yaml fix

README.md

@@ -3,59 +3,66 @@ configs:
 - config_name: fake_news
   data_files:
   - split: train
-    path: "fake_news/train.jsonl"
+    path: fake_news/train.jsonl
   - split: test
-    path: "fake_news/test.jsonl"
+    path: fake_news/test.jsonl
   - split: validation
-    path: "fake_news/validation.jsonl"
+    path: fake_news/validation.jsonl
 - config_name: job_scams
   data_files:
   - split: train
-    path: "job_scams/train.jsonl"
+    path: job_scams/train.jsonl
   - split: test
-    path: "job_scams/test.jsonl"
+    path: job_scams/test.jsonl
   - split: validation
-    path: "job_scams/validation.jsonl"
+    path: job_scams/validation.jsonl
 - config_name: phishing
   data_files:
   - split: train
-    path: "phishing/train.jsonl"
+    path: phishing/train.jsonl
   - split: test
-    path: "phishing/test.jsonl"
+    path: phishing/test.jsonl
   - split: validation
-    path: "phishing/validation.jsonl"
+    path: phishing/validation.jsonl
 - config_name: political_statements
   data_files:
   - split: train
-    path: "political_statements/train.jsonl"
+    path: political_statements/train.jsonl
   - split: test
-    path: "political_statements/test.jsonl"
+    path: political_statements/test.jsonl
   - split: validation
-    path: "political_statements/validation.jsonl"
+    path: political_statements/validation.jsonl
 - config_name: product_reviews
   data_files:
   - split: train
-    path: "product_reviews/train.jsonl"
+    path: product_reviews/train.jsonl
   - split: test
-    path: "product_reviews/test.jsonl"
+    path: product_reviews/test.jsonl
   - split: validation
-    path: "product_reviews/validation.jsonl"
+    path: product_reviews/validation.jsonl
 - config_name: sms
   data_files:
   - split: train
-    path: "sms/train.jsonl"
+    path: sms/train.jsonl
   - split: test
-    path: "sms/test.jsonl"
+    path: sms/test.jsonl
   - split: validation
-    path: "sms/validation.jsonl"
+    path: sms/validation.jsonl
 - config_name: twitter_rumours
   data_files:
   - split: train
-    path: "twitter_rumours/train.jsonl"
+    path: twitter_rumours/train.jsonl
   - split: test
-    path: "twitter_rumours/test.jsonl"
+    path: twitter_rumours/test.jsonl
   - split: validation
-    path: "twitter_rumours/validation.jsonl"
+    path: twitter_rumours/validation.jsonl
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+size_categories:
+- 10K<n<100K
 ---
 
 # GDDs-2.0
@@ -227,7 +234,6 @@ location = {Baltimore, MD, USA},
 series = {CODASPY '22}
 }
 
-
 ## APPENDIX: Dataset and Domain Details
 
 This section describes each domain/dataset in greater detail.
@@ -240,13 +246,6 @@ often reputable sources, such as "[claim] (Reuters)". It contains 35,028 real ne
 We found a number of out-of-domain statements that are clearly not relevant to news, such as "Cool", which is a potential
 problem for transfer learning as well as classification. 
 
-
-#### Data
-
-The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
-
-There are 20456 samples in the dataset, contained in `phishing.jsonl`. For reproduceability, the data is also split into training, test, 
-and validation sets in 80/10/10 ratio. They are named `train.jsonl`, `test.jsonl`, `valid.jsonl`. The sampling process was stratified. 
 The training set contains 16364 samples, the validation and the test sets have 2064 and 2064 samles, respectively.
 
 ### JOB SCAMS
@@ -260,18 +259,13 @@ The original Job Labels dataset had the labels inverted when released. The probl
 
 #### Cleaning 
 
-HTML tags were removed. 
-
-#### Data
-
-T**With just under 600 deceptive texts, this dataset is heavily imbalanced.**
+It was cleaned by removing all HTML tags, empty descriptions, and duplicates. 
+The final dataset is heavily imbalanced, with 599 deceptive and 13696 non-deceptive samples out of the 14295 total.
 
 ### PHISHING
 
 This dataset consists of various phishing attacks as well as benign emails collected from real users.
 
-#### Data
-
 The training set contains 12217 samples, the validation and the test sets have 1527 and 1528 samples, respectively.
 
 ### POLITICAL STATEMENTS
@@ -298,7 +292,8 @@ Following
 
 and
 
-*Shahriar, Sadat, Arjun Mukherjee, and Omprakash Gnawali. "Deception Detection with Feature-Augmentation by Soft Domain Transfer." International Conference on Social Informatics. Cham: Springer International Publishing, 2022.*
+*Shahriar, Sadat, Arjun Mukherjee, and Omprakash Gnawali. "Deception Detection with Feature-Augmentation by Soft Domain Transfer." 
+International Conference on Social Informatics. Cham: Springer International Publishing, 2022.*
 
 we map the labels  map labels “pants-fire,” “false,”
 “barely-true,” **and “half-true,”** to deceptive; the labels "mostly-true" and "true" are mapped to non-deceptive. 
@@ -311,8 +306,6 @@ The dataset has been cleaned using cleanlab with visual inspection of problems f
 "On inflation", were removed. Text with large number of errors induced by a parser were also removed.
 Statements in language other than English (namely, Spanish) were also removed. 
 
-#### Data
-
 The training set contains 9997 samples, the validation and the test sets have 1250 samples each in them.
 
 ### PRODUCT REVIEWS
@@ -320,7 +313,13 @@ The training set contains 9997 samples, the validation and the test sets have 12
 We post-process and split Product Reviews dataset to ensure uniformity with Political Statements 2.0 and Twitter Rumours 
 as they all go into form GDDS-2.0
 
-#### Data
+The dataset is produced from English Amazon Reviews labeled as either real or fake, relabeled as deceptive and non-deceptive respectively. 
+The reviews cover a variety of products with no particular product dominating the dataset. Although the dataset authors filtered out 
+non-English reviews, through outlier detection we found that the dataset still contains reviews in Spanish and other languages. 
+Problematic label detection shows that over 6713 samples are potentially mislabeled; since this technique is error-prone,
+we visually examine 67 reviews that are found to be the largest potential sources of error (the top percentile) and confirm that
+most of them appear to be mislabeled. The final dataset of 20,971 reviews is evenly balanced with 10,492 deceptive and 10,479 
+non-deceptive samples.
 
 The training set contains 16776 samples, the validation and the test sets have 2097 and 2098 samples, respectively.
 
@@ -331,8 +330,6 @@ which contained 5,574 and 5,971 real English SMS messages, respectively. As thes
 the final dataset is made up of 6574 texts released by a private UK-based wireless operator; 1274 of them are deceptive, 
 and the remaining 5300 are not.
 
-#### Data
-
 The training set contains 5259 samples, the validation and the test sets have 657 and 658 samples, 
 respectively.
 
@@ -345,21 +342,4 @@ https://figshare.com/articles/dataset/PHEME_dataset_of_rumours_and_non-rumours/4
 was used in creation of this dataset. We took source tweets only, and ignored replies to them. 
 We used source tweet's label as being a rumour or non-rumour to label it as deceptive or non-deceptive.
 
-#### Data
-
-The training set contains 4631 samples, the validation and the test sets have 579 samples each.
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+The training set contains 4631 samples, the validation and the test sets have 579 samples each.