SWAROG dataset

Following repository contains dataset acquired and labeled during the first phase of a SWAROG project, financed by the National Center for Research and Development, under grant INFOSTRATEG-I/0019/2021.

How to use me?

The usage is quite simple. To access the dataset you only need to get a gdown package, available on PyPI repository and to run a gather.sh script from this repository. The data is stored in HDF5 format, according to its pandas serialization procedure, so for easiest processing it is also suggested to install pandas and pytables packages, as indicated in requirements.txt file.

pip install -r requirements.txt
./gather.sh

Dataset will be unpacked to the data directory, as two .h5 files:

• labeled_data_embed.h5 – containing the labeled subset of acquired data with established embedding.
• nolabel_data_embed.h5 – containing all the acquired data with the same approach for feature extraction.

To encourage users to use a proper experimental protocol, data is made available without imposed division to cross-validation folds. If all the packages from requirements.txt file are properly installed, all what is needed to load data into the experimental script is to read the files with pandas read_hdf() function with its defalult parameters, as in the example.py file:

import pandas as pd

# Load labeled data
labeled_data = pd.read_hdf('data/labeled_data_embed.h5')

# Load nolabel data
nolabel_data = pd.read_hdf('data/nolabel_data_embed.h5')

Data structure

The dataframes of SWAROG-dataset are organized with the following manner:

Key	Description	dtype
url	SHA-1 sum of URL address to the analyzed article.	object
download_timestamp	Epoch time of the moment of acquisition.	object
publication_timestamp	Identified epoch time of the moment of article publication.	object
Poll_id	Identifier of a labeling poll. [only for labeled data]	int64
P{5:17}	12 consecutive fields of binary labeling structure. [only for labeled data]	int64
T{0:383}	384 consecutive fields of established embedding for article's titles for the moment of acquisition.	float32
E{0:383}	384 consecutive fields of established embedding for article's body for the moment of acquisition.	float32

Labels description

SWAROG's approach for annotation differs from standard labeling protocol, explicitly assigning each data object into one of the fake news categories, proposing extended definition build around 13 questions connected with this phenomenon.

Key	Question
P5	Is there at least one reliable source that confirms all the information contained in the content?
P6	Is most of the information provided confirmed by reliable sources?
P7	Is none of the information confirmed by reliable sources?
P8	Does the statement refer to current (at the time of creation of the statement) data?
P9	Is additional information required to correctly understand the content?
P10	Does the statement contain any inaccuracies?
P11	Does the statement contain fragments taken out of context?
P12	Does the author of the statement use cherry picking?
P13	Is the author of the statement trying to mislead the reader?
P14	Is the content satirical?
P15	Does the author admit that the facts presented are made up?
P16	Does the statement contain political promises?
P17	Does the statement contain religious content?

Citation policy

If you will use this dataset in your research, please use the following citation

@inproceedings{kozik2023swarog,
  title={SWAROG Project Approach to Fake News Detection Problem},
  author={Kozik, Rafa{\l} and Komorniczak, Joanna and Ksieniewicz, Pawe{\l} and Pawlicka, Aleksandra and Pawlicki, Marek and Chora{\'s}, Micha{\l}},
  booktitle={Computational Intelligence in Security for Information Systems Conference},
  pages={79--88},
  year={2023},
  organization={Springer}
}