Why They Link: An Intent Taxonomy for Including Hyperlinks in Social Posts

• URL: http://arxiv.org/abs/2601.17601v1
• Date: Sat, 24 Jan 2026 21:32:28 GMT
• Title: Why They Link: An Intent Taxonomy for Including Hyperlinks in Social Posts
• Authors: Fangping Lan, Abdullah Aljebreen, Eduard C. Dragut,
• Abstract summary: URLs serve as bridges between social media platforms and the broader web, linking user-generated content to external information resources.<n>On Twitter (X), approximately one in five tweets contains at least one URL, underscoring their central role in information dissemination.<n>We develop an intent taxonomy for including hyperlinks in social posts through a hybrid approach that begins with a bottom-up, data-driven process using large-scale crowdsourced annotations.<n>The final taxonomy comprises 6 top-level categories and 26 fine-grained intention classes, capturing diverse communicative purposes.
• Score: 8.617846623592962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: URLs serve as bridges between social media platforms and the broader web, linking user-generated content to external information resources. On Twitter (X), approximately one in five tweets contains at least one URL, underscoring their central role in information dissemination. While prior studies have examined the motivations of authors who share URLs, such author-centered intentions are difficult to observe in practice. To enable broader downstream use, this work investigates reader-centered interpretations, i.e., how users perceive the intentions behind hyperlinks included in posts. We develop an intent taxonomy for including hyperlinks in social posts through a hybrid approach that begins with a bottom-up, data-driven process using large-scale crowdsourced annotations, and is then refined using large language model assistance to generate descriptive category names and precise definitions. The final taxonomy comprises 6 top-level categories and 26 fine-grained intention classes, capturing diverse communicative purposes. Applying this taxonomy, we annotate and analyze 1000 user posts, revealing that advertising, arguing, and sharing are the most prevalent intentions. This resulting taxonomy provides a foundation for intent-aware information retrieval and NLP applications, enabling more accurate retrieval, recommendation, and understanding of social media content.

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