Results 1 to 10 of about 353,387 (75)

User Engagement and the Toxicity of Tweets [PDF]

arXiv, 2022
Twitter is one of the most popular online micro-blogging and social networking platforms. This platform allows individuals to freely express opinions and interact with others regardless of geographic barriers. However, with the good that online platforms offer, also comes the bad.
arxiv  

ToxiSpanSE: An Explainable Toxicity Detection in Code Review Comments [PDF]

The 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2023, 2023
Background: The existence of toxic conversations in open-source platforms can degrade relationships among software developers and may negatively impact software product quality. To help mitigate this, some initial work has been done to detect toxic comments in the Software Engineering (SE) domain. Aims: Since automatically classifying an entire text as
arxiv  

Simple Text Detoxification by Identifying a Linear Toxic Subspace in Language Model Embeddings [PDF]

arXiv, 2021
Large pre-trained language models are often trained on large volumes of internet data, some of which may contain toxic or abusive language. Consequently, language models encode toxic information, which makes the real-world usage of these language models limited. Current methods aim to prevent toxic features from appearing generated text. We hypothesize
arxiv  

RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization [PDF]

, 2021
With the widespread use of toxic language online, platforms are increasingly using automated systems that leverage advances in natural language processing to automatically flag and remove toxic comments. However, most automated systems -- when detecting and moderating toxic language -- do not provide feedback to their users, let alone provide an avenue
arxiv   +1 more source

Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks [PDF]

arXiv, 2023
The widespread dissemination of toxic online posts is increasingly damaging to society. However, research on detecting toxic language in Chinese has lagged significantly. Existing datasets lack fine-grained annotation of toxic types and expressions, and ignore the samples with indirect toxicity. In addition, it is crucial to introduce lexical knowledge
arxiv  

Fortifying Toxic Speech Detectors Against Veiled Toxicity [PDF]

arXiv, 2020
Modern toxic speech detectors are incompetent in recognizing disguised offensive language, such as adversarial attacks that deliberately avoid known toxic lexicons, or manifestations of implicit bias. Building a large annotated dataset for such veiled toxicity can be very expensive.
arxiv  

Twitter Users' Behavioral Response to Toxic Replies [PDF]

arXiv, 2022
Online toxic attacks, such as harassment, trolling, and hate speech have been linked to an increase in offline violence and negative psychological effects on victims. In this paper, we studied the impact of toxicity on users' online behavior. We collected a sample of 79.8k Twitter conversations.
arxiv  

Understanding the Bystander Effect on Toxic Twitter Conversations [PDF]

arXiv, 2022
In this study, we explore the power of group dynamics to shape the toxicity of Twitter conversations. First, we examine how the presence of others in a conversation can potentially diffuse Twitter users' responsibility to address a toxic direct reply.
arxiv  

Unveiling the Implicit Toxicity in Large Language Models [PDF]

arXiv, 2023
The open-endedness of large language models (LLMs) combined with their impressive capabilities may lead to new safety issues when being exploited for malicious use. While recent studies primarily focus on probing toxic outputs that can be easily detected with existing toxicity classifiers, we show that LLMs can generate diverse implicit toxic outputs ...
arxiv  

Toxic Comments Hunter : Score Severity of Toxic Comments [PDF]

arXiv, 2022
The detection and identification of toxic comments are conducive to creating a civilized and harmonious Internet environment. In this experiment, we collected various data sets related to toxic comments. Because of the characteristics of comment data, we perform data cleaning and feature extraction operations on it from different angles to obtain ...
arxiv