Update README.md

README.md

@@ -31,15 +31,15 @@ configs:
 
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
+<!-- - [Dataset Description](#dataset-description) -->
   - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  <!-- - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) -->
   - [Languages](#languages)
 - [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
+  <!-- - [Data Instances](#data-instances) -->
   - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
+  <!-- - [Data Splits](#data-splits) -->
+<!-- - [Dataset Creation](#dataset-creation)
   - [Curation Rationale](#curation-rationale)
   - [Source Data](#source-data)
   - [Annotations](#annotations)
@@ -47,35 +47,76 @@ configs:
 - [Considerations for Using the Data](#considerations-for-using-the-data)
   - [Social Impact of Dataset](#social-impact-of-dataset)
   - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
+  - [Other Known Limitations](#other-known-limitations) -->
 - [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
+  <!-- - [Dataset Curators](#dataset-curators) -->
   - [Licensing Information](#licensing-information)
   - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
+  <!-- - [Contributions](#contributions) -->
 
-## Dataset Description
+<!-- ## Dataset Description -->
 
-- **Homepage:**
-- **Repository:**
-- **Paper:**
+<!-- - **Homepage:** -->
+<!-- - **Repository:** -->
+<!-- - **Paper:**
 - **Leaderboard:**
-- **Point of Contact:**
+- **Point of Contact:** -->
 
 ### Dataset Summary
 
-Reddit posts annotated for moral foundations
-
-### Supported Tasks and Leaderboards
+Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, environmental action, political engagement, and protest. 
+Various computational methods in Natural Language Processing (NLP) have been used to detect moral sentiment from textual data, but achieving strong performance 
+in such subjective tasks requires large, hand-annotated datasets. Previous corpora annotated for moral sentiment have proven valuable, and have generated new insights 
+both within NLP and across the social sciences, but have been limited to Twitter. To facilitate improving our understanding of the role of moral rhetoric, we present the
+Moral Foundations Reddit Corpus, a collection of 16,123 English Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three 
+trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality)
+based on the updated Moral Foundations Theory (MFT) framework.    
+
+
+
+Number of Reddit Posts Annotated by N Annotators for Each Subreddit:
+| subreddit           | 2  | 3    | 4 | 5 |
+|---------------------|----|------|---|---|
+| AmItheAsshole       | 3  | 1336 | 0 | 0 |
+| Conservative        | 22 | 1911 | 1 | 0 |
+| antiwork            | 31 | 1757 | 0 | 0 |
+| confession          | 6  | 1325 | 0 | 0 |
+| europe              | 15 | 2641 | 1 | 0 |
+| geopolitics         | 2  | 112  | 0 | 0 |
+| neoliberal          | 10 | 1660 | 3 | 2 |
+| nostalgia           | 6  | 1336 | 0 | 0 |
+| politics            | 17 | 1758 | 0 | 0 |
+| relationship_advice | 4  | 1350 | 0 | 0 |
+| worldnews           | 19 | 2555 | 3 | 0 |
+
+
+Table 2: Frequency of Reddit posts per Foundation Calculated Based on Annotators’ Majority Vote.
+| subreddit           | Care | Equality | Proportionality | Loyalty | Authority | Purity | Thin Morality | Non-Moral |
+|---------------------|------|----------|-----------------|---------|-----------|--------|---------------|-----------|
+| AmItheAsshole       | 371  | 138      | 79              | 78      | 98        | 49     | 389           | 676       |
+| Conservative        | 209  | 231      | 123             | 62      | 249       | 47     | 459           | 1362      |
+| antiwork            | 308  | 156      | 194             | 48      | 129       | 53     | 328           | 1264      |
+| confession          | 320  | 57       | 83              | 28      | 66        | 63     | 400           | 772       |
+| europe              | 106  | 194      | 88              | 137     | 330       | 34     | 590           | 2106      |
+| geopolitics         | 0    | 3        | 2               | 2       | 8         | 1      | 11            | 108       |
+| neoliberal          | 34   | 86       | 46              | 56      | 191       | 18     | 321           | 1422      |
+| nostalgia           | 29   | 13       | 13              | 10      | 15        | 24     | 130           | 1216      |
+| politics            | 153  | 132      | 91              | 60      | 249       | 37     | 409           | 1309      |
+| relationship_advice | 424  | 102      | 52              | 112     | 51        | 75     | 369           | 700       |
+| worldnews           | 154  | 264      | 80              | 105     | 345       | 60     | 626           | 1929      |
+| Overall             | 2108 | 1376     | 851             | 698     | 1731      | 461    | 4032          | 12864     |
+
+
+<!-- ### Supported Tasks and Leaderboards -->
 
 
 ### Languages
 
 English
 
-## Dataset Structure
+<!-- ## Dataset Structure
 
-### Data Instances
+### Data Instances -->
 
 
 
@@ -88,7 +129,7 @@ English
 - annotation
 - confidence
 
-### Data Splits
+<!-- ### Data Splits
 
 
 
@@ -101,10 +142,10 @@ English
 ### Source Data
 
 #### Initial Data Collection and Normalization
+ -->
 
 
-
-#### Who are the source language producers?
+<!-- #### Who are the source language producers?
 
 
 
@@ -126,16 +167,16 @@ English
 
 
 
-### Discussion of Biases
+### Discussion of Biases -->
 
 
-### Other Known Limitations
+<!-- ### Other Known Limitations
 
 
 ## Additional Information
 
 ### Dataset Curators
-
+ -->
 
 
 ### Licensing Information
@@ -145,14 +186,19 @@ cc-by-4.0
 ### Citation Information
 
 ```bibtex
-@misc{trager2022moral,
-    title={The Moral Foundations Reddit Corpus},
-    author={Jackson Trager and Alireza S. Ziabari and Aida Mostafazadeh Davani and Preni Golazazian and Farzan Karimi-Malekabadi and Ali Omrani and Zhihe Li and Brendan Kennedy and Nils Karl Reimer and Melissa Reyes and Kelsey Cheng and Mellow Wei and Christina Merrifield and Arta Khosravi and Evans Alvarez and Morteza Dehghani},
-    year={2022},
-    eprint={2208.05545},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
+@inproceedings{trager-etal-2026-moral,
+  title = {The Moral Foundations Reddit Corpus},
+  author = {Trager, Jackson P. and Ziabari, Alireza S. and Rahmati, Elnaz and Davani, Aida Mostafazadeh and Golazizian, Preni and Karimi-Malekabadi, Farzan and Omrani, Ali and Li, Zhihe and Kennedy, Brendan and Chochlakis, Georgios and Reimer, Nils Karl and Reyes, Melissa and Cheng, Kesley and Wei, Mellow and Merrifield, Christina and Khosravi, Arta and Alvarez, Evans and Dehghani, Morteza},
+  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)},
+  month = {May},
+  year = {2026},
+  pages = {6383--6407},
+  address = {Palma, Mallorca, Spain},
+  publisher = {European Language Resources Association (ELRA)},
+  editor = {Piperidis, Stelios and Bel, Núria and van den Heuvel, Henk and Ide, Nancy and Krek, Simon and Toral, Antonio},
+  doi = {10.63317/2b6xmbq3kphf},
+  abstract = {Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, environmental action, political engagement, and protest. Various computational methods in Natural Language Processing (NLP) have been used to detect moral sentiment from textual data, but achieving strong performance in such subjective tasks requires large, hand-annotated datasets. Previous corpora annotated for moral sentiment have proven valuable and have generated new insights both within NLP and across the social sciences, but have been limited to Twitter. To facilitate improving our understanding of the role of moral rhetoric, we present the Moral Foundations Reddit Corpus, a collection of 16,123 English Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality) based on the updated Moral Foundations Theory (MFT) framework. We evaluate baselines using large language models (Llama3-8B, Ministral-8B) in zero-shot, few-shot, and PEFT (Parameter-Efficient Fine-Tuning) settings, comparing their performance to fine-tuned encoder-only models like BERT (Bidirectional Encoder Representations from Transformers). The results show that LLMs continue to lag behind fine-tuned encoders on this subjective task, underscoring the ongoing need for human-annotated moral corpora for AI alignment evaluation}
 }
 ```
 
-### Contributions
+<!-- ### Contributions -->