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Link-based web spam detection using weight properties

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Abstract

Link spam is created with the intention of boosting one target’s rank in exchange of business profit. This unethical way of deceiving Web search engines is known as Web spam. Since then many anti-link spam detection techniques have constantly being proposed. Web spam detection is a crucial task due to its devastation towards Web search engines and global cost of billion dollars annually. In this paper, we proposed a novel technique by incorporating weight properties to enhance the Web spam detection algorithms. Weight properties can be defined as the influences of one Web node towards another Web node. We modified existing Web spam detection algorithms with our novel technique to evaluate the performances on a large public Web spam dataset – WEBSPAM-UK2007. The overall performance have shown that the modified algorithms outperform the benchmark algorithms up to 30.5 % improvement at host level and 6.11 % improvement at page level.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Curtin University, Sarawak Campus, Sarawak, Malaysia

    Kwang Leng Goh & Ravi Kumar Patchmuthu

  2. Department of Computer Application, National Institute of Technology, Kurukshetra, Haryana, India

    Ashutosh Kumar Singh

Authors
  1. Kwang Leng Goh
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  2. Ravi Kumar Patchmuthu
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  3. Ashutosh Kumar Singh
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Corresponding author

Correspondence to Kwang Leng Goh.

Appendix

Appendix

Anti-TrustRank algorithms

$$ATR(p)=j\cdot \sum\limits_{(p,q)\in \varepsilon } {\left( {\frac{ATR(q)}{\tau (q)}} \right)+(1-j)\cdot B(p)} $$

Wu et al. Distrust algorithm

$$DISTR(p)=j\cdot c\cdot MaxShare\left[ {\sum\limits_{\forall (p:q)\in G} {\left( {\frac{DISTR(q)}{\log (1+\tau (q))}} \right)} } \right]+(1-j)\cdot B(p) $$

Nie et al. Distrust algorithm

$$Distrust(p)=j\cdot MaxShare\left[ {\sum\limits_{\forall (p:q)\in G} {\left( {\frac{Distrust(q)}{\tau (q)}} \right)} } \right]+(1-j)\cdot B(p) $$

Where ATR represent Anti-TrustRank, DISTR represent Baoning Wu et al. Distrust algorithm and Distrust(p) represent Lan Nie et al. Distrust algorithm. j is a jumping probability, τ(q) is the number of incoming links of host q and B(p) is the spam vector. MaxShareis a function that only takes the maximum distrust values from the children.

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Goh, K.L., Patchmuthu, R.K. & Singh, A.K. Link-based web spam detection using weight properties. J Intell Inf Syst 43, 129–145 (2014). https://doi.org/10.1007/s10844-014-0310-y

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