Trait, Not State: The Durability of Reading Identity in Social Highlighting
- URL: http://arxiv.org/abs/2606.12904v1
- Date: Thu, 11 Jun 2026 04:59:37 GMT
- Title: Trait, Not State: The Durability of Reading Identity in Social Highlighting
- Abstract summary: We ask the temporal question: is a reader's selection signature a trait or a state?<n>We freeze each reader's first six months of highlighting as a profile.<n>We track its own-vs-other advantage on its later selections at growing gaps.
- Score: 1.8620637029128544
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Measuring Alignment With Reader Highlights Net of Position and Length [1.8620637029128544]
On 120 web documents (at least 12 independent readers each), a language-model importance ranking keeps 38.4% of crowd-marked sentences against 19.9% of their matched neighbours.<n>A single human reader reaches +0.182 - indistinguishable from GPT-5.4 (+0.002 [-0.081, +0.088]) and below 5.
arXiv Detail & Related papers (2026-07-30T06:24:38Z) - Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scale [76.72846830885882]
Personalization algorithms determine what content users encounter on online platforms.<n> auditing these systems is difficult because independent auditors have only black-box access to the algorithms.<n>We introduce a framework for black-box audits of personalization algorithms using generative AI agents as behavioral engines for synthetic accounts.
arXiv Detail & Related papers (2026-06-29T18:25:09Z) - The Long Tail, Not the Front Page: Cold-Start Prediction of Crowd Highlight Salience [1.8620637029128544]
A social highlighter's most useful signal -- which passages a crowd of readers marks -- exists only for documents people have already read.<n>Can the aggregate crowd salience of a document be predicted from its text before its marks accumulate?<n>Prior work on this data found that zero-shot language models recover highlight locations worse than a trivial lead (position) baseline.<n>We ask whether a model trained on the highlight corpus can beat that baseline.
arXiv Detail & Related papers (2026-06-10T04:37:23Z) - Factions Within, Uncertain Across: Within-Document Reader Sub-Groups in Social Highlighting [1.8620637029128544]
We ask the group-level question on a co-readership platform using a margin-preserving curveball null.<n>Experiment 1 shows readers form strong sub-groups far beyond what shared salience, mark density, and sentence popularity predict.<n>Experiment 2 is that grouping a stable reader trait? Here we are honest about power.
arXiv Detail & Related papers (2026-06-10T03:25:36Z) - Selection, Not Salience: The Shape and Limits of Personalization in Social Highlighting [1.8620637029128544]
We map the shape and limits of personalization across reading altitudes.<n>Measurable personalization appears primarily at the selection layer.<n>Going beyond the shared salience layer may be better approached by aggregating individuals.
arXiv Detail & Related papers (2026-06-09T04:18:08Z) - Personal Salience: Highlighting Is Social, but Individuality Lives in Selection [1.8620637029128544]
We ask whether a person's own history predicts their marks better than another reader's does.<n>We separate generic salience (structure), crowd salience (what others marked), and personal salience (the individual residual)<n>Our results are leakage-free, use a dense crowd, and a model-matched control.
arXiv Detail & Related papers (2026-06-08T04:44:51Z) - Response Time Enhances Alignment with Heterogeneous Preferences [49.69696266152175]
We show that augmenting preference datasets with a simple, secondary signal can restore identifiability of the population's average preference.<n>Our results bring promises and open up new opportunities for future data-collection pipelines.
arXiv Detail & Related papers (2026-05-07T22:05:23Z) - NearID: Identity Representation Learning via Near-identity Distractors [116.71529576796605]
Existing vision encoders entangle object identity with background context, leading to unreliable representations and metrics.<n>We introduce the first principled framework to address this vulnerability using Near-identity distractors.<n>We present the NearID dataset (19K identities, 316K matched-context distractors) together with a strict margin-based evaluation protocol.
arXiv Detail & Related papers (2026-04-02T12:33:14Z) - An External Fairness Evaluation of LinkedIn Talent Search [55.18656975953939]
We conduct an independent, third-party audit for bias of LinkedIn's Talent Search ranking system.<n>We focus on potential ranking bias across two attributes: gender and race.<n>Our analysis reveals an under-representation of minority groups in early ranks.
arXiv Detail & Related papers (2025-11-13T19:10:49Z) - CLUE: Non-parametric Verification from Experience via Hidden-State Clustering [64.50919789875233]
We show that correctness of a solution is encoded as a geometrically separable signature within the trajectory of hidden activations.<n>ClUE consistently outperforms LLM-as-a-judge baselines and matches or exceeds modern confidence-based methods in reranking candidates.
arXiv Detail & Related papers (2025-10-02T02:14:33Z) - LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form
Summarization [48.02158981582502]
LongEval is a set of guidelines for human evaluation of faithfulness in long-form summaries.
We deploy LongEval in annotation studies on two long-form summarization datasets in different domains.
arXiv Detail & Related papers (2023-01-30T21:31:48Z) - Sequential Recommendation via Stochastic Self-Attention [68.52192964559829]
Transformer-based approaches embed items as vectors and use dot-product self-attention to measure the relationship between items.
We propose a novel textbfSTOchastic textbfSelf-textbfAttention(STOSA) to overcome these issues.
We devise a novel Wasserstein Self-Attention module to characterize item-item position-wise relationships in sequences.
arXiv Detail & Related papers (2022-01-16T12:38:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.