Linking Social Media Posts to News with Siamese Transformers
This work addresses the need for efficient data acquisition in computational social science by reducing reliance on manual methods, though it is incremental as it builds on existing Siamese Transformer techniques.
The paper tackled the problem of automatically retrieving social media comments discussing trending topics from large corpora using only a few seed news articles, achieving accurate ad-hoc retrieval to replace costly keyword searches and crowd-sourced annotations.
Many computational social science projects examine online discourse surrounding a specific trending topic. These works often involve the acquisition of large-scale corpora relevant to the event in question to analyze aspects of the response to the event. Keyword searches present a precision-recall trade-off and crowd-sourced annotations, while effective, are costly. This work aims to enable automatic and accurate ad-hoc retrieval of comments discussing a trending topic from a large corpus, using only a handful of seed news articles.