Machine-learning approaches have been widely used for fault-proneness detection. Introduction of machine learning approaches induces development of new software metrics for fault-prone module detection. We have proposed an approach to detect fault-prone modules using the spam-filtering technique. To treat our approach as the conventional faultprone approaches, we summarize the output of spam-fi...

In recent years the volume of junk email (spam, virus etc.) has increased dramatically. These unwanted messages clutter up users’ mailboxes, consume server resources, and cause delays to the delivery of mail. This paper presents an approach that ensures that non-junk mail is delivered without excessive delay, at the expense of delaying junk mail. Using data from two Internet-facing mail servers...

In this paper, we study the performance of N-gram language models on classification tasks such as sentiment analysis and spam detection and evaluate the effect of prior probability estimates on the results. Our data is in the form of public online posts pertaining to fast fashion brands, from different social media channels (Twitter and Facebook). We propose a novel ensemble model based on the ...

This paper addresses the challenge of detecting spam URLs in social media, which is an important task for shielding users from links associated with phishing, malware, and other low-quality, suspicious content. Rather than rely on traditional blacklist-based filters or content analysis of the landing page for Web URLs, we examine the behavioral factors of both who is posting the URL and who is ...

We perform a statistical analysis of a large collection of Web pages, focusing on spam detection. We study several metrics such as degree correlations, number of neighbors, rank propagation through links, TrustRank and others to build several automatic web spam classifiers. This paper presents a study of the performance of each of these classifiers alone, as well as their combined performance. ...

Although opinion spam (or fake review) detection has attracted significant research attention in recent years, the problem is far from solved. One key reason is that there is no large-scale ground truth labeled dataset available for model building. Some review hosting sites such as Yelp.com and Dianping.com have built fake review filtering systems to ensure the quality of their reviews, but the...

Spam is commonly defined as unsolicited email messages, and the goal of spam filtering is to eliminate these messages. In this paper, we argue that not all unsolicited email messages are useless to recipients, and dropping all unsolicited messages is not optimal for either the recipient or the sender. Increasing the sender’s cost (with stamps, puzzles or tarpits) has been another approach aimed...

Today the major problem that the people are facing is spam mails or e-mail spam. In recent years there are so many schemes are developed to detect the spam emails. Here the primary idea of the similarity matching scheme for spam detection is to maintain a known spam database, formed by users feedback, to block the subsequent near-duplicate spam’s. We propose a novel e-mail abstraction scheme, w...

This paper analayzes different FTAA (Free Trade Area of the Americas) elements and the related economic, political, technological and social effects. We will demonstrate that, in Latin America, noncooperative or competitive equilibria do not solve the conflicts between FFTA supporters (the government and exporters) and detractors (social, religious and academic movements.) Finally, we will show...

Instant Messaging Spam Detection in Long Term Evolution Networks