With the rapid growth of social networking sites for communicating, sharing, storing and managing significant information, it is attracting cybercriminals who misuse the Web to exploit vulnerabilities for their illicit benefits. Forged online accounts crack up every day. Impersonators, phishers, scammers and spammers crop up all the time in Online Social Networks (OSNs), and are harder to identify. Spammers are the users who send unsolicited messages to a large audience with the intention of advertising some product or to lure victims to click on malicious links or infecting user's system just for the purpose of making money. A lot of research has been done to detect spam profiles in OSNs. In this paper we have reviewed the existing techniques for detecting spam users in Twitter social network. Features for the detection of spammers could be user based or content based or both. Current study provides an overview of the methods, features used, detection rate and their limitations (if any) for detecting spam profiles mainly in Twitter.

The size of the WWW is increasing rapidly and its nature is dynamic, building an efficient search mechanism is very necessary. A vast number of pages continually being added every day, so fetching information about a special-topic is gaining importance, which poses exceptional scaling challenges for general-purpose crawlers and search engines. This paper describes a web crawling approach based on best first search. Instead of collecting and indexing all available web documents to be able to answer all possible queries, a focused crawler choose the links that are likely to be most relevant for the crawl, and avoids irrelevant links of the document. This leads to significant savings in hardware as well as network resources and also helps keep the crawl more up-to-date. To accomplish such goal-directed crawling, select top most K relevant documents for a given query and then expand the most promising link chosen according to link score, to circumvent irrelevant regions of the web. General TermsWeb Crawler, search engine, hyperlink.