Unsolicited commercial e-mail (spam): integrated policy and practice

The internet offers a cost-effective medium to build better relationships with customers than has been possible with traditional marketing media. Internet technologies, such as electronic mail, web sites and digital media, offer companies the ability to expand their customer reach, to target specific communities, and to communicate and interact with customers in a highly customised manner. In the last few years, electronic mail has emerged as an important marketing tool to build and maintain closer relationships both with customers and with prospects. E-mail marketing has become a popular choice for companies as it greatly reduces the costs associated with previously conventional methods such as direct mailing, cataloguing (i.e. sending product catalogues to potential customers) and telecommunication marketing. As small consumers obtain e-mail addresses, the efficiency of using e-mail as a marketing tool will grow. While e-mail may be a boon for advertisers, it is a problem for consumers, corporations and internet service providers since it is used for sending 'spam' (junk-mail). Unsolicited commercial e-mail (UCE), which is commonly called spam, impinges on the privacy of individual internet users. It can also cost users in terms of the time spent reading and deleting the messages, as well as in a direct financial sense where users pay time-based connection fees. Spam, which most frequently takes the form of mass mailing advertisements, is a violation of internet etiquette (EEMA, 2002). This thesis shows that spam is an increasing problem for information society citizens. For the senders of spam, getting the message to millions of people is easy and cost-effective, but for the receivers the cost of receiving spam is financial, time-consuming, resource-consuming, possibly offensive or even illegal, and also dangerous for information systems. The problem is recognised by governments who have attempted legislative measures, but these have had little impact because of the combined difficulties of crossing territorial boundaries and of continuously evasive originating addresses. Software developers are attempting to use technology to tackle the problem, but spammers keep one step ahead, for example by adapting subject headings to avoid filters. Filters have difficulty differentiating between legitimate e-mail and unwanted e-mail, so that while we may reduce our junk we may also reduce our wanted messages. Putting filter control into the hands of individual users results in an unfair burden, in that there is a cost of time and expertise from the user. Where filter control is outsourced to expert third parties, solving the time and expertise problems, the cost becomes financial. Given the inadequacy of legislation, and the unreliability of technical applications to resolve the problem, there is an unfair burden on information society citizens. This research has resulted in the conclusion that cooperation between legislation and technology is the most effective way to handle and manage spam, and that therefore a defence in depth should be based on a combination of those two strategies. The thesis reviews and critiques attempts at legislation, self-regulation and technical solutions. It presents a case for an integrated and user-oriented approach, and provides recommendations

Link-based similarity search to fight web spam

www.ilab.sztaki.hu/websearch We investigate the usability of similarity search in fighting Web spam based on the assumption that an unknown spam page is more similar to certain known spam pages than to honest pages. In order to be successful, search engine spam never appears in isolation: we observe link farms and alliances for the sole purpose of search engine ranking manipulation. The artificial nature and strong inside connectedness however gave rise to successful algorithms to identify search engine spam. One example is trust and distrust propagation, an idea originating in recommender systems and P2P networks, that yields spam classificators by spreading information along hyperlinks from white and blacklists. While most previous results use PageRank variants for propagation, we form classifiers by investigating similarity top lists of an unknown page along various measures such as co-citation, companion, nearest neighbors in low dimensional projections and SimRank. We test our method over two data sets previously used to measure spam filtering algorithms. 1