Personal Salience: Highlighting Is Social, but Individuality Lives in Selection

• URL: http://arxiv.org/abs/2606.09024v1
• Date: Mon, 08 Jun 2026 04:44:51 GMT
• Title: Personal Salience: Highlighting Is Social, but Individuality Lives in Selection
• Abstract summary: 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.
• Score: 1.8620637029128544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• PersonaShot: Benchmarking Person-Centric Narrative Continuity in Multi-Shot Video Generation [118.49150310327475]
  PersonaShot is the first person-centric benchmark for narrative continuity in multi-shot video generation.<n>It contains approximately 1,000 multi-shot segments and 16 metrics spanning physical continuity, affective dynamics, and cinematic grammar.
  arXiv  Detail & Related papers  (2026-08-17T15:34:54Z)
• Can MLLMs Critique Like Humans? Evaluating Open-Ended Aesthetic Reasoning in Multimodal Large Language Models [16.0895345187708]
  Open-ended aesthetic critique is a challenge for multimodal large language models (MLLMs)<n>We score five open-weight MLLMs against multiple ranked human critiques per photo with reference-based similarity metrics.<n>Stricter lexical and learned metrics show only weak alignment with human critiques.<n>We argue that reference-based similarity rewards a fluent, comprehensive critique style rather than the selectivity and specificity of human critique.
  arXiv  Detail & Related papers  (2026-06-29T01:31:28Z)
• Trait, Not State: The Durability of Reading Identity in Social Highlighting [1.8620637029128544]
  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.
  arXiv  Detail & Related papers  (2026-06-11T04:59:37Z)
• 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)
• From Self to Other: Evaluating Demographic Perspective-Taking in LLM Hate Speech Annotation [5.762370982168011]
  Persona-conditioned Large Language Models (models prompted to adopt a specific demographic identity) have been proposed as a way to simulate diverse perspectives at scale.<n>We evaluate three aspects of human social judgement: (i) whether personas from different groups disagree in human-like ways (inter-group disagreement), (ii) whether they become more sensitive when content targets their own identity (in-group sensitivity), and (iii) whether they can accurately predict how another group would react (vicarious prediction).<n>Our results show that no model consistently captures all three dimensions, and performance is highly model-dependent and does not emerge reliably from
  arXiv  Detail & Related papers  (2026-06-04T15:09:58Z)
• ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost? [54.44351107428039]
  Large language models (LLMs) offer new opportunities for personalizing Plain Language Summaries (PLS)<n>It remains unclear whether personalization helps, which strategies are most effective, and how to balance personalization with safety.<n>We introduce ReLay, a dataset of 300 participant--PLS pairs from 50 lay participants in both static (expert-written) and interactive settings.<n>ReLay includes user characteristics, health information needs, information-seeking behavior, comprehension outcomes, interaction logs, and quality ratings.
  arXiv  Detail & Related papers  (2026-05-01T07:11:12Z)
• 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)
• Large-scale online deanonymization with LLMs [58.46277616551135]
  We show that large language models can be used to perform at-scale deanonymization.<n>With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision.
  arXiv  Detail & Related papers  (2026-02-18T19:02:50Z)
• Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback [8.04095222893591]
  We find significant gaps in fairness preferences depending on the race, age, political stance, educational level, and LGBTQ+ identity of annotators. We also demonstrate that demographics mentioned in text have a strong influence on how users perceive individual fairness in moderation.
  arXiv  Detail & Related papers  (2024-06-09T19:42:25Z)
• Understanding writing style in social media with a supervised contrastively pre-trained transformer [57.48690310135374]
  Online Social Networks serve as fertile ground for harmful behavior, ranging from hate speech to the dissemination of disinformation. We introduce the Style Transformer for Authorship Representations (STAR), trained on a large corpus derived from public sources of 4.5 x 106 authored texts. Using a support base of 8 documents of 512 tokens, we can discern authors from sets of up to 1616 authors with at least 80% accuracy.
  arXiv  Detail & Related papers  (2023-10-17T09:01:17Z)
• Unifying Data Perspectivism and Personalization: An Application to Social Norms [10.480094567764606]
  We examine a corpus of social media posts about conflict from a set of 13k annotators and 210k judgements of social norms. We apply personalization methods to the modeling of annotators and compare their effectiveness for predicting the perception of social norms.
  arXiv  Detail & Related papers  (2022-10-26T07:43:26Z)
• The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks [75.58692290694452]
  We compare social biases with non-social biases stemming from choices made during dataset construction that might not even be discernible to the human eye. We observe that these shallow modifications have a surprising effect on the resulting degree of bias across various models.
  arXiv  Detail & Related papers  (2022-10-18T17:58:39Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• My tweets bring all the traits to the yard: Predicting personality and relational traits in Online Social Networks [4.095574580512599]
  This study aims to provide a prediction model for a holistic personality profiling in Online Social Networks (OSNs) We first designed a feature engineering methodology that extracts a wide range of features from OSN accounts of users. Then, we designed a machine learning model that predicts scores for the psychological traits of the users based on the extracted features.
  arXiv  Detail & Related papers  (2020-09-22T20:30:56Z)
• On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation [14.12186042953335]
  This work focuses on analyzing Singapore users' personal values and developing effective models to predict their personal values using their Facebook data. We incorporate the correlations among personal values into our proposed Stack Model consisting of a task-specific layer of base models and a cross-stitch layer model.
  arXiv  Detail & Related papers  (2020-07-16T04:36:13Z)

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.