SURGE: An Event-Centric Social Media Sentiment Time Series Benchmark with Interaction Structure
arXiv:2605.21198v1 Announce Type: cross Abstract: 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
The proliferation of social media data, coupled with advancements in AI capabilities for analysis, makes refining sentiment analysis benchmarks a pressing need.
Improved social media sentiment analysis directly impacts opinion forecasting, crisis response, and the ability to understand collective dynamics, which are crucial for strategic decision-making in various sectors.
The introduction of an event-centric benchmark with interaction structure provides a more nuanced and accurate foundation for developing and evaluating AI models that interpret social media sentiment, moving beyond fragmented post analysis.
- · AI researchers
- · Social media analytics platforms
- · Governments (for crisis response)
- · Marketing and PR firms
- · Legacy sentiment analysis models
- · Organizations relying on simple keyword-based sentiment analysis
More accurate and context-aware social media sentiment analysis becomes possible, improving predictions related to public opinion.
Enhanced real-time understanding of public reaction to events could lead to more effective policy interventions and corporate strategies.
The ability to model social interactions within sentiment could foster new AI agent designs that better understand and influence group dynamics, rather than individual opinions.
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Read at arXiv cs.AI