Deep Learning for Multi-Class Antisocial Behavior Identification From Twitter

Abstract

Social Media has become an integral part of our daily life. Not only it enables collaboration and flow of information but has also become an imperative tool for businesses and governments around the world. All this makes a compelling case for everyone to be on some sort of online social media platform. However, this virtuousness is overshadowed by some of its shortcomings. The manifestation of antisocial behaviour online is a growing concern that hinders participation and cultivates numerous social problems. Antisocial behaviour exists in its various forms such as aggression, disregard for safety, lack of remorse, unlawful behaviour, etc. The paper introduces a deep learning-based approach to detect and classify online antisocial behaviour (ASB). The automatic content classification addresses the issue of scalability, which is imperative when dealing with online platforms. A benchmark dataset was created with multi-class annotation under the supervision of a domain expert. Extensive experiments were conducted with multiple deep learning algorithms and their superior results were validated against the results from the traditional machine learning algorithms. Visually enhanced interpretation of the classification process is presented for model and error analyses. Accuracy of up to 99% in class identification was achieved on the ground truth dataset for empirical validation. The study is an evidence of how the cutting-edge deep learning technology can be utilized to solve a real-world problem of curtailing antisocial behaviour, which is a public health threat and a social problem.

Investigation of Imbalanced Sentiment Analysis in Voice Data: A Comparative Study of Machine Learning Algorithms

“…Employing machine learning and deep learning techniques, [27] successfully identifies antisocial behavior on Twitter. It leverages natural language processing to proactively detect such behavior, using a dataset labeled for this purpose.…”

Antisocial Behavior Monitoring Services of Indonesian Public Twitter Using Machine Learning

“…Text classification automatically consists of two stages, namely feature engineering and label prediction (Singh et al, 2020). Feature engineering is the process of extracting features from the input data and its vector number representation.…”

“…Feature engineering is the process of extracting features from the input data and its vector number representation. Some of the feature engineering techniques that are usually used for text classification are Term Frequency Inverse Document Frequency (TF-IDF), Bag-of-Words, topic modeling features, Psycholinguistic features, Sentiment lexicon features, Word n-grams, and Word Frequency (Singh et al, 2020). The next stage is label prediction where at this stage the machine learning model is trained on a benchmark data set that is extracted and annotated features are performed, which is also known as the ground truth dataset.…”

“…Antisocial behavior is a mental health disorder that has several characteristics that are often shown in someone who suffers from the disorder. Characteristics of antisocial behavior include repeated actions that violate social norms, deceit and lying, impulsiveness, irritability and aggression, reckless disregard for the safety of oneself and others, consistently irresponsible, and lack of remorse (Singh, 2020). In addition, there are several other characteristics such as stealing, lying, lack of remorse towards other people and living beings, irresponsible behavior, impulsive behavior, abuse of alcohol or drugs, violating the law, violating the rights of others and aggressive behavior (Nuryati and Kresnowati, 2018)).…”

Enhancing Hate Speech Detection in the Digital Age: A Novel Model Fusion Approach Leveraging a Comprehensive Dataset

“…Relying solely on human moderators for precise and timely identification of hate speech is impractical, necessitating the use of advanced natural language processing (NLP) algorithms and machine learning models. The NLP community has recently made significant progress in developing hate speech identification systems, with machine learning and particularly deep learning techniques demonstrating superior effectiveness [1], [4], [5], [6], [7], [8]. Deep learning is particularly valuable in swiftly identifying hate speech, as it analyzes language and behavioral patterns linked to hate speech.…”