No Time Like the Present: Effects of Language Change on Automated Comment Moderation

• URL: http://arxiv.org/abs/2207.04003v1
• Date: Fri, 8 Jul 2022 16:39:21 GMT
• Title: No Time Like the Present: Effects of Language Change on Automated Comment Moderation
• Authors: Lennart Justen, Kilian M\"uller, Marco Niemann, J\"org Becker
• Abstract summary: The spread of online hate has become a significant problem for newspapers that host comment sections. There is growing interest in using machine learning and natural language processing for automated abusive language detection. We show using a new German newspaper comments dataset that the classifiers trained with naive ML techniques will underperform on future data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The spread of online hate has become a significant problem for newspapers that host comment sections. As a result, there is growing interest in using machine learning and natural language processing for (semi-) automated abusive language detection to avoid manual comment moderation costs or having to shut down comment sections altogether. However, much of the past work on abusive language detection assumes that classifiers operate in a static language environment, despite language and news being in a state of constant flux. In this paper, we show using a new German newspaper comments dataset that the classifiers trained with naive ML techniques like a random-test train split will underperform on future data, and that a time stratified evaluation split is more appropriate. We also show that classifier performance rapidly degrades when evaluated on data from a different period than the training data. Our findings suggest that it is necessary to consider the temporal dynamics of language when developing an abusive language detection system or risk deploying a model that will quickly become defunct.

Related papers

• MENTOR: Multilingual tExt detectioN TOward leaRning by analogy [59.37382045577384]
  We propose a framework to detect and identify both seen and unseen language regions inside scene images. "MENTOR" is the first work to realize a learning strategy between zero-shot learning and few-shot learning for multilingual scene text detection.
  arXiv  Detail & Related papers  (2024-03-12T03:35:17Z)
• Making Retrieval-Augmented Language Models Robust to Irrelevant Context [55.564789967211844]
  An important desideratum of RALMs, is that retrieved information helps model performance when it is relevant. Recent work has shown that retrieval augmentation can sometimes have a negative effect on performance.
  arXiv  Detail & Related papers  (2023-10-02T18:52:35Z)
• The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning [20.643270151774182]
  We seek to continually learn from on-device user corrections through Federated Learning (FL) We explore techniques to target fresh terms that the model has not previously encountered, learn long-tail words, and catastrophic forgetting. In experimental evaluations, we find that the proposed techniques improve model recognition of fresh terms, while preserving quality on the overall language distribution.
  arXiv  Detail & Related papers  (2023-09-29T21:04:10Z)
• Examining Temporal Bias in Abusive Language Detection [3.465144840147315]
  Machine learning models have been developed to automatically detect abusive language. These models can suffer from temporal bias, the phenomenon in which topics, language use or social norms change over time. This study investigates the nature and impact of temporal bias in abusive language detection across various languages.
  arXiv  Detail & Related papers  (2023-09-25T13:59:39Z)
• Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media [0.0]
  This paper explores the applicability of large language models for automated stance detection in a challenging scenario. It involves a morphologically complex, lower-resource language, and a socio-culturally complex topic, immigration. If the approach works in this case, it can be expected to perform as well or better in less demanding scenarios.
  arXiv  Detail & Related papers  (2023-05-22T13:56:35Z)
• Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast [50.19681990847589]
  Existing research has shown that a multilingual pre-trained language model fine-tuned with one (source) language also performs well on downstream tasks for non-source languages. This paper analyzes the fine-tuning process, discovers when the performance gap changes and identifies which network weights affect the overall performance most.
  arXiv  Detail & Related papers  (2023-05-19T06:04:21Z)
• Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors [8.525950031069687]
  We show that general abusive language classifiers tend to be fairly reliable in detecting out-of-domain explicitly abusive utterances but fail to detect new types of more subtle, implicit abuse. We propose an interpretability technique, based on the Testing Concept Activation Vector (TCAV) method from computer vision, to quantify the sensitivity of a trained model to the human-defined concepts of explicit and implicit abusive language.
  arXiv  Detail & Related papers  (2022-04-05T14:52:18Z)
• Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale [52.663117551150954]
  A few popular metrics remain as the de facto metrics to evaluate tasks such as image captioning and machine translation. This is partly due to ease of use, and partly because researchers expect to see them and know how to interpret them. In this paper, we urge the community for more careful consideration of how they automatically evaluate their models.
  arXiv  Detail & Related papers  (2020-10-26T13:57:20Z)
• Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification [52.69730591919885]
  We present a semi-supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations. We observe significant gains in effectiveness on document and intent classification for a diverse set of languages.
  arXiv  Detail & Related papers  (2020-07-29T19:38:35Z)
• How Context Affects Language Models' Factual Predictions [134.29166998377187]
  We integrate information from a retrieval system with a pre-trained language model in a purely unsupervised way. We report that augmenting pre-trained language models in this way dramatically improves performance and that the resulting system, despite being unsupervised, is competitive with a supervised machine reading baseline.
  arXiv  Detail & Related papers  (2020-05-10T09:28:12Z)

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.