Analysis of Socially Unacceptable Discourse with Zero-shot Learning

• URL: http://arxiv.org/abs/2409.13735v1
• Date: Tue, 10 Sep 2024 07:32:00 GMT
• Title: Analysis of Socially Unacceptable Discourse with Zero-shot Learning
• Authors: Rayane Ghilene, Dimitra Niaouri, Michele Linardi, Julien Longhi,
• Abstract summary: Socially Unacceptable Discourse (SUD) analysis is crucial for maintaining online positive environments. We investigate the effectiveness of Entailment-based zero-shot text classification (unsupervised method) for SUD detection and characterization by leveraging pre-trained transformer models and prompting techniques. The results demonstrate good generalization capabilities of these models to unseen data and highlight the promising nature of this approach for generating labeled datasets for the analysis and characterization of extremist narratives.
• Score: 2.3999111269325266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Socially Unacceptable Discourse (SUD) analysis is crucial for maintaining online positive environments. We investigate the effectiveness of Entailment-based zero-shot text classification (unsupervised method) for SUD detection and characterization by leveraging pre-trained transformer models and prompting techniques. The results demonstrate good generalization capabilities of these models to unseen data and highlight the promising nature of this approach for generating labeled datasets for the analysis and characterization of extremist narratives. The findings of this research contribute to the development of robust tools for studying SUD and promoting responsible communication online.

Related papers

• Towards Biologically Plausible and Private Gene Expression Data Generation [47.72947816788821]
  Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, however, primarily focuses on basic benchmarking datasets and tends to report promising results only for elementary metrics and relatively simple data distributions. We initiate a systematic analysis of how DP generative models perform in their natural application scenarios, specifically focusing on real-world gene expression data.
  arXiv  Detail & Related papers  (2024-02-07T14:39:11Z)
• Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free Metric [5.592917884093537]
  The potential of quality estimation (QE) metrics is introduced and evaluated as a novel tool to enhance explainable artificial intelligence (XAI) in ASR systems. The capabilities of the NoRefER metric are explored in identifying word-level errors to aid post-editors in refining ASR hypotheses.
  arXiv  Detail & Related papers  (2024-01-20T16:48:55Z)
• FIMBA: Evaluating the Robustness of AI in Genomics via Feature Importance Adversarial Attacks [0.0]
  This paper demonstrates the vulnerability of AI models often utilized downstream tasks on recognized public genomics datasets. We undermine model robustness by deploying an attack that focuses on input transformation while mimicking the real data and confusing the model decision-making. Our empirical findings unequivocally demonstrate a decline in model performance, underscored by diminished accuracy and an upswing in false positives and false negatives.
  arXiv  Detail & Related papers  (2024-01-19T12:04:31Z)
• TRIAGE: Characterizing and auditing training data for improved regression [80.11415390605215]
  We introduce TRIAGE, a novel data characterization framework tailored to regression tasks and compatible with a broad class of regressors. TRIAGE utilizes conformal predictive distributions to provide a model-agnostic scoring method, the TRIAGE score. We show that TRIAGE's characterization is consistent and highlight its utility to improve performance via data sculpting/filtering, in multiple regression settings.
  arXiv  Detail & Related papers  (2023-10-29T10:31:59Z)
• Post Hoc Explanations of Language Models Can Improve Language Models [43.2109029463221]
  We present a novel framework, Amplifying Model Performance by Leveraging In-Context Learning with Post Hoc Explanations (AMPLIFY) We leverage post hoc explanation methods which output attribution scores (explanations) capturing the influence of each of the input features on model predictions. Our framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25% over a wide range of tasks.
  arXiv  Detail & Related papers  (2023-05-19T04:46:04Z)
• On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training [109.9218185711916]
  Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind social media texts or reviews. We propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
  arXiv  Detail & Related papers  (2023-04-19T11:07:43Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes [93.61202366677526]
  We study the offline reinforcement learning (RL) in the face of unmeasured confounders. We propose various policy learning methods with the finite-sample suboptimality guarantee of finding the optimal in-class policy.
  arXiv  Detail & Related papers  (2022-09-18T22:03:55Z)
• Artificial Text Detection via Examining the Topology of Attention Maps [58.46367297712477]
  We propose three novel types of interpretable topological features for this task based on Topological Data Analysis (TDA) We empirically show that the features derived from the BERT model outperform count- and neural-based baselines up to 10% on three common datasets. The probing analysis of the features reveals their sensitivity to the surface and syntactic properties.
  arXiv  Detail & Related papers  (2021-09-10T12:13:45Z)
• Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models [6.428095289290804]
  Deceptive news posts shared in online communities can be detected with NLP models. We use characteristics of online communities and authors to explain the performance of a neural network deception detection model.
  arXiv  Detail & Related papers  (2021-04-27T21:49:34Z)

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.