- **Manifesto Temporal Orientation (Muller 2021DV)**
 
 Each folder contains a `data.csv` file with the parsed annotations. Each dataset is capped at 5k samples (full dataset used if <5k). 
 
@@ -158,4 +162,44 @@ Each data file includes standardized columns:
 - **Source**: [Danescu-Niculescu-Mizil et al. (2013)](https://aclanthology.org/P13-1025.pdf)
 - **Dataset**: [Data](https://convokit.cornell.edu/documentation/stack_politeness.html)
 - **Annotation goal**: Classify the level of politeness of the text. Labels: `1`(polite), `0`(neutral), `-1`(impolite).
-- **Ground truth**: Provided
+- **Ground truth**: Provided
+
+---
+
+### Gohdes 2020DV
+- **Text content**: Short reports on individual killings committed by the Syrian regime and pro-government forces between June 2013 and April 2015
+- **Source**: [Gohdes replication data](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KG2APR)
+- **Dataset**: [Huggingface](https://huggingface.co/datasets/RuleFollower/rulefollower_parsed/tree/main/gohdes_2020dv)
+- **Annotation goal**: Classify each killing report as `targeted` or `untargeted`
+- **Ground truth**: Provided (targeted/untargeted label)
+- **Note**: Original two-column labels `cat_targeted` and `cat_untargeted` are converted to a single mutually exclusive label.
+
+---
+
+### Peng 2022
+- **Text content**: Short social media texts that link to or discuss scientific papers
+- **Source**: Peng et al. (2022)
+- **Dataset**: [Huggingface](https://huggingface.co/datasets/RuleFollower/rulefollower_parsed/tree/main/peng_2022)
+- **Annotation goal**: Classify whether the text is `critical` or `not critical` of the linked scientific paper
+- **Ground truth**: Provided (`CRITICAL` label)
+- **Note**: The original `CRITICAL` binary label is retained as the classification target.
+
+---
+
+### BFRS Political Violence
+- **Text content**: News stories from Pakistan describing political violence and related events
+- **Source**: Halterman and Keith codebook/data package
+- **Dataset**: [Huggingface](https://huggingface.co/datasets/RuleFollower/rulefollower_parsed/tree/main/bfrs_political_violence)
+- **Annotation goal**: Classify each story into one event type, such as `ASSASSINATION`, `TERRORISM`, `RIOT`, `DRONE_ASSASSINATION`, or `VIOLENT_POLITICAL_DEMONSTRATION`
+- **Ground truth**: Provided (single multiclass event label)
+- **Note**: A stratified 2,000-example subset is retained from the original train/dev/test files.
+
+---
+
+### Muller 2021DV
+- **Text content**: Sentences from political party manifestos
+- **Source**: [Müller replication data](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/7NP2XH)
+- **Dataset**: [Huggingface](https://huggingface.co/datasets/RuleFollower/rulefollower_parsed/tree/main/muller_2021dv)
+- **Annotation goal**: Classify each sentence’s temporal orientation as `future`, `past`, or `present`
+- **Ground truth**: Provided (temporal orientation label)
+- **Note**: Original one-hot labels `FUTURE`, `PAST`, and `PRESENT` are converted to a single label: `0 = future`, `1 = past`, `2 = present`.