Suspicious tweet identification using machine learning approaches for improving social media marketing analysis

Abstract

Social media acts as one of the eminent platforms for communication. Twitter is one of the leading social media microblogging platforms, where users can post and interact. #Hashtags specify the tweeter trends on a certain topic. Currently, the hashtag value or trend ranking for a particular hashtag has been calculated based on the cumulative number of tweets. This type of cumulative amount of hashtag ranking may result in an anonymous intervention of irrelevant tweets, which affects social media marketing. The proposed approach uses the relevance of tweets and #hashtags to improve and identify the suspicious or irrelevant tweets of media marketing. The proposed research work uses the linear regression algorithm, which is one of the familiar machine learning approaches to explain the spam tweet generation and the method to identify. The test results found the proposed system has 84% of significance when compared to the market analysis algorithms.