PolaritySpam: Propagating Content-based Information Through a Web-Graph to Detect Web Spam
Spam web pages have become a problem for Information Retrieval systems due to the negative effects that this phenomenon can cause in their results. In this work we tackle the problem of detecting these pages with a propagation algorithm that, taking as input a web graph, chooses a set of spam and no...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/130681 |
| Acceso en línea: | https://hdl.handle.net/11441/130681 |
| Access Level: | acceso abierto |
| Palabra clave: | Information retrieval Web spam detection Graph algorithms PageRank Web search |
| Sumario: | Spam web pages have become a problem for Information Retrieval systems due to the negative effects that this phenomenon can cause in their results. In this work we tackle the problem of detecting these pages with a propagation algorithm that, taking as input a web graph, chooses a set of spam and not-spam web pages in order to spread their spam likelihood over the rest of the network. Thus we take advantage of the links between pages to obtain a ranking of pages according to their relevance and their spam likelihood. Our intuition consists in giving a high reputation to those pages related to relevant ones, and giving a high spam likelihood to the pages linked to spam web pages. We introduce the novelty of including the content of the web pages in the computation of an a priori estimation of the spam likelihood of the pages, and propagate this information. Our graph-based algorithm computes two scores for each node in the graph. Intuitively, these values represent how bad or good (spam-like or not) is a web page, according to its textual content and its relations in the graph. The experimental results show that our method outperforms other techniques for spam detection |
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