Datasets:

lukeslp
/

us-attention-data

Error code:   FeaturesError
Exception:    ArrowTypeError
Message:      ("Expected bytes, got a 'int' object", 'Conversion failed for column metadata with type object')
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
                  pa_table = paj.read_json(
                             ^^^^^^^^^^^^^^
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column(/categories/wikipedia/datasets/[]/records) changed from string to number 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 243, 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 3608, 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 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 544, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, 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/json/json.py", line 190, in _generate_tables
                  pa_table = pa.Table.from_pandas(df, preserve_index=False)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 4795, in pyarrow.lib.Table.from_pandas
                File "/usr/local/lib/python3.12/site-packages/pyarrow/pandas_compat.py", line 637, in dataframe_to_arrays
                  arrays = [convert_column(c, f)
                            ^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pyarrow/pandas_compat.py", line 625, in convert_column
                  raise e
                File "/usr/local/lib/python3.12/site-packages/pyarrow/pandas_compat.py", line 619, in convert_column
                  result = pa.array(col, type=type_, from_pandas=True, safe=safe)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/array.pxi", line 365, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 91, in pyarrow.lib._ndarray_to_array
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column metadata with type object')

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US Attention Data

Weekly cross-platform attention metrics for tracking how much the world pays attention to the United States. Combines Wikipedia pageviews, GDELT global event mentions, and Google Trends search interest from 2020-2025.

I built this dataset for the one-year visualization project, which maps US global sentiment over time. Part of the Data Trove collection.

What's Inside

File	Size	Description
`wikipedia_pageviews.json`	2.5 MB	Daily pageview counts for US-related Wikipedia articles
`wikipedia_event_articles.json`	214 KB	Event-linked article metadata
`wikipedia_trending.json`	256 KB	Trending article detection
`trends_data.json`	810 KB	Google Trends search interest over time
`weekly_trends.json`	26 KB	Weekly trending topic aggregations
`gdelt_timeline.json`	131 KB	GDELT event mention timelines
`gdelt_weekly_events.json`	158 KB	GDELT weekly aggregated event counts and tone
`events_unified.json`	89 KB	Unified event data across all sources
`weekly_attention_timeline.json`	57 KB	Combined weekly attention metrics
`unified_data.json`	27 KB	Merged dataset across all attention sources
`attention_metadata.json`	2 KB	Collection metadata and schema

Total: ~4.2 MB

Quick Start

Python

import json

with open("wikipedia_pageviews.json") as f:
    pageviews = json.load(f)

# Weekly attention across all sources
with open("weekly_attention_timeline.json") as f:
    timeline = json.load(f)

D3.js

const pageviews = await d3.json("wikipedia_pageviews.json");
const gdelt = await d3.json("gdelt_weekly_events.json");

Data Sources

Source	What It Tracks	Coverage
Wikipedia Pageviews API	Article view counts	2020-2025, daily
GDELT Project	Global event mentions and media tone	2020-2025, weekly
Google Trends	Search interest indices	2020-2025, weekly

Use Cases

Tracking how global attention to the US shifts over time
Correlating media events with Wikipedia traffic and search interest
Identifying seasonal attention patterns (elections, holidays, crises)
Building composite attention indices from multiple independent signals

one-year visualization -- the viz this data powers
Data Trove -- full dataset catalog
lukesteuber.com -- portfolio

Author

Luke Steuber -- @lukesteuber.com on Bluesky

License

MIT. See LICENSE.

Data sourced from Wikipedia (CC BY-SA), GDELT (open), and Google Trends (fair use for research).

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