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• W4285163982 abstract "In contrast to traditional media, social media is populated by anonymous individuals who have the freedom to broadcast whatever they choose. This online social media culture is dynamic, and the move from traditional media to digital media is growing increasingly popular among people. While conventional media will continue to be used less frequently in the future, the increasing use of Online Social Networks will obfuscate the real information provided by traditional media. Genuine users provide material benefits to the broader public; on the other hand, spammers transmit irrelevant or misleading content that turns social media into a front for spreading false information. A precise statistical classification for news is not possible with the current systems because of data limitations and communication types. We will look at a variety of research publications that employ various strategies for master training in the prediction and detection of harmful material on social media websites and networks. This study attempted to identify spam tweets from an extensive collection of tweets by utilizing TVC Algorithm to identify them. When a content paper is incorporated in this manner, a summary produced by utilizing the most important keywords from the first document is known as summarization. It is necessary to have a dynamic approach to dealing with the condensed information supplied through Twitter feeds. This research presents a novel way to produce a significant substance-based summery in a shorter period than previously available. We also propose to detect harmful tweets both offline and online and to do so in real-time. Most notably, compared to the other current frameworks, the proposed framework performs multi-subject summarization on an online dataset, which reduces the amount of time required." @default.
• W4285163982 created "2022-07-14" @default.
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• W4285163982 date "2022-01-01" @default.
• W4285163982 modified "2023-09-26" @default.
• W4285163982 title "Development and Implementation of Tweet Stream Summarization Technique for Pernicious Tweet Detection" @default.
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• W4285163982 doi "https://doi.org/10.1007/978-981-19-2719-5_45" @default.
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