A Survey On Hybrid Recommendation System And Algorithms For Rumour Detection
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Abstract
In online-social networking, rumours and emotions play a vital role in judging and deciding everything. Rumours and Comments are considered to be the reaction/opinions of public. Not all the comments that circulates the social media are trustworthy. Nowadays, even a post is randomly shared for publicity purpose which are not true and relevant. Several social media uses small user groups which is also a major concern. Group-recommendation system has become highly demanded, where the users empathize in the forms of group activities in social media. Also, when there are several new attacks detected, where the attackers use the comment section for injecting false or biased information. Some of these unwanted or false rumours bring a chaos in people's mind to decide what is good and what is bad. In existing system lots of machine learning algorithms were used to detect and stop the rumours. As the attacks and rumours count has increased massively in recent times it is hard for the other MLA (Machine learning algorithms) to detect and eliminate the rumours. A deep survey on how group recommendation can be used and how rumours are detected, analyzed, compared and listed.
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