arXiv AI
Oct 12

UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes

The paper introduces UkrMeme, a dataset of 200 Ukrainian wartime memes annotated for persuasive techniques, and PTM-UA, a Ukrainian translation of a subset of the Propaganda 950 benchmark that retains the original visual content. The authors evaluate vision‑language models in a zero‑shot prompting setup across image, text, and multimodal inputs using hierarchical F1 metrics. They also compare Layer‑wise Relevance Propagation and model‑generated token attributions to analyze the semantic categories of evidence emphasized by each explanation method.

By Nazar Andrushko, Vera Schmitt, Veronika Solopova
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