Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks

• URL: http://arxiv.org/abs/2605.12814v1
• Date: Tue, 12 May 2026 23:14:37 GMT
• Title: Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks
• Abstract summary: Polarization in online communities is often studied through either language or interaction structure.<n>We develop a language-grounded signed-network pipeline that derives continuous signed edge weights from stance scores.<n>We analyze how window-level discourse signals relate to temporal variation in structural polarization.
• Score: 17.11804614640313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Polarization in online communities is often studied through either language or interaction structure, but the two views are rarely connected in a unified measurement pipeline. Prior work links them by building interaction graphs from human judgments of agreement and disagreement, leaving a gap between language as observed text and structure as an engineered representation of that text. We address this gap with a language-grounded signed-network pipeline that derives continuous signed edge weights from LLM stance scores and quantifies structural polarization using two complementary measures: a spectral Eigen-Sign score and a partition-based frustration score. After normalization, the two measures show substantial agreement while retaining important differences in their sensitivity to edge magnitude. Applying the framework to Reddit Brexit discussions, we analyze how window-level discourse signals, including toxicity, extreme scalar claims, and perplexity, relate to temporal variation in structural polarization. Edge-level and ablation analyses show that continuous, confidence-weighted signed edges reveal intensity-sensitive patterns that are muted under sign-only representations. We further report an exploratory one-step-ahead forecasting analysis suggesting that lagged language signals may contain information about future polarization beyond structural persistence. Together, the results demonstrate how discourse and signed-network structure can be connected in a single framework for measuring and interpreting polarization dynamics over time.

Related papers

• Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks [68.64050157343334]
  Existing watermarks preserve provenance under editing, but this same robustness allows an adversary to alter critical content while retaining attribution.<n>We introduce an innovative watermark that jointly provides provenance and tamper evidence.<n>The signals share the same mechanism but use independent keys and different seeding windows over normalized text, making one resilient to edits and the other sensitive to reader-visible changes.
  arXiv  Detail & Related papers  (2026-08-13T01:55:16Z)
• Warp-free Cross-view Geo-localization via Feature-space Consensus Mining [49.843311525678565]
  Cross-view geo-localization is challenging due to drastic viewpoint changes and large appearance discrepancies between street-level and satellite imagery.<n>We propose a novel joint-view consensus-guided learning framework that entirely bypasses explicit geometric warping.
  arXiv  Detail & Related papers  (2026-08-10T09:02:06Z)
• From graphemic dependence to lexical structure: a Markovian perspective on Dante's Commedia [0.0]
  This study investigates the structural organisation of Dante's Divina Commedia through a symbolic representation based on vowel-consonant (V/C) encoding.<n>The resulting sequence as a four-state Markov chain yields a parsimonious index of graphemic memory.<n>Across the poem, this index exhibits a slight but consistent increase from the Inferno to the Paradiso, indicating a directional shift in local dependency structure.
  arXiv  Detail & Related papers  (2026-04-24T14:54:59Z)
• SignDPO: Multi-level Direct Preference Optimisation for Skeleton-based Gloss-free Sign Language Translation [52.752934028506274]
  We present SignDPO, a novel multi-level Direct Preference optimisation framework.<n>We show that SignDPO consistently outperforms state-of-the-art gloss-free methods.<n>Our results suggest that multi-level preference alignment is a powerful paradigm for bridging the gap between high-entropy skeletal trajectories and discrete linguistic semantics.
  arXiv  Detail & Related papers  (2026-04-20T09:59:18Z)
• Anatomy of the Modality Gap: Dissecting the Internal States of End-to-End Speech LLMs [15.914430317382077]
  We analyze how speech and text representations evolve layer-by-layer.<n>We find that speech representations exhibit a broad cross-layer alignment band, attributable to the redundant nature of speech.
  arXiv  Detail & Related papers  (2026-03-02T06:21:43Z)
• TOPol: Capturing and Explaining Multidimensional Semantic Polarity Fields and Vectors [0.0]
  This work introduces TOPol, a framework for reconstructing and interpreting multidimensional narrative polarity fields.<n>The framework embeds documents using a transformer-based large language model (tLLM)<n>We evaluate TOPol on two corpora: (i) U.S. Central Bank speeches around a macroeconomic breakpoint, and (ii) Amazon product reviews across rating strata.
  arXiv  Detail & Related papers  (2025-10-29T01:14:21Z)
• Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings [5.229806149125529]
  Stance detection plays a pivotal role in enabling an extensive range of downstream applications, from discourse parsing to tracing the spread of fake news and the denial of scientific facts.<n>We introduce TASTE -- a multimodal architecture for stance detection that harmoniously fuses Transformer-based content embedding with unsupervised structural embedding.<n> TASTE achieves state-of-the-art results on common benchmarks, significantly outperforming an array of strong baselines.
  arXiv  Detail & Related papers  (2024-12-04T19:23:37Z)
• Unpacking polarization: Antagonism and Alignment in Signed Networks of Online Interaction [0.3581083356941628]
  In the 20th century, major fault lines were formed by structural conflicts, like owners vs workers, center vs periphery, etc. We present the FAULTANA pipeline, a computational method to uncover major fault lines in data of signed online interactions. Our method makes it possible to quantify the degree of antagonism prevalent in different online debates, as well as how aligned each debate is to the major fault line.
  arXiv  Detail & Related papers  (2023-07-13T05:57:48Z)
• Word separation in continuous sign language using isolated signs and post-processing [47.436298331905775]
  We propose a two-stage model for Continuous Sign Language Recognition. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model.
  arXiv  Detail & Related papers  (2022-04-02T18:34:33Z)
• Context-Aware Interaction Network for Question Matching [51.76812857301819]
  We propose a context-aware interaction network (COIN) to align two sequences and infer their semantic relationship. Specifically, each interaction block includes (1) a context-aware cross-attention mechanism to effectively integrate contextual information, and (2) a gate fusion layer to flexibly interpolate aligned representations.
  arXiv  Detail & Related papers  (2021-04-17T05:03:56Z)
• Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph [54.03786611989613]
  signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) We develop a unified framework, termed as Signed Infomax Hyperbolic Graph (textbfSIHG) In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion.
  arXiv  Detail & Related papers  (2020-11-25T05:09:03Z)
• Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification [56.893393134328996]
  Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis subtasks. We propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE.
  arXiv  Detail & Related papers  (2020-11-01T11:00:19Z)
• A Neural Approach to Discourse Relation Signal Detection [16.874380052348904]
  We present a data-driven approach to signal detection using a distantly supervised neural network and develop a metric, Delta s. The metric represents each word's positive or negative contribution to the identifiability of a relation in specific instances in context. Our analysis examines the reliability of the metric, the places where it overlaps with and differs from human judgments, and the implications for identifying features that neural models may need.
  arXiv  Detail & Related papers  (2020-01-08T05:14:49Z)

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.