Update README describing each of the files in the repository.

README.md

@@ -11,4 +11,32 @@ tags:
 - llm-in-the-loop
 - llm-as-annotator
 pretty_name: WSF-ARG+
----
+---
+
+# WSF-ARG+
+
+This dataset card describes the **WSF-ARG+** dataset presented in the paper *"When Hate Meets Facts: LLMs-in-the-Loop for Checkworthiness Detection in Hate Speech,"* accepted to the main conference of EMNLP 2026.
+
+We organized the data into two tabular formats:
+
+**Message-level format**: In this format, each row corresponds to an entire message, which can be either hate speech or non-hate speech. Each message is divided into one or more claims. If the message is argumentative, its premises and conclusion are explicitly annotated; otherwise, it is stored as one or more claims. Consequently, each row represents a full message, with columns corresponding to the individual claims. Additional information includes:
+- The overall hatefulness of the message
+- Whether the message is argumentative
+- Annotations per claim (hatefulness and check-worthiness labels)
+- Existing annotations from WSF-ARG
+
+The file `wsf_arg_plus_per_message.csv` contains the aggreggated labels through majority voting. It has both gold (obtained through LLM-in-the-loop) and platinum (obtained through full human annotation) check-worthiness annotations. `wsf_arg_plus_per_message_gold_disagg.csv` and `wsf_arg_plus_per_message_platinum_disagg.csv` contain the annotations disaggregated given by all annotators for both gold and platinum. We also indicate which claims required to be judged. We also released `wsf_arg_plus_per_message_all_llms.csv` that contains all the predictions carried out in the check-worthiness detection task for all configurations (per LLM and prompt). We release the three runs per configuration and the majority voting label. Consider that we have 12 LLMs tested, and 2 prompt strategies being in total 12x2 = 24 configurations. Each configuration is run three times meaning a total of 24x3 = 96 runs. Therefore, the .csv file contains 96 columns with each of these runs and 12 other columns for the majority voting label.
+
+**Claim-level format**: This format transforms the message-level table into one where each row represents a single claim. Each claim may originate from a full message. For each claim, we record:
+- The message it comes from
+- Whether the claim itself is hateful
+- Whether it comes from a hate speech or non-hate speech message
+- Check-worthiness annotations provided by the LLM-in-the-loop
+- Annotations from all human annotators
+- Annotations from each model included in our experimental study
+
+Similarly to the message-level format, we have:
+
+`wsf_arg_plus_per_claim.csv` as the general dataset organized in claim-level format.
+`wsf_arg_plus_per_claim_gold_disagg.csv` and `wsf_arg_plus_per_claim_platinum_disagg.csv` for the disaggregated gold and platinum labels.
+`wsf_arg_plus_per_claim_all_llms.csv` that contains the disaggregated and aggregated annotations carried out by all our 24 configuration (12 LLMs and 2 prompting strategies).