Mephedrone Mentions in 5,000 Double-Annotated Tweets

Anna Kolliakou created a collection of 5,000 tweets manually double-annotated for relevance to mephedrone, a novel psychoactive substance. The dataset provides labeled text data for training models to detect drug-related content on social media. Its geographic scope is not specified, but the data originates from the Twitter platform.

Use Cases

• Train a binary text classifier based on the manually annotated relevance labels for mephedrone mentions.
• Benchmark natural language processing models for detecting novel psychoactive substance discussions on social media.
• Analyze linguistic patterns in social media posts that mention mephedrone based on the annotated tweet text.

Strengths

• Contains 5,000 data points.
• Annotations were performed manually and double-checked, which likely suggests a focus on label quality.

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

Anna Kolliakou via paperswithcode.

Collection Method

Manually double-annotated tweets collected from the Twitter platform.