README.md

metadata

configs:
  - config_name: fake_news
    data_files:
      - split: train
        path: fake_news/train.jsonl
      - split: test
        path: fake_news/test.jsonl
      - split: validation
        path: fake_news/validation.jsonl
  - config_name: job_scams
    data_files:
      - split: train
        path: job_scams/train.jsonl
      - split: test
        path: job_scams/test.jsonl
      - split: validation
        path: job_scams/validation.jsonl
  - config_name: phishing
    data_files:
      - split: train
        path: phishing/train.jsonl
      - split: test
        path: phishing/test.jsonl
      - split: validation
        path: phishing/validation.jsonl
  - config_name: political_statements
    data_files:
      - split: train
        path: political_statements/train.jsonl
      - split: test
        path: political_statements/test.jsonl
      - split: validation
        path: political_statements/validation.jsonl
  - config_name: product_reviews
    data_files:
      - split: train
        path: product_reviews/train.jsonl
      - split: test
        path: product_reviews/test.jsonl
      - split: validation
        path: product_reviews/validation.jsonl
  - config_name: sms
    data_files:
      - split: train
        path: sms/train.jsonl
      - split: test
        path: sms/test.jsonl
      - split: validation
        path: sms/validation.jsonl
  - config_name: twitter_rumours
    data_files:
      - split: train
        path: twitter_rumours/train.jsonl
      - split: test
        path: twitter_rumours/test.jsonl
      - split: validation
        path: twitter_rumours/validation.jsonl

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.

Authors

ReDAS Lab, University of Houston, 2023. See https://www2.cs.uh.edu/~rmverma/ for contact information.

Contents

Each task is (or has been converted to) a binary classification problem.

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.

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.

The contents of these files are also appeneded to the end of this document.

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

This dataset is a successor of the GDD dataset.

Noteable changes from GDD are:

1. Addition of SMS and Twitter Rumours datasets, making it 7 deception datasets from different domains in total

2. Re-labeling of Political Statements dataset using a scheme that better fits with prior published work that used it and is stricter in terms of non-deceptive statement criteria of acceptance (see the README file specific to the dataset within its directory)

3. Job Scams datasets' labeles were previously inverted, with ~13500 labeled as deceptive (is_deceptive=True) and ~600 as non-deceptive. This could lead to potential issues with using metrics such as f1-score, which for binary classification is computed for the class considered to be positive. This issue has been addressed and the deceptive texts are labeled as 1 (e.g. positive or True) while non-deceptive as 0 (e.g. negative or False)

4. All datasets have been processed using Cleanlab, with problematic samples maually examined and issues addressed if needed. See the details in each of the individual datasets README files.

5. All datasets now come in 2 formats: the entirety of the data in a single jsonl file located in the data/ subdirectory of each dataset, and a standard train-test-valid stratified split of 80-10-10, in 3 separate jsonl files.

6. All datasets have two fields: "text" (string) and "label" (integer, 0 or 1 - 0 indicates that the text is non-deceptive, 1 means it is deceptive)

7. '\n' has been normalized to ' ' for all datasets as it causes issues with BERT's tokenizer in some cases (and to be in line with general whitespace normalization). Broken unicode has been fixed. Whitespace, quotations, and bullet points were normalized. Text is limited to 1,000,000 characters in length and guaranteed to be non-empty. Duplicates within the the same dataset (even in text only) were dropped, so were empty and None values.

Statistics

The entire dataset contains 95854 samples, 37282 are deceptive and 58572 non-deceptive.

The split of data within the individual datasets/domains:

fake_news total: 20456 deceptive: 8832 non-deceptive: 11624

job_scams total: 14295 deceptive: 599 non-deceptive: 13696

phishing total: 15272 deceptive: 6074 non-deceptive: 9198

political_statements total: 12497 deceptive: 8042 non-deceptive: 4455

product_reviews total: 20971 deceptive: 10492 non-deceptive: 10479

sms total: 6574 deceptive: 1274 non-deceptive: 5300

twitter_rumours total: 5789 deceptive: 1969 non-deceptive: 3820

License

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

If you found this dataset useful in your research, please consider citing it as:

TODO: ADD our paper reference

Original GDD paper:

@inproceedings{10.1145/3508398.3519358, author = {Zeng, Victor and Liu, Xuting and Verma, Rakesh M.}, title = {Does Deception Leave a Content Independent Stylistic Trace?}, year = {2022}, isbn = {9781450392204}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3508398.3519358}, doi = {10.1145/3508398.3519358}, abstract = {A recent survey claims that there are em no general linguistic cues for deception. Since Internet societies are plagued with deceptive attacks such as phishing and fake news, this claim means that we must build individual datasets and detectors for each kind of attack. It also implies that when a new scam (e.g., Covid) arrives, we must start the whole process of data collection, annotation, and model building from scratch. In this paper, we put this claim to the test by building a quality domain-independent deception dataset and investigating whether a model can perform well on more than one form of deception.}, booktitle = {Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy}, pages = {349–351}, numpages = {3}, keywords = {domain-independent deception detection, dataset quality/cleaning}, location = {Baltimore, MD, USA}, 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

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.

Preprocessing

Whitespace, quotes, bulletpoints, unicode is normalized.

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

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

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.

Preprocessing

Whitespace, quotes, bulletpoints, unicode is normalized.

Data

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.

Phishing

This dataset consists of various phishing attacks as well as benign emails collected from real users.

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.

Preprocessing

Whitespace, quotes, bulletpoints, unicode is normalized.

Data

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.

Political Statements

Political Statements dataset was created from the LIAR corpus.

Labeling

The primary difference is the change in the re-labeling scheme when converting the task from multiclass to binary.

Old scheme

We use the claim field as the text and map labels “pants-fire,” “false,” “barely-true,” to deceptive and “half-true,” “mostly-true,” and “true” to non-deceptive, resulting in 5,669 deceptive and 7,167 truthful statements.

New scheme

Following

Upadhayay, B., Behzadan, V.: "Sentimental liar: Extended corpus and deep learning models for fake claim classification" (2020)

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.

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.

Preprocessing

Whitespace, quotes, bulletpoints, unicode is normalized.

Data

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.

Product Reviews

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

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.

Preprocessing

Whitespace, quotes, bulletpoints, unicode is normalized.

Data

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.

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.

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.

Preprocessing

Whitespace, quotes, bulletpoints, unicode is normalized.

Data

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.

Rumors dataset

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.

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.

Preprocessing

Whitespace, quotes, bulletpoints, unicode is normalized.

Data

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.