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With the astronomical growth of Online Social Networks (OSN), they have become the new target of many cyber criminals like spammers and phishers and many advertisers which have resulted in worrying issues. These issues range from low-quality content to phishing and frauds. Rumor diffusion is another problem causing serious social issues. Since information can propagate much faster than ever on OSN, the negative impact of rumors is thus much worse. However, we would not stop using OSN to interact with our friends and acquaintances, to share news and information, and to take part in other interesting online activities just because of the issues it may cause. As a matter of fact, with the content collected from OSN, data analysts would be able to predict box office, terrorism and even the stock price and a lot of other interesting topics. OSN is like a double-edged sword. Therefore it is necessary to reduce the negative effect of it and benefit as many individuals and organizations as possible. In this thesis, the author carries out research on making detection and prediction tasks more accurate through mining the different aspects of the content collected from OSN. Detection techniques of malicious content like spam and phishing on OSN are common while in contrast little attention is paid to other low-quality content which actually impacts user browsing experience most. The author proposes a framework to detect low-quality content from the users' perspective in real time. Based on preliminary studies, a survey is carefully designed to gather users' opinions on different categories of low-quality content. Both direct and indirect features …