Personal Salience: Highlighting Is Social, but Individuality Lives in Selection
Not on the map. Why?
Abstract (arXiv)
Social highlighters let people mark passages that matter to them. We ask how much of an individual is recoverable from these naturalistic traces, using a co-readership identity control (the same document highlighted by many users) that holds document and topic fixed and asks whether a person's own history predicts their marks better than another reader's does. We separate generic salience (structure), crowd salience (what others marked), and personal salience (the individual residual). First, highlighting is social: which sentences you mark is predicted far better by the crowd than by structure or by a personal model, and even a well-estimated crowd, an information-privileged baseline that sees others' marks on the same document, beats a frontier LLM twin built from your other-document history; the within-document personal signal is at most a whisper (own-vs-other gap +0.017 by an embedding scorer, small but significant). Second, in sharp contrast, individuality lives in selection: asked which of the already-salient passages are yours, your own history is a strong, leakage-free predictor (gap +0.14). A topic decomposition shows this is largely stable thematic preference: it shrinks ~6-8x against a topically-matched peer, and a thin residual cannot be separated from finer topic. The non-obvious part is an asymmetry: under the same scorer the individual signal is ~6-8x weaker in salience than in selection. Methodologically, naive history-conditioning evaluations leak (the target's own marks enter the profile in ~42% of pairs, inflating personal scores by up to +0.15 AP) and small crowds overstate personalization; our results are leakage-free, use a dense crowd, and a model-matched control. Highlights carry a genuine individual signature, but a thin layer over a strong shared one, surfacing far more in which salient things a person selects than in what is salient.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 8 Jun 2026.
Builds on
6 of 13 references.
References · 6
- 2023Whose Opinions Do Language Models Reflect?cited by 759
- 2023LaMP: When Large Language Models Meet Personalizationcited by 425
- 2022The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluationcited by 133
- 2018PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creationcited by 19
- 2020Better Highlighting: Creating Sub-Sentence Summary Highlightscited by 11
- 2022One Size Does Not Fit All: The Case for Personalised Word Complexity Modelscited by 10
Built on by
Citing papers per year: 2026: 5.
5 papers.
- 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 2
- 2026Factions Within, Uncertain Across: Within-Document Reader Sub-Groups in Social Highlightingcited by 1
- 2026Trait, Not State: The Durability of Reading Identity in Social Highlightingcited by 0
- 2026Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict?cited by 0
- 2026Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict?3 Aug 2026
- 2026Trait, Not State: The Durability of Reading Identity in Social Highlighting11 Jun 2026
- 2026The Long Tail, Not the Front Page: Cold-Start Prediction of Crowd Highlight Salience10 Jun 2026
- 2026Factions Within, Uncertain Across: Within-Document Reader Sub-Groups in Social Highlighting10 Jun 2026
- 2026Selection, Not Salience: The Shape and Limits of Personalization in Social Highlighting9 Jun 2026