Scalable Spam Classifier for Web Tables

Internet mail spam is a problem for most organizations and individuals. Spam can be classified into two categories: fraud and commercial. The fraud category includes phishing, scams, malware, counterfeit products and any other criminal activities. The commercial category includes promotional messages and newsletters that we do not want to receive, being sent illegally from legitimate organizations. Fraud can be seen as being a high threat with high volume while commercial spam is the opposite. Similar to mail, there are spam Web tables that do not have any useful content. Here we describe our machine-learning classifier for efficient and effective Web tables spam filtering that was tested on a large-scale Web tables corpus of approximate to 36 million tables.