A Framework for Spam Detection in Twitter Based on Recommendation System

Abstract

The rapidly growing online social networking sites have been infiltrated by a large amount of spam. Spammers are a particular kind of ill-intentioned users who degrade the quality of OSNs information through misusing all possible services provided by OSNs. Social spammers spread many intensive posts/tweets to lure legitimate users to malicious or commercial sites containing malware downloads, phishing, and drug sales. Given the fact that Twitter is not immune towards the social spam problem, different researchers have designed various detection methods, which inspect individual tweets or accounts for the existence of spam contents. Today, social networks are exposed to various threats that exploit their vulnerability. However, although of the high detection rates of the account-based spam detection methods, these methods are not suitable for filtering tweets in the real-time detection because of the need for information from Twitter's servers. At tweet spam detection level, many light features have been proposed for real-time filtering; however, the existing classification models separately classify a tweet without considering the state of previous handled tweets associated with a topic. First, they propose the identification of spam tweet by the security approach based on social honeypots and then they propose a method based on an algorithm "content filtering" in order to detect those that are similar to spam tweet detected by the approach of honeypots. Our approach has greatly improved the quality of abstraction in terms of performance and design. The algorithm is also fast and simple to implement. Experimental results show the stability and accuracy (over 99%), F-measure 98% of our approach.

Financial sentiment analysis of tweets based on deep learning approach

“…Also, current study findings show that utilizing a domain-specific lexicon produces better outcomes than using a general dictionary. The approach discussed above try to discover the similarity while utilizing the content filtering algorithm, which is why we can use recommendation systems to forecast tweets with a strong association with the trust domain and the set of tweets that are comparable in this context [12].…”