Dynamic hashtag recommendation in social media with trend shift detection and adaptation
This addresses the challenge of adapting to evolving trends in social media for users and platforms, though it is incremental as it builds on existing recommendation systems.
The paper tackled the problem of hashtag recommendation in dynamic social media by introducing H-ADAPTS, a method that detects trend shifts and adapts models using recent posts, significantly outperforming existing solutions in case studies like COVID-19 and the 2020 US election.
Hashtag recommendation systems have emerged as a key tool for automatically suggesting relevant hashtags and enhancing content categorization and search. However, existing static models struggle to adapt to the highly dynamic nature of social media conversations, where new hashtags constantly emerge and existing ones undergo semantic shifts. To address these challenges, this paper introduces H-ADAPTS (Hashtag recommendAtion by Detecting and adAPting to Trend Shifts), a dynamic hashtag recommendation methodology that employs a trend-aware mechanism to detect shifts in hashtag usage-reflecting evolving trends and topics within social media conversations-and triggers efficient model adaptation based on a (small) set of recent posts. Additionally, the Apache Storm framework is leveraged to support scalable and fault-tolerant analysis of high-velocity social data, enabling the timely detection of trend shifts. Experimental results from two real-world case studies, including the COVID-19 pandemic and the 2020 US presidential election, demonstrate the effectiveness of H-ADAPTS in providing timely and relevant hashtag recommendations by adapting to emerging trends, significantly outperforming existing solutions.