From Tweets to Insights: Enhancing Summarization through Social Interaction Context

Abstract

Social media platforms, particularly Twitter (now X), generate massive volumes of short and fragmented posts that pose significant challenges for information retrieval and knowledge extraction. Traditional tweet summarization methods rely primarily on the content of individual tweets, often producing incomplete and shallow summaries that fail to capture the broader discourse context. This study introduces a comment-aware summarization framework that integrates user replies and interactions into the summarization process, aiming to enhance informativeness, coherence, and contextual richness. A dataset of tweets and associated comments was manually collected and preprocessed. Two summarization interfaces were developed: (1) a baseline tweet-only system and (2) an experimental comment-aware system that combines tweets with semantically relevant, engagement-weighted comments. Both systems employed TF–IDF vectorization, cosine similarity, and the TextRank algorithm for extractive summarization. A within-subjects user study with 32 participants compared the two systems across quantitative metrics (task completion time, number of queries, correctness of results) and qualitative dimensions (satisfaction, credibility, ease of use, diversity of perspectives). Results showed that the comment-aware system required slightly longer task times but significantly reduced the number of queries (p < 0.001) and improved accuracy (p < 0.01). Participants consistently rated the comment-aware summaries as more informative and reflective of diverse perspectives. These findings highlight the value of socially enriched summarization in overcoming the limitations of short-form content and provide design recommendations for next-generation information retrieval systems.