Datasets:
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Error code: FeaturesError
Exception: ParserError
Message: Error tokenizing data. C error: Expected 21 fields in line 6, saw 24
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 246, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 4196, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2533, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2711, in iter
for key, pa_table in ex_iterable.iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2249, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
return self.get_chunk()
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
return self.read(nrows=size)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1923, in read
) = self._engine.read( # type: ignore[attr-defined]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
chunks = self._reader.read_low_memory(nrows)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 21 fields in line 6, saw 24Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Unified Spanish Misinformation and Satire Corpus (USMSC)
Dataset Description
This dataset is a comprehensive, deduplicated, and systematically structured corpus for domain-specific misinformation detection in Spanish social media text. It addresses the critical gap in Spanish-language resources by unifying multiple distinct datasets into a single, highly refined corpus.
Crucially, this dataset employs a three-class formulation (Fake, Real, Satire). Recent literature and empirical testing demonstrate that conflating satire with malicious fake news creates a fundamental construct-validity problem, as models risk learning source-specific humor or parody markers instead of robust misinformation cues. By isolating satire into its own distinct class, this corpus enables the training of highly calibrated models (such as BETO) capable of distinguishing creative parody from deceptive content.
Dataset Structure
The corpus consists of exactly 61,674 unique records, structured into three distinct target classes. This structure achieves a methodological balance where Deceptive + Satire records (49.8%) effectively balance Truthful records (50.2%).
| Class Label | Category | Count | Percentage | Description |
|---|---|---|---|---|
| 0 | FAKE |
21,746 | 35.3% | Deceptive, malicious, or misleading misinformation. |
| 1 | REAL |
30,943 | 50.2% | Factual, verified, and legitimate news articles. |
| 2 | SATIRE |
8,985 | 14.5% | Parody and ironic content. |
| - | Total | 61,674 | 100% | After rigorous deduplication and normalization. |
Corpus Construction & Methodological Evolution
The dataset was curated by unifying four publicly available Spanish-language sources:
- Posadas-Durán et al.: 572 records
- Acosta et al.: 598 records
- Tretiakov et al.: 2,000 records
- Blanco-Fernández et al.: 57,231 records
Preprocessing and Deduplication Protocol: The initial merge yielded 60,401 records showing a binary imbalance (41.3% Fake vs. 58.7% Real). To prevent source leakage and ensure robust generalization, the raw data underwent strict normalization (lowercasing, removal of non-alphanumeric characters, and whitespace standardization). Content hashing was then applied, resulting in the removal of exactly 7,712 near-duplicates.
This process yielded 52,689 unique factual/deceptive articles (21,746 Fake; 30,943 Real). Finally, 8,985 satirical records were appended as a distinct 3rd class, resolving the construct validity vulnerability and producing the final 61,674 records.
Applications & Baselines
This corpus was expressly designed for the fine-tuning of specialized Transformer encoders. Under identical evaluation protocols, the Spanish-specific BETO encoder achieves State-of-the-Art performance on this dataset, isolating the SATIRE class with a perfect F1-score of 1.00 and achieving an overall Macro F1 of 0.9095.
It is highly recommended to use this dataset for:
- Multi-class classification of Spanish misinformation.
- Stylometric and semantic analysis of deceptive vs. satirical language.
- Training efficient, domain-specific NLP microservices.
Citation
If you use this dataset in your research, please cite our upcoming paper in Informatics (MDPI):
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