Conference Papers

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    Child online safety in indian context
    (Institute of Electrical and Electronics Engineers Inc., 2020) Andrews, D.; Alathur, S.; Chetty, N.; Kumar, V.
    Children initiates the usage of Internet during young age and spend more time online. Apart from the benefits like improved education, entertainment, news and gaming, Internet poses severe threats to the children online. Ensuring online safety is a global challenge. The purpose of this paper is to examine online social media responses and awareness posts on children online safety. In this relation, Twitter social media responses after freeing the accusers of children sexual harassment and Facebook pages of some prominent personalities in India for online safety are analyzed. The results reveal that though the people are angry and fearful, they believe judiciary and police system and expecting safety from the same. The analysis of Facebook posts depicts that the concerned authorities are active towards child online safety and providing awareness through their representatives. People demand legal actions against the perpetrators of the crime to punish them. The necessary actions should be taken for cyber-crime awareness information to reach all social media users. © 2020 IEEE.
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    Analysis of Tweets for Cyberbullying Detection
    (Institute of Electrical and Electronics Engineers Inc., 2023) Mathur, S.A.; Isarka, S.; Dharmasivam, B.; Jaidhar, C.D.
    Cyberbullying takes place online on gadgets like smartphones and computers. Cyberbullying can occur through social media platforms. This paper presents a real-time cyber-bullying detection system for Twitter using Natural Language Processing (NLP) and Machine Learning (ML). The system is trained on a dataset of cyberbullying tweets using several ML algorithms and their performance is compared. Random Forest was found to provide the best results after tuning. To achieve real-time analysis, Selenium was used to scrape tweets from a given Twitter account and store the timestamp of the already checked tweets. Additionally, an image captioning model was employed to generate descriptions for images posted on the account and compare them with user-written captions to filter out spam tweets. The proposed work aims to prevent cyberbullying and provides a valuable tool for online platforms to detect and remove harmful content. The results of this study have shown that the selection of appropriate ML algorithms and preprocessing techniques significantly impact the performance of cyberbullying detection on Twitter. Our model sheds light on the appropriateness of different ML algorithms for the detection of cyberbullying. © 2023 IEEE.