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@article{152097,
        author = {Karan Tailor and Pradnya Navale and Het Bhatt and Rutuja Nehul},
        title = {Social Network Shaming Text identification, inspection, reduction},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {2},
        pages = {459-465},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152097},
        abstract = {Twitter is a web social networking service that has quite 300 million users, generating an enormous amount of data daily. Twitter’s most significant feature is its ability for users to tweet about events, situations, feelings, opinions, or maybe something new in real-time. there's no system of accuracy or reliability in place: Anyone can say just anything. It is a simple thanks to attacking your detractors for them to attack, the type of Twitter war. during this survey, various applications of machine learning and hate speech detection to ease the detection of shammers and shamming tweets were included. With the rise of online social networks, and the growth of publicly shaming events, voices against the callousness of the positioning owners are growing stronger, there's a necessity to investigate the shaming tweets, classify shamming tweets into different categories, and mitigate them by blocking them.},
        keywords = {Shaming, Social Network, OSN network, Performance metrics},
        month = {},
        }