Online social networks (OSNs) provide data valuable for a tremendous range of applications such as search engines and recommendation systems. However, the easy-to-use interactive interfaces and the low barriers to publication have exposed various information quality (IQ) problems, decreasing the quality of user-generated content (UGC) in such networks. The existence of a particular kind of ill-intentioned user, who are responsible for polluting OSNs’ content, imposes challenges to maintaining an acceptable level of IQ. Such kinds of users may misuse all services provided by social networks to pollute their content in an automated way. IQ problems that appear on OSNs can be categorized into three different forms, buzz, rumor and spam, where each of which has properties and goals differing from others. As a natural reaction and because of the marked failure of OSNs in uprooting these IQ problems, various detection and filtering methods have been designed by various researchers to address the three uncorrelated IQ problems. Hence, in this chapter, we discuss in detail three common negative phenomena appearing in OSNs with the main strengths and drawbacks of their detection systems. Then, we provide detailed research perspectives showing the future work of these OSN phenomena.