Perancangan Sistem Perangkat Lunak Anti Cyberbullying Berbasis Agen Sebagai Solusi Pencegahan Dan Penanganan Dampak Negatif Penggunaan Teknologi Internet

Penelitian tentang pembangunan sistem anti cyberbullying ini dibuat untuk memberikan solusi terhadap munculnya fenomena cyberbullying. Teknologi dapat digunakan untuk membantu korban bullying dengan cara mencegah konten bullying diakses dalam perangkat teknologi yang digunakan oleh pengguna, dan juga dapat mencegah pengguna untuk sebagai pelaku bullying. Teknologi yang digunakan untuk fungsi-fungsi tersebut adalah teknologi agen, beserta fungsi reporting yang dapat digunakan untuk melaporkan aksi-aksi bullying yang terjadi pada media sosial. Kegiatan penelitian difokuskan pada kegiatan analisa dan perancangan sistem, serta implementasi perangkat lunak anti cyberbullying berbasis teknologi agen. Perangkat lunak yang dibangun diharapkan dapat membantu mencegah dan mengatasi kejadian cyberbullying. Ada 2 sistem yang akan dirancang, yang pertama aplikasi untuk dijalankan pada PC desktop, dan memiliki kemampuan melakukan filterisasi terhadap konten bullying dari media sosial dan web, kemudian dapat memblok konten tersebut. Sistem juga dilengkapi dengan kemampuan untuk reporting sehingga dapat mengirimkan report ke pihak yang berkepentingan. Untuk sistem yang kedua adalah berbentuk aplikasi mobile dengan basis sistem operasi Android. Hasil akhir dari perancangan aplikasi ini adalah adanya sistem yang membuat orangtua dapat mengawasi penggunaan Internet oleh anak terutama dalam upaya pencegahan terhadap tinadanya sistem yang membuat orangtua dapat mengawasi penggunaan Internet oleh anak terutama dalam upaya pencegahan terhadap tindakan cyberbullying melalui media sosial

ROBUST SEARCH ENGINE TO IMPROVE THE SOCIAL SECURITY ISSUE

Cyber-bullying refers to the anonymous calling of any harassment that occurs through the web, mobiles, and other remote devices. Cyber-bullying takes the help of communication technologies to intentionally distort others through hostile behavior such as sending text messages and posting un-sensible or ugly comments on the Internet. The main definition of this phenomenon is derived from the concept of bullying. In this paper, current review of efforts in cyberbullying detection using web content mining techniques is presented [15].The proposed system effectively overcomes the drawbacks of existing. Also our main contribution is providing a robust search engine that improves the search pattern as well improves the social security issues. Also robust feature extraction improves the accuracy in detecting cyberbully

AN EFFECTIVE SYSTEM TO IMPROVE THE CYBERBULLYING

The rapid growth of social networking is supplementing the progression of cyberbullying activities. Most of the individuals involved in these activities belong to the younger generations,   especially teenagers, who in the worst scenario are at more risk of suicidal attempts. This propose an effective approach to detect cyberbullying messages from social media through a SVM classifier algorithm. This present ranking algorithm to access highest visited link and also provide age verification before access the particular social media. The experiments show effectiveness of our approach

Multilingual Cyberbullying Detection System

Indiana University-Purdue University Indianapolis (IUPUI)Since the use of social media has evolved, the ability of its users to bully others has increased. One of the prevalent forms of bullying is Cyberbullying, which occurs on the social media sites such as Facebook©, WhatsApp©, and Twitter©. The past decade has witnessed a growth in cyberbullying – is a form of bullying that occurs virtually by the use of electronic devices, such as messaging, e-mail, online gaming, social media, or through images or mails sent to a mobile. This bullying is not only limited to English language and occurs in other languages. Hence, it is of the utmost importance to detect cyberbullying in multiple languages. Since current approaches to identify cyberbullying are mostly focused on English language texts, this thesis proposes a new approach (called Multilingual Cyberbullying Detection System) for the detection of cyberbullying in multiple languages (English, Hindi, and Marathi). It uses two techniques, namely, Machine Learning-based and Lexicon-based, to classify the input data as bullying or non-bullying. The aim of this research is to not only detect cyberbullying but also provide a distributed infrastructure to detect bullying. We have developed multiple prototypes (standalone, collaborative, and cloud-based) and carried out experiments with them to detect cyberbullying on different datasets from multiple languages. The outcomes of our experiments show that the machine-learning model outperforms the lexicon-based model in all the languages. In addition, the results of our experiments show that collaboration techniques can help to improve the accuracy of a poor-performing node in the system. Finally, we show that the cloud-based configurations performed better than the local configurations

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection