Datasets:

XinWangcs
/

Stressor

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Dataset Viewer

Split (2)

train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowTypeError
Message:      ("Expected bytes, got a 'int' object", 'Conversion failed for column labels with type object')
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3335, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2096, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2296, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 476, in _iter_arrow
                  for key, pa_table in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 323, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 167, in _generate_tables
                  pa_table = pa.Table.from_pandas(df, preserve_index=False)
                File "pyarrow/table.pxi", line 3874, in pyarrow.lib.Table.from_pandas
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 624, in dataframe_to_arrays
                  arrays[i] = maybe_fut.result()
                File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 439, in result
                  return self.__get_result()
                File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
                  raise self._exception
                File "/usr/local/lib/python3.9/concurrent/futures/thread.py", line 58, in run
                  result = self.fn(*self.args, **self.kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 598, in convert_column
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 592, in convert_column
                  result = pa.array(col, type=type_, from_pandas=True, safe=safe)
                File "pyarrow/array.pxi", line 339, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 85, in pyarrow.lib._ndarray_to_array
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column labels with type object')

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Context

A stressor is a specific cause or trigger of an individual’s mental health issue, such as stress, anxiety, or suicidal thoughts. Identifying stressors is a key step toward understanding and improving mental well-being.

Content

This dataset contains 4,001 manually annotated online posts collected from June 2018 to June 2022. Each sample includes the following fields:

text: The content of the individual's post.
labels: The identified stressor(s) within the post, including their start and end positions (word-level).
time_interval: A numeric value (1-8) representing the half-year period in which the post was published.
- For example, 1 indicates the post was published between 2018-07-01 and 2018-12-31,
  while 8 corresponds to 2022-01-01 to 2022-06-30.

Data Partitioning

By default, we divide the data based on time to simulate a real-world scenario: 1. Samples from time_interval = 1 to 7 serve as training data (historical period). 2. Samples from time_interval = 8 serve as test data (most recent half-year). This setup allows researchers to train models on past data and evaluate performance on future data.

However, you are free to re-partition the dataset based on your specific task or experimental design.

Citation

Wang, Xin, et al. "MISE: Meta-knowledge Inheritance for Social Media-Based Stressor Estimation." Proceedings of the ACM on Web Conference 2025.

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