Trait, Not State: The Durability of Reading Identity in Social Highlighting
Not on the map. Why?
Abstract (arXiv)
Prior work on a social web highlighter located individuality in selection -- which documents a person chooses to highlight -- but measured it cross-sectionally. We ask the temporal question: is a reader's selection signature a trait or a state? We freeze each reader's first six months of highlighting as a profile and track its own-vs-other advantage on their later selections at growing gaps (to 24+ months), with negatives drawn from the same calendar era -- so supply drift cannot masquerade as personal drift -- at a coarse global level and at a fine level whose negatives and controls come from the reader's own interest neighborhood; the anchor cell reproduces the prior cross-sectional level (+0.188 vs +0.169), validating the harness. Four results. Within the same users, the fine-layer advantage shows no statistically detectable paired decline at any horizon (6-12 month retention R = 1.00 [0.85, 1.18], n = 212; the farthest bin is compatible with a modest decline; the only contrast whose interval excludes zero is the coarse layer at 12-24 months, about 13%). The signal is not reducible to repeated domains (~90% survives excluding all profile sources). Within-person drift is slow (a recent-half profile beats the old half by +0.042). Prospectively, personal profiles -- even one built from a reader's earliest documents, median 20 months before evaluation -- rank their next reads at roughly 3x the AP of every simple non-personal prior tested. We use "trait" operationally (a stable signature under continued engagement); the scope is heavy, long-tenured readers of one platform, and exposure is not separable from choice.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 11 Jun 2026.
Builds on
7 of 12 references.
References · 7
- 2018Self-Attentive Sequential Recommendationcited by 1,184
- 2015Session-based Recommendations with Recurrent Neural Networkscited by 695
- 2020A Re-visit of the Popularity Baseline in Recommender Systemscited by 18
- 2026Personal Salience: Highlighting Is Social, but Individuality Lives in Selectioncited by 5
- 2026Selection, Not Salience: The Shape and Limits of Personalization in Social Highlightingcited by 4
- 2026The Long Tail, Not the Front Page: Cold-Start Prediction of Crowd Highlight Saliencecited by 3
- 2026Factions Within, Uncertain Across: Within-Document Reader Sub-Groups in Social Highlightingcited by 1
Built on by
Not cited yet.