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A Review Paper On Cyber Harassment Detection Using Machine Learning Algorithm On Social Networking Website
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The advancement of social media plays an important role in increasing the population of youngsters on the web. And it has become the biggest medium of expressing oneĂąâŹâąs thoughts and emotions. Recent studies report that cyberbullying constitutes a growing problem among youngsters on the web. These kinds of attacks have a major influence on the current generationĂąâŹâąs personal and social life because youngsters are ready to adopt online life instead of a real one, which leads them into an imaginary world. So, we are proposing a system for early detection of cyberbullying on the web and comparing different machine learning Algorithms to obtain the optimal result. We are comparing four different algorithms which can be effectively used for the detection of cyberbullying, with the implementation of the bag of words algorithm with different n gram methods. Comparatively naĂÂŻve Bayes algorithm has the highest accuracy of 79 with trigram implementation of the bag of words algorithm. Rohini K R | Sreehari T Anil | Sreejith P M | Yedumohan P M "Comparative Study of Cyberbullying Detection using Different Machine Learning Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30765.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/30765/comparative-study-of-cyberbullying-detection-using-different-machine-learning-algorithms/rohini-k-r
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How To Start Writing Essay About Yourself. How To Sta
How To Start Writing Essay About Yourself. How To Sta
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Writing A Summary In 3 Steps Summary Writing, T
Writing A Summary In 3 Steps Summary Writing, T
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012 College Application Essay Examples About Yo
012 College Application Essay Examples About Yo
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Thanksgiving Writing Paper Free Printable - Printable F
Thanksgiving Writing Paper Free Printable - Printable F
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PPT - English Essay Writing Help Online PowerPoint Presentation, Fre
PPT - English Essay Writing Help Online PowerPoint Presentation, Fre
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Free Lined Writing Paper Printable - Printable Templ
Free Lined Writing Paper Printable - Printable Templ
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Descriptive Essay College Writing Tutor. Online assignment writing service.
Descriptive Essay College Writing Tutor. Online assignment writing service.
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9 Best Images Of Journal Writing Pap. Online assignment writing service.
9 Best Images Of Journal Writing Pap. Online assignment writing service.
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Divorce Agreement Template - Fill Out, Sign Online And
Divorce Agreement Template - Fill Out, Sign Online And
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Diversity On Campus Essay - Mfawriting515.Web.Fc2.Com
Diversity On Campus Essay - Mfawriting515.Web.Fc2.Com
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What Is Narrative Form Writing - HISTORYZA
What Is Narrative Form Writing - HISTORYZA
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College Admission Essays That Worked - The Oscillati
College Admission Essays That Worked - The Oscillati
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5 Steps To Write A Good Essay REssaysondemand
5 Steps To Write A Good Essay REssaysondemand
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Recently uploaded
IATP Guide
IATP How-to Foreign Travel May 2024.pdff
IATP How-to Foreign Travel May 2024.pdff
17thcssbs2
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Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
How to Split Bills in the Odoo 17 POS Module
How to Split Bills in the Odoo 17 POS Module
Celine George
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Clear articulation of the 21st century skills in the Matatag curriculum
Matatag-Curriculum and the 21st Century Skills Presentation.pptx
Matatag-Curriculum and the 21st Century Skills Presentation.pptx
JenilouCasareno
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This presentation provides an introduction to quantitative trait loci (QTL) analysis and marker-assisted selection (MAS) in plant breeding. The presentation begins by explaining the type of quantitative traits. The process of QTL analysis, including the use of molecular genetic markers and statistical methods, is discussed. Practical examples demonstrating the power of MAS are provided, such as its use in improving crop traits in plant breeding programs. Overall, this presentation offers a comprehensive overview of these important genomics-based approaches that are transforming modern agriculture.
Basic_QTL_Marker-assisted_Selection_Sourabh.ppt
Basic_QTL_Marker-assisted_Selection_Sourabh.ppt
Sourabh Kumar
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To provide details in philippine national symbols
Jose-Rizal-and-Philippine-Nationalism-National-Symbol-2.pptx
Jose-Rizal-and-Philippine-Nationalism-National-Symbol-2.pptx
ricssacare
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AttributeError in odoo is one of the most common errors in odoo which can be defined as the error raised when we try to access or assign an attribute that doesnât exist in the class of the object. In this slide we will discuss on how to fix object has no attribute error in odoo 17.
How to the fix Attribute Error in odoo 17
How to the fix Attribute Error in odoo 17
Celine George
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Incheon National University Capstone Design Final Presentation - Team Password 486 With Seungjun Rye(CSE), Eunbin Lee(BIZ). Jeonggyo Lee(ECON)
INU_CAPSTONEDESIGN_áá ”áá ”áŻáá „á«áá ©486_áá „ážá á ©áá łáá ጠáá ĄáŻáá áá Ąá á .pdf
INU_CAPSTONEDESIGN_áá ”áá ”áŻáá „á«áá ©486_áá „ážá á ©áá łáá ጠáá ĄáŻáá áá Ąá á .pdf
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This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Instructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptx
Jheel Barad
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UNIT â IV_PCI Complaints: Complaints and evaluation of complaints, Handling of return good, recalling and waste disposal.
UNIT â IV_PCI Complaints: Complaints and evaluation of complaints, Handling o...
UNIT â IV_PCI Complaints: Complaints and evaluation of complaints, Handling o...
Sayali Powar
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This presentation explores the advantages and obstacles associated with Open Educational Resources (OER).
Benefits and Challenges of Using Open Educational Resources
Benefits and Challenges of Using Open Educational Resources
dimpy50
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This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024. He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Introduction to Quality Improvement Essentials
Introduction to Quality Improvement Essentials
Excellence Foundation for South Sudan
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We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as âdistorted thinkingâ.
How to Break the cycle of negative Thoughts
How to Break the cycle of negative Thoughts
Col Mukteshwar Prasad
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In Odoo 17, the process of managing incoming and outgoing shipments is streamlined into two efficient steps, simplifying logistics management. This feature optimizes the workflow by reducing complexity and increasing productivity, ensuring smooth operations for businesses. With Odoo 17, users can easily track and manage their shipments from start to finish, improving overall efficiency and enhancing the customer experience.
Incoming and Outgoing Shipments in 2 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 2 STEPS Using Odoo 17
Celine George
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UNA PRESENTACION IMPECABLE DE PHRASAL VERBS
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Phrasal Verbs.XXXXXXXXXXXXXXXXXXXXXXXXXX
MIRIAMSALINAS13
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DEFINITION OF POLLUTION Environmental pollution Pollutants Types of Pollution Air Water Noise Land Pollution NAAQS AQI Level Central Pollution Control Boar Environment Act, 1986 Air Quality Index (AQI) Level Causes of Air Pollution Fossil Fuels Effects of Air Pollution Air Pollution Control Water Pollution & Types Causes of Water Pollution Standard Parameters drinking Effects of Water Pollution How to Avoid Water Pollution Causes of Noise Pollution Rainwater Harvesting Effects of Noise Pollution Prevention of Noise Pollution Definition of Land Pollution Causes of Land Pollution Prevention of Land Pollution Why is Rainwater Harvesting Objectives of Rainwater Harvesting Methods of Rainwater Harvesting Surface runoff harvesting Roof top rainwater harvesting
Basic Civil Engg Notes_Chapter-6_Environment Pollution & Engineering
Basic Civil Engg Notes_Chapter-6_Environment Pollution & Engineering
Denish Jangid
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The Author of this document is Dr. Abdulfatah A. Salem
Operations Management - Book1.p - Dr. Abdulfatah A. Salem
Operations Management - Book1.p - Dr. Abdulfatah A. Salem
Arab Academy for Science, Technology and Maritime Transport
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College Science, Business and Technology Quiz conducted at Government Engineering College, Thrissur on 4th May 2024
Gyanartha SciBizTech Quiz slideshare.pptx
Gyanartha SciBizTech Quiz slideshare.pptx
Shibin Azad
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A presentation about a famous track athlete
Sha'Carri Richardson Presentation 202345
Sha'Carri Richardson Presentation 202345
beazzy04
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Primer on the benefits and drawbacks of using OER.
Open Educational Resources Primer PowerPoint
Open Educational Resources Primer PowerPoint
ELaRue0
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ppt your views.ppt your views of your college in your eyes
ppt your views.ppt your views of your college in your eyes
ashishpaul799
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IATP How-to Foreign Travel May 2024.pdff
IATP How-to Foreign Travel May 2024.pdff
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How to Split Bills in the Odoo 17 POS Module
How to Split Bills in the Odoo 17 POS Module
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Matatag-Curriculum and the 21st Century Skills Presentation.pptx
Matatag-Curriculum and the 21st Century Skills Presentation.pptx
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Basic_QTL_Marker-assisted_Selection_Sourabh.ppt
Basic_QTL_Marker-assisted_Selection_Sourabh.ppt
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Jose-Rizal-and-Philippine-Nationalism-National-Symbol-2.pptx
Jose-Rizal-and-Philippine-Nationalism-National-Symbol-2.pptx
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How to the fix Attribute Error in odoo 17
How to the fix Attribute Error in odoo 17
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INU_CAPSTONEDESIGN_áá ”áá ”áŻáá „á«áá ©486_áá „ážá á ©áá łáá ጠáá ĄáŻáá áá Ąá á .pdf
INU_CAPSTONEDESIGN_áá ”áá ”áŻáá „á«áá ©486_áá „ážá á ©áá łáá ጠáá ĄáŻáá áá Ąá á .pdf
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Instructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptx
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UNIT â IV_PCI Complaints: Complaints and evaluation of complaints, Handling o...
UNIT â IV_PCI Complaints: Complaints and evaluation of complaints, Handling o...
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Benefits and Challenges of Using Open Educational Resources
Benefits and Challenges of Using Open Educational Resources
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Introduction to Quality Improvement Essentials
Introduction to Quality Improvement Essentials
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How to Break the cycle of negative Thoughts
How to Break the cycle of negative Thoughts
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Incoming and Outgoing Shipments in 2 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 2 STEPS Using Odoo 17
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Phrasal Verbs.XXXXXXXXXXXXXXXXXXXXXXXXXX
Phrasal Verbs.XXXXXXXXXXXXXXXXXXXXXXXXXX
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Basic Civil Engg Notes_Chapter-6_Environment Pollution & Engineering
Basic Civil Engg Notes_Chapter-6_Environment Pollution & Engineering
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Operations Management - Book1.p - Dr. Abdulfatah A. Salem
Operations Management - Book1.p - Dr. Abdulfatah A. Salem
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Gyanartha SciBizTech Quiz slideshare.pptx
Gyanartha SciBizTech Quiz slideshare.pptx
Â
Sha'Carri Richardson Presentation 202345
Sha'Carri Richardson Presentation 202345
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Open Educational Resources Primer PowerPoint
Open Educational Resources Primer PowerPoint
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ppt your views.ppt your views of your college in your eyes
ppt your views.ppt your views of your college in your eyes
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A Review Paper On Cyber Harassment Detection Using Machine Learning Algorithm On Social Networking Website
1.
10 X October
2022 https://doi.org/10.22214/ijraset.2022.45847
2.
International Journal for
Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue X Oct 2022- Available at www.ijraset.com 782 © IJRASET: All Rights are Reserved | SJImpact Factor 7.538 | ISRA Journal Impact Factor 7.894 | A Review Paper on Cyber Harassment Detection Using Machine Learning Algorithm on Social Networking Website Miss. Sneha Gajanan Sambare1 , Dr. Sanjay. L. Haridas2 1 PG Student, (M. Tech. Electronics Engineering), JD College of Engineering & Management 2 Professor, Department of Electronics & Telecommunication Engineering, JD College of Engineering & Management Abstract: Cyber bullying is nothing but bullying with the use of digital technology. Cyber bullying can take place on social media platforms, messaging platforms, gaming platforms and mobile phones etc. Cyber bullying is a big issue that is encountered by the individual on internet that affects teenagers as well as adults. It has lead to nuisances like suicide, mental health problems and depression. Therefore regulation of content on Social media platforms has become a growing need. Also detecting Cyber bullying detection at early stages can help to alleviate impacts on the victims. In the following research we make use of data from two different data sets. The first one is tweets from Twitter and second one is comments based on personal attacks from Wikipedia forums. The approach is to build a model based on detection of cyber bullying in text data using Natural Language Processing and Machine learning. We try to build a model which will provide accuracy up to 90% for tweets and accuracy up to 80% for Wikipedia forums. Cyber bullying detection will be done as a binary classification problem where we are detecting two major form of cyber bullying: hate speech on Twitter and Personal attacks on Wikipedia and classifying them as containing Cyber bullying or not. The end result of this proposed work may reduce negative impacts on the victims as a result of early detection. Keywords: Cyber bullying, Social Media, Machine Learning, I. INTRODUCTION With the development of internet, social media is trending these days. Everyone jokes with everyone. But the problem arises when someone or a group of people starts to joke at you instead of joking with you. More often than not online abuse turnout to be real life threat to the victim. Cyber bullying is repeated behavior which aimed at scaring, angering or shaming an individual even after being asked to stop by the aimed individual. Cyber bullying is common these days and can hurt the victim mentally, emotionally or physically or all of them at once. The feeling of being targeted can lead to people from sharing their problems and dealing with the problems. The victim can also turn towards drugs alcohol to deal with their mental and physical pain. Bullying via textual messages or pictures or videos on social media platforms has proven to be very harmful for adults. The term Bullying can be defined as an aggressive, intentional act that is carried out by a group or an individual against a victim who cannot easily defend him or herself repeatedly and over time even after being asked to stop. The problems associated with cyber bullying makes it compulsory to detect it before it turns out t be a crime. Since until cyber bullying is detected it canât be stopped. Hence for bullying to stop, it needs to be identified first and then it must be reported. Early detection will also helps in supporting the victim and to help him dealing with the problems arising due to bullying. In this project we will be building a model that will be composed of two data sets one from twitter data set âHate Speech Twitter Dataset by Waseem, Zeerak and Hovyâ that contains 17000 tweets labeled for sexism or racism and second one from Wikipedia Dataset The Wikipedia dataset by Wulczyn, Thain and Dixon that contains 1M comments labeled for Personal attacks. For the analysis 40000 comments are taken into consideration from the dataset from which 13000 comments are labeled as Cyber bullying due to personal attack. After collecting data set we will be training the system in order to detect bullying. This detection will be done as a binary classification problem. The algorithm used will be Natural Language Processing and Machine learning. As soon as bullying is detected it needs to be reported in order to stop it. The end result will implicate positive results on the victimâs mental and physical issues.
3.
International Journal for
Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue X Oct 2022- Available at www.ijraset.com 783 © IJRASET: All Rights are Reserved | SJImpact Factor 7.538 | ISRA Journal Impact Factor 7.894 | II. BACKGROUND Researches on Cyber bullying incidents show that 11.4% of 720 young people surveyed in the NCT DELHI were victims of cyber bullying in a 2018 survey by Child Right and You, an NGO in India, and almost half of them did not even report it to their teachers, parents or guardians as they were so terrified. 22.8% aged 13-18 who used the internet for around 3 hours a day were vulnerable to cyber bullying while 28% of people who use internet more than 4 hours a day were victims of cyber bullying. There are so many other reports suggested us that the impact of cyber bullying is affecting badly the people and children between ages of 13 to 20. People are facing so many difficulties in terms of health, mental fitness and their decision making ability in work and other areas of life. Researchers also suggest taking cyber bullying seriously on the national level and try to find solution to cyber bullying. Cyber bullying should be considered as national level issue. As it is known to most of us that in the year 2016 an incident called Blue Whale Challenge led to lots of child suicides in known as Blue Whale Challenge arises in Russia and other countries. The dangerous game that spread over different social networking websites and it was a one to one relationship between an administrator and a participant. For fifty days specific tasks were given to participants starting from easy ones and then leading to the fatal events. Initially they were easy like waking up at 4:30 AM or watching a horror movie and then it finally ends up in suicide attempts. Hence it is quite necessary that these kinds of incidents must be reported at prior stages so as to avoid the hazardous effect. III. MOTIVATION 1) To eradicate the evils existing on social media with the use of proper technology. 2) To help in identifying the crimes on social media and also identifying the criminals before the situation aggravates 3) Use of machine learning algorithms to detect the bullying on social media. 4) Existing methods are not proven to be very efficient methods of detection of cyber bullying so a system is must for countering cyber bullying. IV. PROBLEM STATEMENT 1) Cyber bullying is a new type of bullying that follows an individual from the entrance of their work place and back to their homes. 2) Individuals are bullied from the start of the morning till they reach their homes at night and go to bed for sleep. 3) Existing methods are applied only when bullying becomes an act of crime not before that. 4) So it is very necessary to detect and monitor the anti-social elements before they do a crime. V. LITERATURE REVIEW 1) Manuel F. LĂłpez-VizcaĂno, Francisco J. NĂłvoa, Victor Carneiro and Fidel Cacheda [16] proposed a two machine model which was a learning model for early detection of cyberbullying. They did the experiments using real world data set and following a time aware evaluation. 2) Syed Mahbub, Eric Pardede and A. S. M. Kayes [15] proposes analysis of the effects of predatory approach words in the detection of cyberbullying and proposes a mechanism for generation of a dictionary of such approach words. This was systematic approach design that takes the generation of keyword dictionary into consideration. 3) Patxi Galan-Garcia [2] proposed a hypothesis that a troll (cyber bully) on social networking sites under a fake profile always has a real profile to check how others view the fake profile. He proposed a machine learning approach to determine such profiles. The identification process studied some profiles that have some kind of close relationship to them. The method used was to use ML to select profiles to study, retrieve information about tweets, select features to be used from profiles, and find the author of the tweets. 1900 tweets from 19 different profiles were used. It had an accuracy of 68% for author identification. This was later used in a case study at a school in Spain, where cyberbullying had to find the real owner of the profile among some of the suspected students, and this method worked in the case. The following method still has some drawbacks. For example a case where the trolling account doesn't have a real account to fool the system or experts who can change the writing style and behavior so that no patterns are found. Changing writing style will require more efficient algorithms. 4) Kelly Reynolds, April Kontostathis and Lynne Edwards [6] proposed a Formspring (A forum for anonymous question answers) dataset which gives recall of 78.5% accuracy using Machine learning Algorithms and oversampling due to imbalance in cyber bullying posts. They used the labeled data, in conjunction with machine learning techniques which utilizes Weka tool kit, to train a computer to recognize harassment contents. 5) Maral Dadvar and Kai Eckert [8] trained deep neural networks on Twitter, Wikipedia, Formspring datasets and used the model on YouTube dataset and investigated the performance of their model in new social media platforms for the same and achieved F1 score of 0.97 using Bidirectional Long Short-Term Memory(BLSTM) model.
4.
International Journal for
Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue X Oct 2022- Available at www.ijraset.com 784 © IJRASET: All Rights are Reserved | SJImpact Factor 7.538 | ISRA Journal Impact Factor 7.894 | 6) Sweta Agrawal and Amit Awekar [9] used similar same datasets for training Deep Neural Networks they provided several useful insights about cyberbullying detection. Their proposed system was basically the first of its kind that systematically analyzes cyberbullying detection using deep learning based models and transfer learning. They resolved that how the vocabulary for such models changes across various Social Media Platforms. VI. PROPOSED SYSTEM Figure: Proposed System The proposed system consists of two datasets that we will be using to train our model. A. Twitter Dataset: The Twitter Dataset is combined from two datasets containing hate speech: Hate Speech Twitter Dataset by Waseem, Zeerak and Hovy, Dirk [11] which contains 17000 tweets labeled for sexism or racism. The tweets are mined using the annotations. 5900 tweets are lost due to accounts being deactivated or tweet deleted and second dataset of Hate Speech Language Dataset by Thomas Davidson, Dana Warmsley, Michael Macy and Ingmar Weber, [12]. It contained 25000 tweets obtained by crowd sourcing. This gives total 35787 tweets for the task distribution. For the following dataset, 70 % (25,050) of this dataset is used as training data and 30 percent as testing data (10,737). B.Wikipedia Dataset The Wikipedia dataset by Wulczyn, Thain and Dixon [13] contains 1M comments labeled for Personal attacks. For our research analysis 40000 comments are used from the databanks from which 13000 comments are labeled as cyber bullying due to smear campaign. These comments are extracted from conversations between editors of pages on Wikipedia labeled by 10 annotators via Crowd Flower. For this dataset the same split (70 percent i.e 28000 to training data and 30 percent i.e 12000 to testing data) is used. Most of the existing system techniques are manual, which includes human intervention and decision making. Also existing system looks for patterns that already exist in the data. To overcome these deficiencies we are making use of Support Vector Machine (SVM). SVM is a computer algorithm that assigns the labels to object based on examples. For instance, an SVM can learn to categorize fraudulent or no fraudulent credit card activities by examining hundreds or thousands of fraudulent and or no fraudulent credit card activity reports. SVM is basically used to plot a hyper plane that creates a boundary between data points in number of features (N)-dimensional space. Linear SVM is used in the following case which is optimum for linearly separable data. In case of 0 misclassification, i.e. the class of data point is accurately predicted by our model, we only have to change the gradient from the regularization arguments. A random forest consists of different individual decision trees which individually predict a class for given query points and the class with maximum votes will be the final result. Decision Tree is a building block for random forest which provides a prediction by decision rules learned from feature vectors. A group of these uncorrelated trees provides a more corrective decision for classification. First we will design a Python language based portal which will in which we will be training our system using the algorithm with the help of data sets. Python is highly readable language also it has fewer syntactical constructions than other languages. Using Python language based portal, the two data sets and SVM we will train our network and as the system is trained if we enter the keywords or group of words then the system will show whether the entered content is bullying or not. A flag or warning message will pop out to show whether the entered content is bullying or not. VII. CONCLUSION A larger part of cyberbullying detection aimed at routine detection of cyberbullying incidents on online platforms focuses on machine learning models to detect bullying. Due to ever changing nature of online platforms detection of such events can have multiple ways. Cyber bullying on the online platforms is very menacing and leads to unfortunate events like depression, mental and physical illness and even suicide. Hence there is a need to control its scattering.
5.
International Journal for
Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue X Oct 2022- Available at www.ijraset.com 785 © IJRASET: All Rights are Reserved | SJImpact Factor 7.538 | ISRA Journal Impact Factor 7.894 | This leads to important aspect of online activities that is cyber bullying detection on social media platforms. In this proposed system we presented an idea for detection of cyber bullying and if integrated with real time applications it will help in restricting abusers bullying. We demonstrated the build up for two types of data: Hate speech Data on Twitter and Personal attacks on Wikipedia. For Hate speech Natural Language Processing techniques proved effective having accuracy of over 90% using basic Machine learning algorithms. As a result it gives better results with BoW and TF-IDF models instead of Word2Vec models. However, personal attacks were difficult to detect through the model because of the fact that the comments usually did not use any common sentiment that could be used to train the network. Word2Vec models which utilizes context of features proved effective in both datasets giving like to like results in comparatively less features after combining with Multi Layered Perceptrons. Additional perspective can also be taken into consideration for more specific results in future works. The nature of attacks are required to be studied further for a more specific and accurate detection. REFERENCES [1] H. Ting, W. S. Liou, D. Liberona, S. L. Wang, and G. M. T. Bermudez, âTowards the detection of cyberbullying based on social network mining techniques,â in Proceedings of 4th International Conference on Behavioral, Economic, and SocioCultural Computing, BESC 2017, 2017. [2] P. GalĂĄn-GarcĂa, J. G. de la Puerta, C. L. GĂłmez, I. Santos, and P. G. Bringas, âSupervised machine learning for the detection of troll profiles in twitter social network: Application to a real case of cyberbullying,â 2014. [3] A. Mangaonkar, A. Hayrapetian, and R. Raje, âCollaborative detection of cyberbullying behavior in Twitter data,â 2015. [4] R. Zhao, A. Zhou, and K. Mao, âAutomatic detection of cyberbullying on social networks based on bullying features,â 2016. [5] V. Banerjee, J. Telavane, P. Gaikwad, and P. Vartak, âDetection of Cyberbullying Using Deep Neural Network,â 2019. [6] K. Reynolds, A. Kontostathis, and L. Edwards, âUsing machine learning to detect cyberbullying,â 2011. [7] J. Yadav, D. Kumar, and D. Chauhan, âCyberbullying Detection using Pre-Trained BERT Model,â 2020. [8] M. Dadvar and K. Eckert, âCyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study,â arXiv. 2018. [9] S. Agrawal and A. Awekar, âDeep learning for detecting cyberbullying across multiple social media platforms,â arXiv. 2018. [10] Y. N. Silva, C. Rich, and D. Hall, âBullyBlocker: Towards the identification of cyber bullying in social networking sites,â 2016. [11] Z. Waseem and D. Hovy, âHateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter,â 2016. [12] T. Davidson, D. Warmsley, M. Macy, and I. Weber, âAutomated hate speech detection and the problem of offensive language,â 2017. [13] E. Wulczyn, N. Thain, and L. Dixon, âEx machina: Personal attacks seen at scale,â 2017. [14] A. Yadav and D. K. Vishwakarma, âSentiment analysis using deep learning architectures: a review,â Artif. Intell. Rev., vol. 53, no. 6, 2020, doi: 10.1007/s10462- 019-09794-5. [15] âDetection of Harassment Type of Cyberbullying: A Dictionary of Approach Words and Its Impactâ Syed Mahbub, Eric Pardede and A. S. M. Kayes [16] âEarly detection of cyberbullying on social media networksâ Manuel F. LĂłpez-VizcaĂno, Francisco J. NĂłvoa, Victor Carneiro and Fidel Cacheda.
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