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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 12 new columns ({'url', 'width', 'image_size_bytes', 'average_hash', 'colorhash', 'height', 'total_face_area', 'num_faces', 'mime', 'image_id', 'has_text', 'phash'}) and 3 missing columns ({'fact_check_title', 'fact_check_url', 'review_date'}).

This happened while the csv dataset builder was generating data using

hf://datasets/academic-datasets/AMMeBa/image_metadata.csv (at revision b9a4e82e8e00ee496df8b75101820c53a64b2440)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              image_id: string
              mime: string
              height: string
              width: string
              image_size_bytes: int64
              num_faces: int64
              total_face_area: int64
              has_text: bool
              url: string
              average_hash: string
              phash: string
              colorhash: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1649
              to
              {'fact_check_url': Value(dtype='string', id=None), 'review_date': Value(dtype='string', id=None), 'fact_check_title': Value(dtype='string', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1324, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 938, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 12 new columns ({'url', 'width', 'image_size_bytes', 'average_hash', 'colorhash', 'height', 'total_face_area', 'num_faces', 'mime', 'image_id', 'has_text', 'phash'}) and 3 missing columns ({'fact_check_title', 'fact_check_url', 'review_date'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/academic-datasets/AMMeBa/image_metadata.csv (at revision b9a4e82e8e00ee496df8b75101820c53a64b2440)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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fact_check_url
string
review_date
string
fact_check_title
string
http://africacheck.org/comment/reply/node/11692/field_comments
2022.04.11
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http://africacheck.org/comment/reply/node/11788/field_comments
2022.05.03
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2019.07.23
Do 1 in 4 South African employees have depression?
http://africacheck.org/fact-checks/reports/do-10-nigerians-die-cancer-every-hour
2018.07.05
Do 10 Nigerians die from cancer every hour?
http://africacheck.org/fact-checks/reports/do-17-million-people-pay-80-sas-income-tax
2017.05.09
Do 1.7 million people pay 80% of SA’s income tax?
http://africacheck.org/fact-checks/reports/do-200000-abortions-many-repeat-take-place-south-africa-each-year
2018.06.07
Do 200,000 abortions - many a ‘repeat’ - take place in South Africa each year?
End of preview.

Dataset Card for AMMeBa: Annotated Misinformation, Media-Based

Dataset Details

The prevalence and harms of online misinformation is a perennial concern for internet platforms, institutions and society at large. Over time, information shared online has become more media-heavy and misinformation has readily adapted to these new modalities. The rise of generative AI-based tools, which provide widely-accessible methods for synthesizing realistic audio, images, video and human-like text, have amplified these concerns. Despite intense interest on the part of the public and significant press coverage, quantitative information on the prevalence and modality of media-based misinformation remains scarce. Here, we present the results of a two-year study using human raters to annotate online media-based misinformation, mostly focusing on images, based on claims assessed in a large sample of publicly-accessible fact checks with the ClaimReview markup. We present an image typology, designed to capture aspects of the image and manipulation relevant to the image's role in the misinformation claim. We visualize the distribution of these types over time. We show the the rise of generative AI-based content in misinformation claims, and that it's commonality is a relatively recent phenomenon, occurring significantly after heavy press coverage. We also show "simple" methods dominated historically, particularly context manipulations, and continued to hold a majority as of the end of data collection in November 2023. The dataset, Annotated Misinformation, Media-Based (AMMeBa), is publicly-available, and we hope that these data will serve as both a means of evaluating mitigation methods in a realistic setting and as a first-of-its-kind census of the types and modalities of online misinformation.

Dataset Description

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Uses

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Dataset Structure

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Dataset Creation

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Source Data

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Annotation process

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Personal and Sensitive Information

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Bias, Risks, and Limitations

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Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation

BibTeX:

@misc{dufour2024ammeba,
      title={AMMeBa: A Large-Scale Survey and Dataset of Media-Based Misinformation In-The-Wild}, 
      author={Nicholas Dufour and Arkanath Pathak and Pouya Samangouei and Nikki Hariri and Shashi Deshetti and Andrew Dudfield and Christopher Guess and Pablo Hernández Escayola and Bobby Tran and Mevan Babakar and Christoph Bregler},
      year={2024},
      eprint={2405.11697},
      archivePrefix={arXiv},
      primaryClass={cs.CY}
}
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