Net spam a network based spam detection framework for reviews in online social media
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The document presents 'netspam', a novel network-based framework for detecting spam in online reviews, particularly on platforms like Yelp and Amazon. It models review datasets as heterogeneous information networks, focusing on the importance of various spam features to enhance detection accuracy compared to existing methods. The proposed system shows superior results across different metrics and initiates work on detecting spam features, addressing gaps in previous research.
Net spam a network based spam detection framework for reviews in online social media
- 1. NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media Abstract—Nowadays, a big part of people rely on available content in social media in their decisions (e.g. reviews and feedback on a topic or product). The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this study, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review datasets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features help us to obtain better results in terms of different metrics experimented on real-world review datasets from Yelp and Amazon websites. The results show that NetSpam outperforms the existing methods and among four categories of features; including review-behavioral, user-behavioral, review linguistic, user- linguistic, the first type of features performs better than the other categories. Existing work In Existing work, the work only depend on the detect the spam reviews and spammers. None of them show the importance of each extracted feature type. On the other hand, a considerable amount of literature has been published on the techniques used to identify spam and spammers as well as different type of analysis on this topic. These techniques can be classified into different categories; some using linguistic patterns in text which are mostly based on bigram, and unigram, others are based on behavioral patterns that rely on features extracted from patterns in users’ behavior which are mostly metadata based. Disadvantages: These work not enough to classify the spam network. Lack of work to detect spam features.
- 2. Proposed System We propose NetSpam framework that is a novel network based approach which models review networks as heterogeneous information networks. The general concept of our proposed framework is to model a given review dataset as a Heterogeneous Information Network (HIN) and to map the problem of spam detection into a HIN classification problem. In particular, we model review dataset as a HIN in which reviews are connected through different node types (such as features and users). A weighting concept is then employed to calculate each feature’s importance (or weight). These weights are utilized to calculate the final labels for reviews using both unsupervised and supervised approaches. Advantages Importance of spam features help us to obtain better results in terms of different metrics experimented on real-world review datasets Initiating the work to detect spam features. SYSTEM REQUIREMENTS HARDWARE REQUIREMENTS: Hardware : Pentium Speed : 1.1 GHz RAM : 1GB Hard Disk : 20 GB SOFTWARE REQUIREMENTS: Operating System : Windows Family Technology : Java and J2EE
- 3. Web Technologies : Html, JavaScript, CSS Web Server : Apache Tomcat 7.0/8.0 Database : My SQL 5.5 or Higher UML's : StarUml Java Version : JDK 1.7 or 1.8 Implemented by Development team : Cloud Technologies Website : http://www.cloudstechnologies.in/ Contact : 8121953811, 040-65511811