Update README.md

README.md

@@ -60,47 +60,82 @@ configs:
 
 # GDDs-2.0
 
-The Generalized Deception Dataset version 2.0 is a labeled corpus containing over 95000 samples of deceptive and truthful texts from a number of independent domains and tasks. 
+The Generalized Deception Dataset version 2.0 is a labeled corpus containing over 95000 samples of
+deceptive and truthful texts from a number of independent domains and tasks. 
 
 ## Authors
 
 ReDAS Lab, University of Houston, 2023. See https://www2.cs.uh.edu/~rmverma/ for contact information.
 
+## Domains/Sub-Tasks
+
+There are 7 independent domains in the dataset. Each one was constructed from one or more datasets.
+Some tasks were not initially binary and had to be relabeled. The inputs vary wildly both stylistically
+and syntactically, as well as in terms of the goal of deception (or absence of thereof) being performed in the context of each
+dataset. The two uniting factors are: all seven datasets contain some fraction of texts that are meant
+to deceive the person reading them one way or another.
+
+1) Phishing (Email)
+
+2) Fake News (News Articles)
+
+3) Political Statements (Claims and statements by politicians and other entities, made from LIAR)
+
+4) Product Reviews (Amazon product reviews)
+
+5) Job Scams (Job postings)
+
+6) SMS (Phishing attacks via sms) (combination of SMS Spam from UCI repository and SMS Phishing datasets)
+
+7) Twitter Rumours (Collection of rumours from PHEME dataset, covers multiple topics)
+
 ## Contents
 
 Each task is (or has been converted to) a binary classification problem.
 
+### Structure
+
+The directory layout of gdds is like so:
+
+``
+gdds
+  fake_news/
+    train.jsonl
+    test.jsonl
+    validation.jsonl
+    README.md
+  ...
+  ...
+  ...
+  sms/
+    train.jsonl
+    test.jsonl
+    validation.jsonl
+    README.md
+  README.md
+  LICENSE.txt
+``
+
 Each subdirectory/config contains the domain/individual dataset.
 `train.jsonl`, `test.jsonl`, and `valid.jsonl` contain train, test, and validation sets, respectively.
 
 The splits are train=80%, test=10%, valid=10%
 The sampling process was random with seed=42, and stratified with respect to `y` (label) for each domain.
 
+### Fields
+
 Each `jsonl` file has two fields (columns): `text` and `label`
 
 `label` answers the question whether text is deceptive: `1` means yes, it is deceptive, `0` means no, the text is not deceptive (it is truthful).
 
 `text` is guaranteed to be valid unicode, less than 1 million characters, and contains no empty entries or non-values.
 
-Each dataset's directory contains a `README.md` file with additional details about it: sources, cleaning and preprocessing applied, and other information. 
+### Documentation
 
-The contents of these files are also appeneded to the end of this document.
+Primary documentation is this README file. Each dataset's directory contains a `README.md` file with additional details. 
+The contents of these files are also included at the end of this document in the Appendix. 
+LICENSE.txt contains the MIT license this dataset is distributed under.
 
-## List of Domains/Datasets
-
-1) Phishing (Email)
-
-2) Fake News (News Articles)
-
-3) Political Statements (Claims and statements by politicians and other entities, made from LIAR)
-
-4) Product Reviews (Amazon product reviews)
-
-5) Job Scams (Job postings)
-
-6) SMS (Phishing attacks via sms) (combination of SMS Spam from UCI repository and SMS Phishing datasets)
-
-7) Twitter Rumours (Collection of rumours from PHEME dataset, covers multiple topics)
 
 ## Changes and Additions
 
@@ -142,17 +177,19 @@ sms total: 6574 deceptive: 1274 non-deceptive: 5300
 
 twitter_rumours total: 5789 deceptive: 1969 non-deceptive: 3820
 
+## LICENSE
 
-## License
+This dataset is published under the MIT license and can be used and modified by anyone free of charge. 
+See LICENSE.txt file for details. 
 
-This dataset is published under the MIT license and can be used and modified by anyone free of charge. See LICENSE file for details.
-
-## Citing
+## CITING
 
 If you found this dataset useful in your research, please consider citing it as:
 
 TODO: ADD our paper reference
 
+## REFERENCES 
+
 Original GDD paper:
 
 @inproceedings{10.1145/3508398.3519358,
@@ -174,6 +211,8 @@ series = {CODASPY '22}
 }
 
 
+
+
 ## APPENDIX: Dataset and Domain Details
 
 This section describes each domain/dataset in greater detail.