Abstract: This project “Harmful Content Detection on Social Media Platform “is Python based project. It is designed using PYTHON/FLASK as front end and PHP as backend. The web application for the detection of offensive word is used to find the offensive Word and it performs a comparison between the words stored in the database and the words present in the text. The system then shows the user if any offensive words are detected. It shows the offensive and non-offensive words in graphical representation like chart, bar graph to find the presence of offensive word in the text. The proposed system is tested on a dataset of offensive words, and the results show that it can effectively detect offensive words in offline mode. Harmful Content Detection On Social Media Platforms implements our coded, machine learning algorithms, in finding a negative comment from the messages it receives by a user. The algorithm first gives the message a value and then based on our pre trained data, it decides if the comment is harsh enough to be transformed or not. It is assigned a value and if the value results in a positive sentence, the system will proceed to send the transformed positive sentence to the end user. Otherwise, the sentence will be placed through the models again. The users communicate through a developed web front face and they are connected to a central server. The users are termed as clients. If any messages are modified the receiving user will be notified along with the modified message. A major source of cyberbullying is social media. These platforms can have the opposite desired effect of uniting peers, and instead can be weaponized to harass and bully others. Most existing solutions have shown techniques/approaches to detect cyberbullying, but they are not freely available for end-users to use. They haven’t considered the evolution of language which makes a big impact on cyberbullying text. It doesn’t affect only for health, there are more different aspects which will lead life to a threat. Cyberbullying is a worldwide modern phenomenon which humans cannot avoid hundred percent but can be prevented.
Keywords: Harmful Content Detection, Social Media Moderation, Offensive Word Detection, Cyberbullying Prevention, Machine Learning Algorithms, Sentiment Analysis, Natural Language Processing (NLP), Flask Web Application, PHP Backend, Text Classification, Data Filtering, Content Moderation, Automated Censorship.
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DOI:
10.17148/IJARCCE.2025.14344
