Survey on Web Spam Detection using Link and Content Based Features

Web spam is one of the recent problems of search engines because it powerfully reduced the quality of the Web page. Web spam has an economic impact because spammers provide a large free advertising data or sites on the search engines and so an increase in the web traffic volume. In this paper we Survey on efficient spam detection techniques based on a classifier that combines new link based features with language models. Link Based features are related to qualitative data extracted from the web pages and also to the qualitative properties of the page links. Spam technique applies LM approach to different sources of information from a web page that belongs to the context of a link in order to provide high quality indicators of web spam. Specifically Detection technique applied the Kullback Leibler divergence on different combinations of these sources of information in order to characterize the relationship between two linked pages

Investigating and Validating Scam Triggers: A Case Study of a Craigslist Website

The internet and digital infrastructure play an important role in our day-to-day live, and it has also a huge impact on the organizations and how we do business transactions every day. Online business is booming in this 21st century, and there are many online platforms that enable sellers and buyers to do online transactions collectively. People can sell and purchase products that include vehicles, clothes, and shoes from anywhere and anytime. Thus, the purpose of this study is to identify and validate scam triggers using Craigslist as a case study. Craigslist is one of the websites where people can post advertising to sell and buy personal belongings online. However, with the growing number of people buying and selling, new threats and scams are created daily. Private cars are among the most significant items sold and purchased over the craigslist website. In this regard, several scammers have been drawn by the large number of vehicles being traded over craigslist. Scammers also use this forum to cheat others and exploit the vulnerable. The study identified online scam triggers including Bad key words, dealers’ posts as owners, personal email, multiple location, rogue picture and voice over IP to detect online scams that exists in craigslist. The study also found over 360 ads from craigslist based on our scam trigger. Finally, the study validated each and every one of the scam triggers and found 53.31% of our data is likelihood to be considered as a scam

Semisupervised ranking on very large graphs with rich metadata

ABSTRACT Graph ranking plays an important role in many applications, such as page ranking on web graphs and entity ranking on social networks. In applications, besides graph structure, rich information on nodes and edges and explicit or implicit human supervision are often available. In contrast, conventional algorithms (e.g., PageRank and HITS) compute ranking scores by only resorting to graph structure information. A natural question arises here, that is, how to effectively and efficiently leverage all the information to more accurately calculate graph ranking scores than the conventional algorithms, assuming that the graph is also very large. Previous work only partially tackled the problem, and the proposed solutions are also not satisfying. This paper addresses the problem and proposes a general framework as well as an efficient algorithm for graph ranking. Specifically, we define a semi-supervised learning framework for ranking of nodes on a very large graph and derive within our proposed framework an efficient algorithm called Semi-Supervised PageRank. In the algorithm, the objective function is defined based upon a Markov random walk on the graph. The transition probability and the reset probability of the Markov model are defined as parametric models based on features on nodes and edges. By minimizing the objective function, subject to a number of constraints derived from supervision information, we simultaneously learn the optimal parameters of the model and the optimal ranking scores of the nodes. Finally, we show that it is possible to make the algorithm efficient to * This work was performed when the third author was an intern at Microsoft Research Asia. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. handle a billion-node graph by taking advantage of the sparsity of the graph and implement it in the MapReduce logic. Experiments on real data from a commercial search engine show that the proposed algorithm can outperform previous algorithms on several tasks

Few are as Good as Many: An Ontology-Based Tweet Spam Detection Approach

Due to the high popularity of Twitter, spammers tend to favor its use in spreading their commercial messages. In the context of detecting twitter spams, different statistical and behavioral analysis approaches were proposed. However, these techniques suffer from many limitations due to (1) ongoing changes to Twitter\u2019s streaming API which constrains access to a user\u2019s list of followers/followees, (2) spammer\u2019s creativity in building diverse messages, (3) use of embedded links and new accounts, and (4) need for analyzing different characteristics about users without their consent. To address the aforementioned challenges, we propose a novel ontology-based approach for spam detection over Twitter during events by analyzing the relationship between ham user tweets vs. spams. Our approach relies solely on public tweet messages while performing the analysis and classification tasks. In this context, ontologies are derived and used to generate a dictionary that validates real tweet messages from random topics. Similarity ratio among the dictionary and tweets is used to reflect the legitimacy of the messages. Experiments conducted on real tweet data illustrate that message-to-message techniques achieved a low detection rate compared to our ontology based approach which outperforms them by approximately 200%, in addition to promising scalability for large data analysis