Event Details

In the digital age, social media are key for sharing news, with most users relying on them for updates. Detecting events in this vast, unstructured data is labour-intensive, requiring automated methods. This talk explores how temporal word dynamics can improve event detection, addressing semantic gaps in current approaches.

In today's digital era, social media have become primary platforms for disseminating newsworthy content, with most internet users relying on them for regular updates. Thus, understanding and detecting important events from social media data streams is vital for various applications ranging from crisis management to market analysis. However, the vast volume and unstructured nature of this data, generated by a diverse range of users, make manual detection methods highly labour-intensive. As a result, automated intelligent mechanisms have become essential for efficiently handling event detection tasks. However, most available social media event detection approaches primarily rely on data statistics, ignoring semantics, making them vulnerable to critical information loss. Following this gap, this presentation will explore how temporal word dynamics can be involved in achieving effective event detection from social media data.