@InProceedings{10.1007/978-3-031-43129-6_9,
author="Chang, Rong-Ching
and May, Jonathan
and Lerman, Kristina",
editor="Thomson, Robert
and Al-khateeb, Samer
and Burger, Annetta
and Park, Patrick
and A. Pyke, Aryn",
title="Feedback Loops and Complex Dynamics of Harmful Speech in Online Discussions",
booktitle="Social, Cultural, and Behavioral Modeling",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="85--94",
abstract="Harmful and toxic speech contribute to an unwelcoming online environment that suppresses participation and conversation. Efforts have focused on detecting and mitigating harmful speech; however, the mechanisms by which toxicity degrades online discussions are not well understood. This paper makes two contributions. First, to comprehensively model harmful comments, we introduce a multilingual misogyny and sexist speech detection model (https://huggingface.co/annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal). Second, we model the complex dynamics of online discussions as feedback loops in which harmful comments lead to negative emotions which prompt even more harmful comments. To quantify the feedback loops, we use a combination of mutual Granger causality and regression to analyze discussions on two political forums on Reddit: the moderated political forum r/Politics and the moderated neutral political forum r/NeutralPolitics. Our results suggest that harmful comments and negative emotions create self-reinforcing feedback loops in forums. Contrarily, moderation with neutral discussion appears to tip interactions into self-extinguishing feedback loops that reduce harmful speech and negative emotions. Our study sheds more light on the complex dynamics of harmful speech and the role of moderation and neutral discussion in mitigating these dynamics.",
isbn="978-3-031-43129-6"
}

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• generated_from_trainer metrics:
• accuracy
• f1
• precision
• recall model-index:
• name: xlm-roberta-base-misogyny-sexism-indomain-mix-bal results: []

xlm-roberta-base-misogyny-sexism-indomain-mix-bal

• Loss: 0.8259
• Accuracy: 0.826
• F1: 0.8333
• Precision: 0.7996
• Recall: 0.87
• Mae: 0.174
• Tn: 391
• Fp: 109
• Fn: 65
• Tp: 435

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Framework versions

• Transformers 4.20.1
• Pytorch 1.12.0+cu102
• Datasets 2.3.2
• Tokenizers 0.12.1

Multilingual_Misogyny_Detection