Muhammad Abulaish

A Generic Statistical Approach for Spam Detection in Online Social Networks

Abstract In this paper, we present a generic statistical approach to identify spam profiles on Online Social Networks (OSNs). Our study is based on real datasets containing both normal and spam profiles crawled from Facebook and Twitter networks. We have identified a set of 14 generic statistical features to identify spam profiles. The identified features are common to both Facebook and Twitter networks. For classification task, we have used three different classification algorithms – na i ve Bayes , Jrip , and J48 , and evaluated them on both individual and combined datasets to establish the discriminative property of the identified features. The results obtained on a combined dataset has detection rate (DR) as 0.957 and false positive rate (FPR) as 0.048, whereas on Facebook dataset the DR and FPR values are 0.964 and 0.089, respectively, and that on Twitter dataset the DR and FPR values are 0.976 and 0.075, respectively. We have also analyzed the contribution of each individual feature towards the detection accuracy of spam profiles. Thereafter, we have considered 7 most discriminative features and proposed a clustering-based approach to identify spam campaigns on Facebook and Twitter networks.

Community-based features for identifying spammers in online social networks

The popularity of Online Social Networks (OSNs) is often faced with challenges of dealing with undesirable users and their malicious activities in the social networks. The most common form of malicious activity over OSNs is spamming wherein a bot (fake user) disseminates content, malware/viruses, etc. to the legitimate users of the social networks. The common motives behind such activity include phishing, scams, viral marketing and so on which the recipients do not indent to receive. It is thus a highly desirable task to devise techniques and methods for identifying spammers (spamming accounts) in OSNs. With an aim of exploiting social network characteristics of community formation by legitimate users, this paper presents a community-based framework to identify spammers in OSNs. The framework uses community-based features of OSN users to learn classification models for identification of spamming accounts. The preliminary experiments on a real-world dataset with simulated spammers reveal that proposed approach is promising and that using community-based node features of OSN users can improve the performance of classifying spammers and legitimate users.

Feature and Opinion Mining for Customer Review Summarization

In this paper, we present an opinion mining system to identify product features and opinions from review documents. The features and opinions are extracted using semantic and linguistic analysis of text documents. The polarity of opinion sentences is established using polarity scores of the opinion words through Senti-WordNet to generate a feature-based summary of review documents. The system is also integrated with a visualization module to present feature-based summary of review documents in a comprehendible way.

An MCL-Based Approach for Spam Profile Detection in Online Social Networks

Over the past few years, Online Social Networks (OSNs) have emerged as cheap and popular communication and information sharing media. Huge amount of information is being shared through popular OSN sites. This aspect of sharing information to a large number of individuals with ease has attracted social spammers to exploit the network of trust for spreading spam messages to promote personal blogs, advertisements, phishing, scam and so on. In this paper, we present a Markov Clustering (MCL) based approach for the detection of spam profiles on OSNs. Our study is based on a real dataset of Facebook profiles, which includes both benign and spam profiles. We model social network using a weighted graph in which profiles are represented as nodes and their interactions as edges. The weight of an edge, connecting a pair of user profiles, is calculated as a function of their real social interactions in terms of active friends, page likes and shared URLs within the network. MCL is applied on the weighted graph to generate different clusters containing different categories of profiles. Majority voting is applied to handle the cases in which a cluster contains both spam and normal profiles. Our experimental results show that majority voting not only reduces the number of clusters to a minimum, but also increases the performance values in terms of FP and FB measures from FP=0.85 and FB=0.75 to FP=0.88 and FB=0.79, respectively.

HOCTracker: Tracking the Evolution of Hierarchical and Overlapping Communities in Dynamic Social Networks

In this paper, we propose a unified framework, HOCTracker, for tracking the evolution of hierarchical and overlapping communities in online social networks. Unlike most of the dynamic community detection methods, HOCTracker adapts a preliminary community structure towards dynamic changes in social networks using a novel density-based approach for detecting overlapping community structures, and automatically tracks evolutionary events like birth, growth, contraction, merge, split, and death of communities. It uses a novel and efficient log-based approach to map evolutionary relations between communities identified at two consecutive time-steps of a dynamic network, which considerably reduces the number of community comparisons. Moreover, it does not require an ageing function to remove old interactions for identifying community evolutionary events. HOCTracker is applicable to directed/undirected and weighted/unweighted networks. Experimental results have shown that community structures identified by HOCTracker on some well-known benchmark networks are significant and in general better that the community structures identified by the state-of-the-art methods.

Mining feature-opinion pairs and their reliability scores from web opinion sources

Due to proliferation of Web 2.0, there is an exponential growth in user generated contents in the form of customer reviews on the Web, containing precious information useful for both customers and manufacturers. However, most of the contents are stored in either unstructured or semi-structured format due to which distillation of knowledge from this huge repository is a challenging task. In this paper, we propose a text mining approach to mine product features, opinions and their reliability scores from Web opinion sources. A rule-based system is implemented, which applies linguistic and semantic analysis of texts to mine feature-opinion pairs that have sentence-level co-occurrence in review documents. The extracted feature-opinion pairs and source documents are modeled using a bipartite graph structure. Considering feature-opinion pairs as hubs and source documents as authorities, Hyperlink-Induced Topic Search (HITS) algorithm is applied to generate reliability score for each feature-opinion pair with respect to the underlying corpus. The efficacy of the proposed system is established through experimentation over customer reviews on different models of electronic products.

Biological Ontology Enhancement with Fuzzy Relations: A Text-Mining Framework

Domain ontology can help in information retrieval from documents. But ontology is a pre-defined structure with crisp concept descriptions and inter-concept relations. However, due to the dynamic nature of the document repository, ontology should be upgradeable with information extracted through text mining of documents in the domain. This also necessitates that concepts, their descriptions and inter-concept relations should be associated with a degree of fuzziness that will indicate the support for the extracted knowledge according to the currently available resources. Supports may be revised with more knowledge coming in future. This approach preserves the basic structured knowledge format for storing domain knowledge, but at the same time allows for update of information. In this paper, we have proposed a mechanism which initiates text mining with a set of ontological concepts, and thereafter extracts fuzzy relations through text mining. Membership values of relations are functions of frequency of co-occurrence of concepts and relations. We have worked on the GENIA corpus and shown how fuzzy relations can be further used for guided information extraction from MEDLINE documents.

A concept-driven biomedical knowledge extraction and visualization framework for conceptualization of text corpora

A number of techniques such as information extraction, document classification, document clustering and information visualization have been developed to ease extraction and understanding of information embedded within text documents. However, knowledge that is embedded in natural language texts is difficult to extract using simple pattern matching techniques and most of these methods do not help users directly understand key concepts and their semantic relationships in document corpora, which are critical for capturing their conceptual structures. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured texts that computers can not interpret very easily. In this paper, we have presented a novel Biomedical Knowledge Extraction and Visualization framework, BioKEVis to identify key information components from biomedical text documents. The information components are centered on key concepts. BioKEVis applies linguistic analysis and Latent Semantic Analysis (LSA) to identify key concepts. The information component extraction principle is based on natural language processing techniques and semantic-based analysis. The system is also integrated with a biomedical named entity recognizer, ABNER, to tag genes, proteins and other entity names in the text. We have also presented a method for collating information extracted from multiple sources to generate semantic network. The network provides distinct user perspectives and allows navigation over documents with similar information components and is also used to provide a comprehensive view of the collection. The system stores the extracted information components in a structured repository which is integrated with a query-processing module to handle biomedical queries over text documents. We have also proposed a document ranking mechanism to present retrieved documents in order of their relevance to the user query.

Using communities against deception in online social networks

Online social networking (OSN) sites such as Facebook and Twitter have become highly popular on the Internet with millions of members sharing information and content, and connecting with each other. The connections thus established reflect the real-world relationships between the users of these social networks. These sites are being looked upon as high-potential marketing opportunities by many organisations. OSNs offer many useful properties that reflect real-world social network characteristics, which include small-world behaviour, significant local clustering, the existence of large, strongly connected components and formation of tightly knit groups or communities. 1 , 2 , 3 Malicious activities in online social networks (OSNs) have transformed from simple spamming to highly deceptive forms focused on breaching the privacy of online social network users and ultimately their trust. Traditional content-based and collaborative filtering techniques give only average results. The topological characteristics of legitimate users, including the formation of tightly knit communities, is a more promising approach, but we need to devise efficient techniques for identifying spammers and attackers, explain Sajid Yousuf Bhat and Muhammad Abulaish of Jamia Millia Islamia, New Delhi.

Analysis and mining of online social networks: emerging trends and challenges

Social network analysis (SNA) is a multidisciplinary field dedicated to the analysis and modeling of relations and diffusion processes among various objects in nature and society, and other information/knowledge processing entities with an aim of understanding how the behavior of individuals and their interactions translates into large‐scale social phenomenon. Because of exploding popularity of online social networks and availability of huge amount of user‐generated content, there is a great opportunity to analyze social networks and their dynamics at resolutions and levels not seen before. This has resulted in a significant increase in research literature at the intersection of the computing and social sciences leading to several techniques for social network modeling and analysis in the area of machine learning and data mining. Some of the current challenges in the analysis of large‐scale social network data include social network modeling and representation, link mining, sentiment analysis, semantic SNA, information diffusion, viral marketing, and influential node mining. WIREs Data Mining Knowl Discov 2013, 3:408–444. doi: 10.1002/widm.1105