Social Media Event Detection Using SpaCy Named Entity Recognition and Spectral Embeddings
Monitoring of public postings on social media platforms, such as Twitter, offers a valuable opportunity to detect events as they happen. Building off existing approaches for event detection, we evaluated the use of a pre-trained named entity recognition model followed by graph-based spectral clustering to detect events. We also examined transformer-based approaches for weighting the edges of the graph for event detection. Unlabeled events in the dataset we detected and verified are described.
- Year: 2022
- Category: Artificial Intelligence
- Tag: Twitter, event detection, microblogging, social media, manifold learning
- Author: Joseph P. Salisbury
- Released: 9th International Conference on Multimedia and Human-Computer Interaction (MHCI '22)
Featured Riverside Research Author(s)
Joseph P. Salisbury
Dr. Joseph Salisbury is a neuroscientist (Ph.D., Brandeis University, 2013) and software developer whose current research focuses on human-computer interaction, human-robot interaction, and applications of large language models.
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