updated dataset descriptions

README.md

@@ -219,19 +219,23 @@ series = {CODASPY '22}
 }
 
 
-
-
 ## APPENDIX: Dataset and Domain Details
 
 This section describes each domain/dataset in greater detail.
 
 ### Fake News
 
-We post-process and split Fake News dataset to ensure uniformity with Political Statements 2.0 and Twitter Rumours as they all go into form GDDS-2.0
+Fake News used WELFake as a basis. The WELFake dataset combines 72,134 news articles from four pre-existing datasets 
+(Kaggle, McIntire, Reuters, and BuzzFeed Political). The dataset was cleaned of data leaks in the form of citations of 
+often reputable sources, such as "[claim] (Reuters)". It contains 35,028 real news articles and 37,106 fake news articles. 
+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. After cleaning and processing, the Fake News dataset consists of
+20456 articles; 8832 are deceptive, and 11624 are not.
 
 #### Cleaning 
 
-Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were all removed.
+Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, 
+entries of length less than 2 characters or exceeding 1000000 characters were all removed.
 
 #### Preprocessing
 
@@ -241,15 +245,25 @@ Whitespace, quotes, bulletpoints, unicode is normalized.
 
 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.
+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
 
-We post-process and split Job Scams dataset to ensure uniformity with Political Statements 2.0 and Twitter Rumours as they all go into form GDDS-2.0
+The Employment Scam Aegean Dataset, henceforth referred to as the Job Scams dataset, consisted of 17,880 human-annotated job listings of
+job descriptions labeled as fraudulent or not.
+
+#### Relabeling
+
+The original Job Labels dataset had the labels inverted when released. The problem is now fixed, the labels are correct.
 
 #### Cleaning 
 
-Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were all removed.
+It was cleaned by removing all HTML tags, empty descriptions, and duplicates. The dataset has been cleaned using Cleanlab.
+Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, entries of length less
+than 2 characters or exceeding 1000000 characters were all removed.
+The final dataset is heavily imbalanced,  with 599 deceptive and 13696 non-deceptive samples out of the 14295 total.
 
 #### Preprocessing
 
@@ -259,7 +273,10 @@ Whitespace, quotes, bulletpoints, unicode is normalized.
 
 The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
 
-There are 14295 samples in the dataset, contained in `job_scams.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 11436 samples, the validation and the test sets have 1429 and 1430 samles, respectively.
+There are 14295 samples in the dataset, contained in `job_scams.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 11436 samples, the validation and the test sets have 1429 and 1430 samles, respectively.
 
 ### Phishing
 
@@ -267,7 +284,8 @@ This dataset consists of various phishing attacks as well as benign emails colle
 
 #### Cleaning 
 
-Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were all removed.
+Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, 
+duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were all removed.
 
 #### Preprocessing
 
@@ -277,11 +295,15 @@ Whitespace, quotes, bulletpoints, unicode is normalized.
 
 The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
 
-There are 15272 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 12217 samples, the validation and the test sets have 1527 and 1528 samles, respectively.
+There are 15272 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 12217 samples, the validation and the test sets have 1527 and 1528 samles, respectively.
 
 ### Political Statements
 
-Political Statements dataset was created from the LIAR corpus.
+This corpus was created from the Liar dataset which consists of political statements made by US speakers assigned
+a fine-grain truthfulness label by PolitiFact.
 
 #### Labeling
 
@@ -305,11 +327,16 @@ 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.*
 
 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. The statements that are only half-true are now considered to be deceptive, making the criterion for statement being non-deceptive stricter -- now 2 out of 6 labels map to non-deceptive and 4 map to deceptive.
+“barely-true,” **and “half-true,”** to deceptive; the labels "mostly-true" and "true" are mapped to non-deceptive. 
+The statements that are only half-true are now considered to be deceptive, making the criterion for statement being non-deceptive stricter: 
+now 2 out of 6 labels map to non-deceptive and 4 map to deceptive.
 
 #### Cleaning
 
-The dataset has been cleaned using cleanlab with visual inspection of problems found. Partial sentences, such as "On Iran nuclear deal", "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. Sequences with unicode errors, containing less than one characters or over 1 million characters were removed. 
+The dataset has been cleaned using cleanlab with visual inspection of problems found. Partial sentences, such as "On Iran nuclear deal", 
+"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. 
+Sequences with unicode errors, containing less than one characters or over 1 million characters were removed. 
 
 #### Preprocessing
 
@@ -319,7 +346,10 @@ Whitespace, quotes, bulletpoints, unicode is normalized.
 
 The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
 
-There are 12497 samples in the dataset, contained in `political_statements.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 9997 samples, the validation and the test sets have 1250 samles each in them.
+There are 12497 samples in the dataset, contained in `political_statements.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 9997 samples, the validation and the test sets have 1250 samles each in them.
 
 ### Product Reviews
 
@@ -337,15 +367,22 @@ Whitespace, quotes, bulletpoints, unicode is normalized.
 
 The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
 
-There are 20971 samples in the dataset, contained in `product_reviews.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 16776 samples, the validation and the test sets have 2097 and 2098 samles, respectively.
+There are 20971 samples in the dataset, contained in `product_reviews.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 16776 samples, the validation and the test sets have 2097 and 2098 samles, respectively.
 
 ### SMS
 
-This dataset was created from the SMS Spam Collection and SMS Phishing Dataset for Machine Learning and Pattern Recognition, which contained 5,574 and 5,971 real English SMS messages, respectively. As these two datasets overlap, after de-duplication, 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.
+This dataset was created from the SMS Spam Collection and SMS Phishing Dataset for Machine Learning and Pattern Recognition, 
+which contained 5,574 and 5,971 real English SMS messages, respectively. As these two datasets overlap, after de-duplication, 
+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.
 
 #### Cleaning 
 
-Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were all removed.
+Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, 
+duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were all removed.
 
 #### Preprocessing
 
@@ -355,7 +392,10 @@ Whitespace, quotes, bulletpoints, unicode is normalized.
 
 The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
 
-There are 6574 samples in the dataset, contained in `sms.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 5259 samples, the validation and the test sets have 657 and 658 samles, respectively.
+There are 6574 samples in the dataset, contained in `sms.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 5259 samples, the validation and the test sets have 657 and 658 samles, 
+respectively.
 
 ### Rumors dataset
 
@@ -363,11 +403,13 @@ This deception dataset was created using PHEME dataset from
 
 https://figshare.com/articles/dataset/PHEME_dataset_of_rumours_and_non-rumours/4010619/1
 
-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.
+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.
 
 #### Cleaning
 
-The dataset has been cleaned using cleanlab with visual inspection of problems found. No issues were identified. Duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were removed.
+The dataset has been cleaned using cleanlab with visual inspection of problems found. No issues were identified. 
+Duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were removed.
 
 #### Preprocessing
 
@@ -377,7 +419,9 @@ Whitespace, quotes, bulletpoints, unicode is normalized.
 
 The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
 
-There are 5789 samples in the dataset, contained in `tweeter_rumours.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 4631 samples, the validation and the test sets have 579 samles each.
+There are 5789 samples in the dataset, contained in `tweeter_rumours.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 4631 samples, the validation and the test sets have 579 samles each.