Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks

• URL: http://arxiv.org/abs/2203.02123v1
• Date: Fri, 4 Mar 2022 03:57:18 GMT
• Title: Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks
• Authors: Zhenxiong Miao, Xingshu Chen, Haizhou Wang, Rui Tang, Zhou Yang, Wenyi Tang
• Abstract summary: We propose an end-to-end method based on community structure and text features for offensive language detection (CT-OLD) We add user opinion to the community structure for representing user features. The user opinion is represented by user historical behavior information, which outperforms that represented by text information.
• Score: 7.723697303436006
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The pervasiveness of offensive language on the social network has caused adverse effects on society, such as abusive behavior online. It is urgent to detect offensive language and curb its spread. Existing research shows that methods with community structure features effectively improve the performance of offensive language detection. However, the existing models deal with community structure independently, which seriously affects the effectiveness of detection models. In this paper, we propose an end-to-end method based on community structure and text features for offensive language detection (CT-OLD). Specifically, the community structure features are directly captured by the graph attention network layer, and the text embeddings are taken from the last hidden layer of BERT. Attention mechanisms and position encoding are used to fuse these features. Meanwhile, we add user opinion to the community structure for representing user features. The user opinion is represented by user historical behavior information, which outperforms that represented by text information. Besides the above point, the distribution of users and tweets is unbalanced in the popular datasets, which limits the generalization ability of the model. To address this issue, we construct and release a dataset with reasonable user distribution. Our method outperforms baselines with the F1 score of 89.94%. The results show that the end-to-end model effectively learns the potential information of community structure and text, and user historical behavior information is more suitable for user opinion representation.

Related papers

• LGB: Language Model and Graph Neural Network-Driven Social Bot Detection [43.92522451274129]
  Malicious social bots achieve their malicious purposes by spreading misinformation and inciting social public opinion. We propose a novel social bot detection framework LGB, which consists of two main components: language model (LM) and graph neural network (GNN) Experiments on two real-world datasets demonstrate that LGB consistently outperforms state-of-the-art baseline models by up to 10.95%.
  arXiv  Detail & Related papers  (2024-06-13T02:47:38Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs [0.0]
  This paper first constructs a user opinion dataset called ITRC-Opinion in a collaborative environment and insource way. Our dataset contains 60,000 informal and colloquial Persian texts from social microblogs such as Twitter and Instagram. Second, this study proposes a new architecture based on the convolutional neural network (CNN) model for more effective sentiment analysis of colloquial text in social microblog posts.
  arXiv  Detail & Related papers  (2023-06-22T05:51:22Z)
• Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation [74.38561925376996]
  Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios. An effective semantic correction algorithm is proposed by mining the inference rule from the knowledge graph. For the multi-user cognitive semantic communication system, a message recovery algorithm is proposed to distinguish messages of different users.
  arXiv  Detail & Related papers  (2023-03-15T12:01:43Z)
• Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional Networks [0.5457150493905063]
  We address the problem of offensive language detection on Twitter. We propose a novel approach called SyLSTM, which integrates syntactic features in the form of the dependency parse tree of a sentence. Results show that the proposed approach significantly outperforms the state-of-the-art BERT model with orders of magnitude fewer number of parameters.
  arXiv  Detail & Related papers  (2022-05-26T05:27:50Z)
• Span Classification with Structured Information for Disfluency Detection in Spoken Utterances [47.05113261111054]
  We propose a novel architecture for detecting disfluencies in transcripts from spoken utterances. Our proposed model achieves state-of-the-art results on the widely used English Switchboard for disfluency detection.
  arXiv  Detail & Related papers  (2022-03-30T03:22:29Z)
• A New Generation of Perspective API: Efficient Multilingual Character-level Transformers [66.9176610388952]
  We present the fundamentals behind the next version of the Perspective API from Google Jigsaw. At the heart of the approach is a single multilingual token-free Charformer model. We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings.
  arXiv  Detail & Related papers  (2022-02-22T20:55:31Z)
• Relational Graph Neural Networks for Fraud Detection in a Super-App environment [53.561797148529664]
  We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
  arXiv  Detail & Related papers  (2021-07-29T00:02:06Z)
• GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph [53.70520466556453]
  We propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph.
  arXiv  Detail & Related papers  (2021-05-06T12:20:41Z)

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.