Efficient Rumour Detection and Elimination Through Boosting Mining Performance

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K. Arulmozhi, R Ponnusamy


The aim behind the work is to find and eliminate the rumours that are evolving around the online social media. To eliminate the rumours the system uses recommendation system. The recommendation system acts in a sequential order the top most recommendation works first and one by one the rumours are detected and eliminated. While, performing both the rumour detection and recommendation system validation performance degradation may occur. At present, many deep-learning-based approaches are leveraged to locate rumours which are not effective. The previous method used a semi-supervised learning for detecting rumours that is proved to be easy but not effective in performance and speed. The paper leverages two alternative based algorithm 1) Adaboosting Algorithm 2) Rotation Forest Algorithm in order to increase the performance, efficiency and speed. The algorithm works in alternative basis that one algorithm demerits are solved by another one. Further, all the habits of the user are noted and their behaviours are recorded. In case, if any rumour detected the algorithms are assigned according to the user data’s requirement. The results of performance variation are deliberated accordingly. 


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