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arXiv Computation and Language

Zero-shot narrative detection in social messaging

The paper explores how large language models can detect hidden narratives in social messages without training data. By feeding the models human-written narrative descriptions, performance improves markedly, while automatically generated descriptions or few-shot examples can hurt accuracy. Ensemble techniques, especially majority voting, further boost robustness, and larger models show the best results with less sensitivity to prompts.

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arXiv Computation and Language
Sep 11

Characterizing Narrative Content in Web-scale LLM Pretraining Data

The paper presents a detailed examination of narrative elements—agency, setting, and events—within the Dolma web-scale pretraining corpus. Using a framework of 11 interpretable dimensions, the authors hand‑annotated 400 passages, expanded this to a 25,000‑passage LLM‑labeled dataset, and trained NarraBERT models to predict narrative features across 13 million passages, producing the NarraDolma dataset. The study reveals that narrative structure is measurable at scale and that narrative qualities vary unevenly across different data sources, topics, and formats, highlighting gaps in current data curation practices.

By Teagan Johnson, Elliott Ash, Andrew Piper, Maria Antoniak
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arXiv Computation and Language
Oct 23

Interpretive Variation and Discourse Structure in Detecting Enthymemes in Controversial Political Discourse

The paper investigates how to detect enthymemes—arguments with unstated premises or conclusions—in controversial political tweets about vaccination and immigration. Despite extensive fine‑tuning, models achieve an F1 score of only about 0.67, prompting the authors to propose new evaluation metrics that compare predictions to the full distribution of annotator votes and a model that directly predicts this distribution. The study also shows that models rely on explicit argumentative structure, often missing implicit links that readers must infer, raising concerns for content moderation systems that depend on majority labels.

By Martial Pastor, Nelleke Oostdijk
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arXiv Machine Learning
Sep 25

Agentic Detection of Online Conspiracies

The paper presents an agentic framework for detecting conspiratorial content in social media by inferring the speaker’s intent rather than merely identifying explicit claims. It leverages social context and adaptive tool use, demonstrating superior performance over text-only and non-agentic models on a large Hebrew tweet dataset spanning election cycles and the COVID pandemic. The study highlights the importance of context-aware, reasoning-driven approaches for accurate conspiracy detection.

By Lior Biton, Oren Tsur
agentssafety
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arXiv AI
Aug 25

ProBel: Propaganda Detection with Techniques, Spans, and Explanations

ProBel is a bilingual Arabic and English resource for propaganda detection that aligns binary labels, multi-label annotations for 23 propaganda techniques grouped into six categories, technique-labeled spans, and reference explanations for news sentences. The dataset supports matched binary, coarse-grained, multi-label, and span-level tasks in both languages, and the authors evaluate zero‑shot prompting, task‑specific fine‑tuning, and joint training. A single bilingual multi‑task model achieves the best overall performance, with cross‑task analysis revealing that joint classification preserves binary performance while span‑only training can weaken sentence‑level prediction, and that joint bilingual training yields the most stable results.

By Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Elisa Sartori, Giovanni Da San Martino, Firoj Alam
llmsfine-tuning
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