SURGE: An Event-Centric Social Media Sentiment Time Series Benchmark with Interaction Structure
Public events on social media generate large volumes of discussion whose collective dynamics carry direct value for opinion forecasting and crisis response. Capturing how these dynamics evolve across an event's lifecycle requires organizing fragmented posts into event-level time series. Existing datasets cover only a small number of events within a single category, and typically discard the interaction structure between posts when constructing time series, which restricts both transfer across event types and controlled study of how interactions shape the resulting collective dynamics. We present SURGE, a multi-event social media benchmark that pairs event-level time series with aligned te...
| Authors | Chen Su, Pengsen Cheng, Yuanhe Tian et al. |
|---|---|
| Primary category | cs.SI |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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