Modern Arabic News Texts From Multiple Websites

Aggregating a collection of single-label modern Arabic texts from news websites and press articles. The data was gathered via web scraping from sources including Al-Arabiya, Al-Youm Al-Sabea (Youm7), and Google search results. The row count, column count, and specific features are unknown.

Use Cases

• Train a text classifier on the single-label modern Arabic news texts for topic categorization.
• Fine-tune a language model on the scraped Arabic news text for generative tasks.
• Analyze linguistic patterns and vocabulary across different news sources like Al-Arabiya and Youm7.

Strengths

• Data is sourced from multiple prominent Arabic news outlets, including Al-Arabiya and Al-Youm Al-Sabea.
• Texts are described as modern Arabic, indicating contemporary language usage.
• The dataset is categorized under 'Size Categories100 Kn1 M', suggesting a scale of at least 100,000 items.

Limitations

• The specific number of rows, columns, and data features is unknown, limiting reproducibility.
• The dataset's temporal coverage and geographic origin of the news articles are not specified.
• The single-label classification schema and label definitions are not detailed in the provided input.

Provenance

Source

News websites including Al-Arabiya, Al-Youm Al-Sabea (Youm7), and Google search results.

Collection Method

Collected via web scraping techniques.

Time Range

null

Freshness

null

Geography

null