Detection of Cyberbullying in Social Networks Using Machine Learning Methods

Altay, Elif Varol; Alataş, Bilal

doi:10.1109/ibigdelft.2018.8625321

Cited by 20 publications

(11 citation statements)

References 4 publications

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“…The algorithm of machine learning trains from the dataset that is provided to the algorithm. The algorithm learns how to identify the images through an iterative process and finally delivers the results [12]. Modern-day machine learning is a part of artificial intelligence and hence does not require any commands after the algorithm has been developed, and a training dataset has been provided to it.…”

Section: Methodsmentioning

confidence: 99%

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

Zhao

2021

Wireless Communications and Mobile Computing

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The archeological sites are a heritage that we have gained from our ancestors. These sites are crucial for understanding the past and the way of life of people during those times. The monuments and the immovable relics of ancient times are a getaway to the past. The critical cultural relics however actually over the years have faced the brunt of nature. The environmental conditions have deteriorated the condition of many important immovable relics over the years since these could not be just shifted away. People also move around the ancient cultural relics that may also deform these relics. The machine learning algorithms were used to identify the location of the relics. The data from the satellite images were used and implemented machine learning algorithm to maintain and monitor the relics. This research study dwells into the importance of the area from a research point of view and utilizes machine learning techniques called CaffeNet and deep convolutional neural network. The result showed that 96% accuracy of predicting the image, which can be used for tracking human activity, protects heritage sites in a unique way.

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Section: Methodsmentioning

confidence: 99%

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

Zhao

2021

Wireless Communications and Mobile Computing

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“…The whole process becomes even harder when dealing with online text that often includes miss-spellings, not commonly accepted abbreviations, different slangs, and short words. Regardless of the difficulties, researchers have applied different machine learning approaches to emotion and sentiments analysis [9], online harassment and cyberbullying prediction [10]- [12], crises response and emergency situation awareness [13], domestic violence crises prediction [14], etc. Automatic text classification consists of two different procedures and the first of these are feature engineering.…”

Section: Background a Automatic Text Classificationmentioning

confidence: 99%

“…These online platforms entice users on a promise to connect them to the rest of the world for ideas and information, however in hindsight, they inadvertently facilitate the spread of antisocial behaviour and putting a large number of users at risk. To detect and classify cyberbullying automatically, using machine learning has been attempted and accomplished by numerous studies [10], [59]- [62]. Similarly, online aggression has also been automatically detected [49], [63].…”

Section: Antisocial Behavior and Social Mediamentioning

confidence: 99%

Deep Learning for Multi-Class Antisocial Behavior Identification From Twitter

Singh

Subramani

et al. 2020

IEEE Access

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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.

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“…It is a new subject of study discovered by computer and data scientists. There are many social media and networking problems in the literature such as sentiment analysis [1], fake news detection [2], rumor detection [3], cyberbullying detection [4], customer satisfaction detection [5], link prediction [6], etc. The most important feature that distinguishes hate speech from other problems is that people who post on social networks think that they use their freedom of expression deliberately or unintentionally.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Ant Lion and Moth Flame Optimization-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks

Baydogan

Alataş

2021

IEEE Access

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In the online social networks, blogs, microblogs, social bookmarking services and sharing sites, and various web forum pages; the sharing of knowledge, opinions, ideas, etc. are spreading very quickly. This situation brings very dangerous problems in social networks. One of these problems is hate speech detection (HSD) problem which is covering issues such as insults, swearing, humiliation, discrimination, exclusion, detest, abhor, blast, damn, and intolerance. These can be reactions to a person, a group, an organization, an order, or an event. Although few machine learning methods have been used in the literature to solve this important problem in online social media, the performance of the HSD models in terms of many metrics needs to be increased. In this study, an automatic HSD system based on metaheuristic methodology was proposed for better results in this new and important problem. In the proposed optimization approach, Ant Lion Optimization (ALO) algorithm and Moth Flame Optimization (MFO) algorithm were designed for the HSD problem. This is the first attempt to use optimization algorithms as solution search strategies for automatic HSD. An efficient representation scheme and flexible fitness function were designed for this purpose. Many metrics can easily be embedded into the designed fitness function in order to be simultaneously optimized. Firstly, the basic natural language processing (NLP) steps were carried out. Feature extraction was performed using Bag of Words (BoW), Term Frequency (TF), and document vector (Word2Vec). Then, the performances of the proposed novel approaches were analyzed in detail on the three different real-world data. The obtained results were also checked against eight popular supervised machine learning algorithms, Social Spider Optimization (SSO) algorithm, and state-of-the-art Tunicate Swarm Algorithm (TSA). Considering the evaluation criteria for three sets of experiments, it was observed that the accuracy, sensitivity, precision, and f-score results of the ALO and MFO algorithms were superior to machine learning methods. As a result of the experimental studies, the highest accuracy value was 92.1% for ALO, while this value was 90.7% for MFO. Other numerical values obtained in the study were given in the experiments and results section with tables and graphics in detail. Due to the promising results of the proposed approaches, they are anticipated to be used in the solution of many social media and networking problems.INDEX TERMS Hate speech detection, metaheuristic optimization, natural language processing, social network analysis, text mining.

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Detection of Cyberbullying in Social Networks Using Machine Learning Methods

Cited by 20 publications

References 4 publications

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

Deep Learning for Multi-Class Antisocial Behavior Identification From Twitter

Metaheuristic Ant Lion and Moth Flame Optimization-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks

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