Mashable News Articles with Text and Auxiliary Features for Channel Prediction

The news_channel dataset predicts which Mashable.com news category an article belongs to based on its title text and auxiliary numerical features. The original data was collected for the Online News Popularity dataset hosted by the UCI Machine Learning Repository. This version was referenced in the paper 'Benchmarking multimodal automl for tabular data with text fields' by Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, and Alexander J. Smola.

Use Cases

• Benchmarking multimodal AutoML systems based on the combination of text and numeric features mentioned in the description.
• Training text classification models to categorize news articles based on title content.
• Developing hybrid models that leverage auxiliary features like word count and keyword density to improve prediction accuracy.
• Studying the relationship between article metadata and its thematic channel for content analysis.

Strengths

• Dataset is derived from a known academic source, the UCI Online News Popularity dataset.
• Represents a specific multimodal task combining one text field with many tabular features.
• Has clear authorship linked to a published arXiv preprint on multimodal AutoML benchmarking.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

UCI Machine Learning Repository (Online News Popularity Data Set)

Collection Method

Collected from Mashable.com, as referenced in the original UCI dataset description.

Time Range

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Freshness

Last updated date is unknown.

Geography

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