Mohammed F. Fofana Tp16/17/H/1295
Literature Review Table
Paper Topic: Identifying Web Spam with User Behaviour Analysis.
S/N AUTHOR(S) & DATE TITLE PROBLEM ADDRESS METHOD USED RESULT COMMENT
1 Ala M. Al-Zoubi
(2017) Spam Profile Detection in Social Networks Based on Public Features Spam are unwanted ad, within the social network and email that causes security threat by criminals and terrorists By collecting Data and analyzing the profiles structural of those effected accounts which lead to identifying those spam profile which help to improvement the future of the system and it policies The twitter account was compromise and other online social networks, but with the Spam detection system and policies regardless of language it improve and prevent spammer There are needs to improvement the system to identify every spam pages and prevent spammers from sending unwanted messages through emails and other online social network
2 Cheng Cao and James Caverlee
(2015) Detecting Spam URLs in Social Media
via Behavioral Analysis Identifying and detecting spam within URLs of the online social media is a serious challenge fixed by user of the social media The method used within this research was the collection data, using ideas analyzed the form of posting and clicking on a links with in the social media without a system but using a query By analyzing the data collected and using query the observations of the posting and clicking, they came up with a perfect result to certify the report they wanted from the social media knowing how many spams link are click on per day and how many are posted on the social media per day The method used within this research is quite not certifying because there wasn’t a dedicated system used to performed the data collection and analyzing the amount of click and post but instead it was an intuitive been used
3 Seyed K. Fayazbakhsh Joydeep Sinha
(2012) Review Spam Detection: A Network-based Approach
Spamming are suspicious of being targeted but they garget people
To get access to information, data that are to be reviews or products that they think will give them some access to business market
The main method used with in this paper is to integrate the important features that have been used in other related
Works in a network based framework, this method help us understand how the older system work and how we can improve the new one, to prevent spam and spammer using a network-based approach the application
applied a well-known outlier detection algorithm to the problem whose results
turn out to be consistent with the output of our method that was used to help us get information needed With the help of information gather from other sources and other papers, this approach and algorithms, the system will still need a live data than following other people information knowing very well that some of those information may have error in it.
YIQUN LIU, FEI CHEN, WEIZE KONG, HUIJIA YU, MIN ZHANG, SHAOPING MA,
and LIYUN RU,
Identifying Web Spam with the Wisdom of the Crowds
To combat web spam within the web search engines are one of the major challenges face by web owner
The method that was used in this work was very active and achieving, after collecting data from various search engine using user behavior oriented web spam detection algorithm to analyzes large-scale web access logs to exploits the differences between web spam pages, and ordinary pages with the help of machine learning and descriptive analysis of user behaviour features of a web spam. The result from user behavior oriented approach is one of the best approach in the combating web spam as compared to other spam pages detectors, the user behavior oriented use algorithm was is very active because it identify and newly appearing web spam in the search engine and it help improve the life user and save them from spam pages
After the successful completion of such system it focus was only on China and their web engines, so to improve this system, the system should be developed to meet the need of the world at-large that is the only way such
System can become a vibrant one but until than it still limited
Yiqun Liu, Rongwei Cen, Min Zhang, Shaoping Ma, Liyun Ru
(2008) Identifying Web Spam with User Behavior Analysis1 How to combat and identify Web spam have become one of the top challenges for Web search engines Base on the deceitful motivation of spam pages and spammers the method used in this paper to combat web spam is a unique one, the user behavior oriented web spam detection framework and Bayesian learning, including the patterns of visiting used web spam pages which is different from ordinary web pages. This method also include the collecting and analyzing user-data and large-scale information of a spam pages.
These approaches where used because learning to classify documents or Web pages was easy. The result from this work is clear that many algorithm has been use to combat web spam but with different ideals, for this framework and Bayesian learning algorithm it make it easy to identify spam pages and how to combat them to prevent from their deceitful plan to cause harm to user. This is another good system, but it only become a good system when it has been tested by the world not a single country and work done within this environment is straightly in chines and it only benefit the chines community, knowing very that situation is not only chines problem but the world at-large.