Mashable News Articles with Text and Auxiliary Features for Channel Prediction

Mashable.com news articles are used to predict their publishing channel based on title text and auxiliary numerical features. The dataset originates from the UCI Machine Learning Repository's Online News Popularity collection and was referenced in a 2021 arXiv preprint benchmarking multimodal AutoML. Authors include Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, and Alexander J. Smola.

Use Cases

• Predict news article category based on title text content.
• Benchmark multimodal machine learning models using combined text and numerical features.
• Analyze the relationship between article metadata (e.g., word count, keyword density) and its topic channel.
• Train text classification models enhanced with auxiliary numerical predictors.

Strengths

• Dataset combines a text field with multiple auxiliary numerical features, representing a multimodal task.
• Originates from the well-known UCI Machine Learning Repository, suggesting a degree of curation.
• Was used in a published academic preprint benchmarking multimodal AutoML.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and last update date are unknown, which may limit suitability assessment.
• Data may reflect temporal or source bias inherent to its collection from a single news platform.

Provenance

Source

UCI Machine Learning Repository (Online News Popularity Data Set)

Collection Method

Collected from Mashable.com news articles, as referenced in the description.

Time Range

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Freshness

Last updated date is unknown; freshness unverified.

Geography

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