IRJET- Prevention of Cyber Bullying Using Machine Learning Approach (original) (raw)
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Automatic Detection and Prevention of Cyberbullying
The recent development of social media poses new challenges to the research community in analyzing online interactions between people. Social networking sites offer great opportunities for connecting with others, but also increase the vulnerability of young people to undesirable phenomena, such as cybervictimization. Recent research reports that on average, 20% to 40% of all teenagers have been victimized online. In this paper, we focus on cyberbullying as a particular form of cybervictimization. Successful prevention depends on the adequate detection of potentially harmful messages. However, given the massive information overload on the Web, there is a need for intelligent systems to identify potential risks automatically. We present the construction and annotation of a corpus of Dutch social media posts annotated with fine-grained cyberbullying-related text categories, such as insults and threats. Also, the specific participants (harasser, victim or bystander) in a cyberbullying conversation are identified to enhance the analysis of human interactions involving cyberbullying. Apart from describing our dataset construction and annotation, we present proof-of-concept experiments on the automatic identification of cyberbullying events and fine-grained cyberbullying categories.
IRJET, 2020
Increasing the use of Internet and facilitating access to online communities such as social media have led to the emergence of cybercrime. Cyberbullying is very common now a days. which have no tracking like it may harm any individual, business, society, country in past few days it seems that riots were happened due to some statement used by one community on another its important to identify such content which spreads hate or harm community text processing, NLP (natural language processing) is an emerging field with the help of NLP and machine learning algorithms such as naive bayes, random forest, SVM we are going to identify cyberbullying in twitter. Objectives of this implementation written in objective section. Image character with the help of OCR will be done by us to find image-based cyberbullying the impact on individual basis thus will be checked on dummy system. Machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. Index Terms-cyber bullying, natural language processing, machine learning algorithms, Social networking.
Automatic detection of cyberbullying in social media text
PloS one, 2018
While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a cyberbullying corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for the task. Experiments o...
Detection of Cyberbullying using Machine Learning
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Cyberbullying is a type of tormenting wherein technology is utilized as a medium to menace somebody. As the new blast of the web and other social media platforms are expanding, the quantity of users is additionally expanding and the primary users of online networking are for the most part adolescents and young adults. As much as these social media platforms are utilized for getting new data and for amusement, it is increasingly inclined for bullies to utilizes these systems as helpless against assaults against casualties. Because of the expansion in cyberbullying on casualties, it is deprived to build up an appropriate strategy for the identification and anticipation of cyberbullying. A developing assortment of work is rising on mechanized ways to deal with cyberbullying location. These methodologies use machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching Textual data. The primary goal of this task is to distinguish cyberbullying by coordinating both Image and Textual information. The test cases are utilized to characterize the dataset and distinguish the bullying. Machine learning techniques are utilized to proficiently anticipate and identify cyberbullying.
Identification of Cyberbullying in Social Media using Machine Learning
IJSREM, 2024
In the modern era, the usage of internet has increased tremendously which in turn has led to the evolution of large amount of data .Cyber world d has its own pros and cons. One of the alarming situations in web 4.0 is cyber bullying a type of cyber-crime. When the bullying occurs on line with the aid of technology it is known as cyber bullying. This research paper have surveyed the work done by 30 different researchers on cyber bullying, and elaborated on different methodologies adopted by them for the detection of bullying, and how you protect the community from online evil act of cyber bullying. Cyber-crimes involve all the crimes where internet is used as an access medium and committed through some electronic device such as computers and mobile phones. Unavailability of datasets, hidden identity of predators and the privacy of the victims are the main factors for limiting the past research in cyberbullying detection.
A machine learning approach towards social media to tackle cyberbullying
The prevalence of social media is expanding step by step y. People of all age group are terribly interested in social networking. Social media connects people from different parts of the world. However, social media may have some side effects such as cyber bullying, which may have negative impacts on the life of people. Research shows that children and teenagers are the main victims of this cyber attack. Through the social media, people share their thoughts and emotions with their friends. There are large numbers of fraud accounts in social media. Cyber bullying is when someone, harass others on social media sites. Some people use it for cyber attack by making negative comments on others post. One way to tackle this problem is to detect those bullying messages and encrypt it. Machine learning techniques make automatic detection of cyber bullying messages. Weka is a powe full machine learning tool which can be used for this purpose. A combination of classification and lexical algorithms can detect whether a message is bullying or not. Cyber bullying is a major problem in society since social media has its presence in all fields of modern man's life.
ArXiv, 2018
The research described in this paper concerns automatic cyberbullying detection in social media. There are two goals to achieve: building a gold standard cyberbullying detection dataset and measuring the performance of the Samurai cyberbullying detection system. The Formspring dataset provided in a Kaggle competition was re-annotated as a part of the research. The annotation procedure is described in detail and, unlike many other recent data annotation initiatives, does not use Mechanical Turk for finding people willing to perform the annotation. The new annotation compared to the old one seems to be more coherent since all tested cyberbullying detection system performed better on the former. The performance of the Samurai system is compared with 5 commercial systems and one well-known machine learning algorithm, used for classifying textual content, namely Fasttext. It turns out that Samurai scores the best in all measures (accuracy, precision and recall), while Fasttext is the sec...
Detection of Cyberbullying on Social Media using Machine Learning
IRJET, 2022
With the rise of the Internet, the usage of social media has increased tremendously, and it has become the most influential networking platform in the twenty-first century. However, increasing social connectivity frequently causes problems. Negative societal effects that add to a handful of disastrous outcomes online harassment, cyberbullying, and other phenomena Online trolling and cybercrime Frequently, cyberbullying leads to severe mental and physical distress, especially in women and children, forcing them to try suicide on occasion. Because of its harmful impact, online abuse attracts attention. Impact on society Many occurrences have occurred recently all across the world. Internet harassment, such as sharing private messages, spreading rumors, etc., and Sexual comments As a result, the detection of bullying texts or messages on social media has grown in popularity. The data we used for our work were collected from the website kaggle.com, which contains a high percentage of bullying content. Electronic databases like Eric, ProQuest, and Google Scholar were used as the data sources. In this work, an approach to detect cyberbullying using machine learning techniques. We evaluated our model on two classifiers SVM and Neural Network, and we used TF-IDF and sentiment analysis algorithms for features extraction. This achieved 92.8% accuracy using Neural Network with 3-grams and 90.3% accuracy using SVM with 4-grams while using TF-IDF and sentiment analysis.
Cyberbullying Detection using Natural Language Processing
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Around the world, the use of the Internet and social media has increased exponentially, and they have become an integral part of daily life. It allows people to share their thoughts, feelings, and ideas with their loved ones through the Internet and social media. But with social networking sites becoming more popular, cyberbullying is on the rise. Using technology as a medium to bully someone is known as Cyberbullying. The Internet can be a source of abusive and harmful content and cause harm to others. Social networking sites provide a great medium for harassment, bullies, and youngsters who use these sites are vulnerable to attacks. Bullying can have long-term effects on adolescents' ability to socialize and build lasting friendships Victims of cyberbullying often feel humiliated. social media users often can hide their identity, which helps misuse the available features. The use of offensive language has become one of the most popular issues on social networking. Text containing any form of abusive conduct that displays acts intended to hurt others is offensive language. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and sometimes forces them to commit suicide. The purpose of this project is to develop a technique that is effective to detect and avoid cyberbullying on social networking sites we are using Natural Language Processing and other machine learning algorithms. The dataset that we used for this project was collected from Kaggle, it contains data from Twitter that is then labeled to train the algorithm. Several classifiers are used to train and recognize bullying actions. The evaluation of the proposed Model for cyberbullying dataset shows that Logistic Regression performs better and achieves good accuracy than SVM, Ransom forest, Naive-Bayes, and Xgboost algorithm.
Implementation of Cyberbullying Detection using Machine Learning Techniques
International Journal for Research in Applied Science & Engineering Technology, 2021
Cyberbullying could be an upsetting on-line wrongdoing with disturbing consequences. It seems in several forms, and in most of the social networks, it's in text format. Automatic detection of such incidents needs intelligent systems. Most of the prevailing studies have approached this drawback with typical machine learning models and therefore the majority of the developed models in these studies are applicable to one social network at a time. In recent studies, deep learning primarily based models have found their means within the detection of cyberbullying incidents, claiming that they will overcome the restrictions of the standard models, and improve the detection performance. Cyberbullying is that the use of technology as a medium to bully somebody. Though it's been a difficulty for several years, the popularity of its impact on teenagers has recently inflated. Social networking sites offer a fertile medium for bullies, and youths and young adults UN agency use these sites are susceptible to attacks. Through machine learning, we are going to realize language patterns utilised by bullies and their victims, and develop rules to automatically realize cyberbullying content. We find that our approach is with success able to determine vital variations between cyberbullying and regular media sessions, and supply a performance increase in cyberbullying detection. This paves the means for a lot of nuanced work on the utilization of temporal modelling to find and mitigate the incidence of cyberbullying. I. INTRODUCTION Day to day life is entirely or in a way dependent on the advent of internet. Thus, cyberbullying has been a major worry. With the advancement in technology, the internet has been a safe and secure sphere of communication, though the arena of social media has been prone to cybercrimes. Since the social lifestyle surpass the physical barrier of human interaction and affords inappropriate interaction with unknown people, it is important to analyse and study the domain of cyberbullying. Moreover, a well-specified law framework for cyberbullying has not been enforced in majority of the countries, therefore the data to defend the matter is unsure. Cyberbullying are often outlined because the use of a web communication to bully or harass an individual World Health Organization doesn't have potential to react [1], usually by causation messages of a threating or discouraging nature. It is evident that around 87 percent of the today's youth have witnessed some form of cyberbullying [2]. One of the rare reports [3] on cyberbullying states that 60 percent of Gulf Countries' youth overtly admit the presence of cyberbullying amongst their peers. This study conjointly states that solely quarter the predators on-line do bully their victims offline. This means that net have impressed three quarters of the predators to bully others, whereas they wouldn't have thought-about bullying physically. Cyberbullying can take various forms like Sexual Harassment, Hostile Environment, Racism, Revenge, and Retaliation. Since the offender is hidden to the victim, the problem statement gets complex. Effects of cyberbullying can range from temporary anxiety to suicide [6]. This is the reason cyberbullying is an interesting field of research. The adverse result of a cybercrime are often drastic-Cyberbullying was powerfully connected dangerous thinking compared with ancient bullying (JAMA pediatrics, 2014) [4], Hence, the requirement for a good system to spot cyberbullying and relieve the plight of distressed users. Since, cyberbullying can take place without the direct confrontation of the perpetrator, it is lot more vulnerable. Moreover, the most vicious state of bullying is that it can take place across social networks which were previously unreachable. Thus, with the proliferation of social media and web access, the act of cyberbullying too has inflated manifold. Twitter is one amongst the foremost lauded and widespread social media existing. It allows users to send and browse 140-character message. It is astonishing that about 330 million active users access the platform and nearly 500 million tweets are exchanged a day. Since about 80 percent of the user's access with their mobile phones, it has been an arena of real-time communication. A study determined that Twitter is turning into a cyberbullying playground (Xuetal, 2012). In this analysis, we tend to tend to utilize this important information and knowledge within the kind of tweets to enhance the prevailing cyberbullying detection performance. Since, Twitter is very user-friendly it enables the use of extended features like network, activity, user and tweet content, to train our detection model and improve its performance.