Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label x_fake_profile_detection@0ce4467b29d9b28f99ce436df00e8aea3cbbe3a9
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
example_data = self.str2int(example_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
output = [self._strval2int(value) for value in values]
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label x_fake_profile_detection@0ce4467b29d9b28f99ce436df00e8aea3cbbe3a9Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset: Detecting Fake Accounts on Social Media Portals—The X Portal Case Study
This dataset was created as part of the study focused on detecting fake accounts on the X Portal (formerly known as Twitter). The primary aim of the study was to classify social media accounts using image data and machine learning techniques, offering a novel approach to identifying fake accounts. The dataset includes generated accounts, which were used to train and test a Convolutional Neural Network (CNN) model.
Dataset Information
- Total Samples: 15,000 accounts
- Data Types: Images of profile pictures based on X portal accounts.
- Classes:
- BOT: Accounts operated by automation, generally created to spread spam or disinformation.
- CYBORG: Accounts that involve a combination of both bot and human activity.
- REAL: Real human accounts with legitimate, human-like behaviors.
- VERIFIED: Authentic and verified accounts typically belonging to well-known individuals or organizations.
Dataset Splits
- Training Set: 10,000 samples
- Validation Set: 1,000 samples
- Test Set: 4,000 samples
Each split contains a balanced number of samples across the four available classes (BOT, CYBORG, REAL, VERIFIED), ensuring an even distribution for training and evaluation.
Citation
If you use this dataset in your research, please cite the following paper:
@Article{electronics13132542,
AUTHOR = {Dracewicz, Weronika and Sepczuk, Mariusz},
TITLE = {Detecting Fake Accounts on Social Media Portals—The X Portal Case Study},
JOURNAL = {Electronics},
VOLUME = {13},
YEAR = {2024},
NUMBER = {13},
ARTICLE-NUMBER = {2542},
URL = {https://www.mdpi.com/2079-9292/13/13/2542},
ISSN = {2079-9292},
ABSTRACT = {Today, social media are an integral part of everyone’s life. In addition to their traditional uses of creating and maintaining relationships, they are also used to exchange views and all kinds of content. With the development of these media, they have become the target of various attacks. In particular, the existence of fake accounts on social networks can lead to many types of abuse, such as phishing or disinformation, which is a big challenge nowadays. In this work, we present a solution for detecting fake accounts on the X portal (formerly Twitter). The main goal behind the developed solution was to use images of X portal accounts and perform image classification using machine learning. As a result, it was possible to detect real and fake accounts and indicate the type of a particular account. The created solution was trained and tested on an adequately prepared dataset containing 15,000 generated accounts and real X portal accounts. The CNN model performing with accuracy above 92% and manual test results allow us to conclude that the proposed solution can be used to detect false accounts on the X portal.},
DOI = {10.3390/electronics13132542}
}
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