Spam, damn spam, and statistics: using statistical analysis to locate spam web pages
Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Improving web spam classifiers using link structure
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Extracting link spam using biased random walks from spam seed sets
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
A large-scale study of link spam detection by graph algorithms
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Know your neighbors: web spam detection using the web topology
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Google Scholar's Ranking Algorithm: The Impact of Articles' Age (An Empirical Study)
ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
Thwarting the nigritude ultramarine: learning to identify link spam
ECML'05 Proceedings of the 16th European conference on Machine Learning
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In this research-in-progress paper we present the current results of several experiments in which we analyzed whether spamming Google Scholar is possible. Our results show, it is possible: We 'improved' the ranking of articles by manipulating their citation counts and we made articles appear in searchers for keywords the articles did not originally contained by placing invisible text in modified versions of the article.