New classification models for detecting Hate and Violence web content
By Shuhua Liu, Thomas Forss. 2015
45 citations recorded by the source corpus.
Today, the presence of harmful and inappropriate content on the web still remains one of the most primary concerns for web users. Web classification models in the early days are limited by the methods and data available. In our research we revisit the web classification problem with the application of new methods and techniques for text content analysis. Our recent studies have indicated the promising potential of combing topic analysis and sentiment analysis in web content classification. In this paper we further explore new ways and methods to improve and maximize classification performance, especially to enhance precision and reduce false positives, thorough examination and handling of the issues with class imbalance, and through incorporation of LDA topic models.
Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yulia Tsvetkov
- Vidhisha Balachandran
- Sachin Kumar
- Antonios Anastasopoulos
- Lucille Njoo
- Mohit Bansal
- Anjalie Field
- Hinrich Schütze
In Information Systems
In Natural Language Processing
- Yulia Tsvetkov
- Sachin Kumar
- Vidhisha Balachandran
- Antonios Anastasopoulos
- Lucille Njoo
- Anjalie Field
- Hinrich Schütze
- Mohit Bansal
In Safety, Robustness & Interpretability
Citations in the corpus
- Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, methodology): “Application Level Interventions Feature-based Detection Toxicity Lexical features (Xiang et al., 2012; Dadvar et al., 2012; Burnap and Williams, 2015; Liu and Forss, 2015); n-gram features (Chen et al.”