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Social-HUMINT: An LLM-Augmented Unified Framework for Measuring Social Media Exposure

Paper recorded by Signals 4 on 2026-10-22 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-22 on arXiv · recorded by Signals 4 on 2026-10-23

Category: cs.AI · 人工智能 · first seen 2026-10-23

Abstract

In the post-API landscape, social-media profiling has become inexpensive and automated: Large Language Models (LLMs) can extract structured information from unstructured content and deduce attributes users never disclosed. A person's true exposure is not what they post but what can be inferred from it. As these practices become routine, exposure to social engineering attacks becomes an operational

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#194 most recent of 960 cs.AI papers we have recorded · ↑ newer: What Makes a Good Unmasking Policy? A Controlled Study of Learned Unma · ↓ older: When to Follow the Teacher: Advantage-Based On-Policy Distillation
Cite this page: Social-HUMINT: An LLM-Augmented Unified Framework for Measuring Social Media Exposure: the #194 most recent of 960 cs.AI papers we have recorded (as of 2026-10-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/social-humint-an-llm-augmented-unified-framework-for-measuring-social-media-expo.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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