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# Rumors dataset |
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This deception dataset was created using PHEME dataset from |
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https://figshare.com/articles/dataset/PHEME_dataset_of_rumours_and_non-rumours/4010619/1 |
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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. |
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## Cleaning |
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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. |
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## Preprocessing |
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Whitespace, quotes, bulletpoints, unicode is normalized. |
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## Data |
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The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise. |
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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. |
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