Network Spam To Create A Arrangement Intended For Online Public Reviews

The ability for anyone to leave a comment offers a golden opportunity for spammers to write spam reviews of products and services for a variety of interests. Using the importance of spam functions helps us perform better in terms of various metrics tested on real-world review data sets from Yelp and Amazon. Identifying spammers and spam is a hot topic of research, and although a large number of studies have recently been conducted for this purpose, the methodologies presented so far barely detect spam reviews and none have demonstrated the importance of each type of extracted feature. . In this study, we propose a new framework, called Network Spam that uses spam properties to model review data sets as heterogeneous information networks to assign a spam detection procedure to the classification problem in those networks. The results show that the spam network outperforms existing methods and four classes of characteristics; including behavior review, user behavior, language review, user language, and the first type of features work better than other categories

Collective Multi-relational Network Mining

Our world is becoming increasingly interconnected, and the study of networks and graphs are becoming more important than ever. Domains such as biological and pharmaceutical networks, online social networks, the World Wide Web, recommender systems, and scholarly networks are just a few examples that include explicit or implicit network structures. Most networks are formed between different types of nodes and contain different types of links. Leveraging these multi-relational and heterogeneous structures is an important factor in developing better models for these real-world networks. Another important aspect of developing models for network data to make predictions about entities such as nodes or links, is the connections between such entities. These connections invalidate the i.i.d. assumptions about the data in most traditional machine learning methods. Hence, unlike models for non-network data where predictions about entities are made independently of each other, the inter-connectivity of the entities in networks should cause the inferred information about one entity to change the models belief about other related entities. In this dissertation, I present models that can effectively leverage the multi-relational nature of networks and collectively make predictions on links and nodes. In both tasks, I empirically show the importance of considering the multi-relational characteristics and collective predictions. In the first part, I present models to make predictions on nodes by leveraging the graph structure, links generation sequence, and making collective predictions. I apply the node classification methods to detect social spammers in evolving multi-relational social networks and show their effectiveness in identifying spammers without the need of using the textual content. In the second part, I present a generalized augmented multi-relational bi-typed network. I then propose a template for link inference models on these networks and show their application in pharmaceutical discoveries and recommender systems. In the third part, I show that my proposed collective link prediction model is an instance of a general graph-based prediction model that relies on a neighborhood graph for predictions. I then propose a framework that can dynamically adapt the neighborhood graph based on the state of variables from intermediate inference results, as well as structural properties of the relations connecting them to improve the predictive performance of the model

Search Rank Fraud Prevention in Online Systems

The survival of products in online services such as Google Play, Yelp, Facebook and Amazon, is contingent on their search rank. This, along with the social impact of such services, has also turned them into a lucrative medium for fraudulently influencing public opinion. Motivated by the need to aggressively promote products, communities that specialize in social network fraud (e.g., fake opinions and reviews, likes, followers, app installs) have emerged, to create a black market for fraudulent search optimization. Fraudulent product developers exploit these communities to hire teams of workers willing and able to commit fraud collectively, emulating realistic, spontaneous activities from unrelated people. We call this behavior “search rank fraud”. In this dissertation, we argue that fraud needs to be proactively discouraged and prevented, instead of only reactively detected and filtered. We introduce two novel approaches to discourage search rank fraud in online systems. First, we detect fraud in real-time, when it is posted, and impose resource consuming penalties on the devices that post activities. We introduce and leverage several novel concepts that include (i) stateless, verifiable computational puzzles that impose minimal performance overhead, but enable the efficient verification of their authenticity, (ii) a real-time, graph based solution to assign fraud scores to user activities, and (iii) mechanisms to dynamically adjust puzzle difficulty levels based on fraud scores and the computational capabilities of devices. In a second approach, we introduce the problem of fraud de-anonymization: reveal the crowdsourcing site accounts of the people who post large amounts of fraud, thus their bank accounts, and provide compelling evidence of fraud to the users of products that they promote. We investigate the ability of our solutions to ensure that fraud does not pay off

Making Thin Data Thick: User Behavior Analysis with Minimum Information

abstract: With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of user-generated content have introduced the human behavior's big-data. This big data has brought about countless opportunities for analyzing human behavior at scale. However, is this data enough? Unfortunately, the data available at the individual-level is limited for most users. This limited individual-level data is often referred to as thin data. Hence, researchers face a big-data paradox, where this big-data is a large collection of mostly limited individual-level information. Researchers are often constrained to derive meaningful insights regarding online user behavior with this limited information. Simply put, they have to make thin data thick. In this dissertation, how human behavior's thin data can be made thick is investigated. The chief objective of this dissertation is to demonstrate how traces of human behavior can be efficiently gleaned from the, often limited, individual-level information; hence, introducing an all-inclusive user behavior analysis methodology that considers social media users with different levels of information availability. To that end, the absolute minimum information in terms of both link or content data that is available for any social media user is determined. Utilizing only minimum information in different applications on social media such as prediction or recommendation tasks allows for solutions that are (1) generalizable to all social media users and that are (2) easy to implement. However, are applications that employ only minimum information as effective or comparable to applications that use more information? In this dissertation, it is shown that common research challenges such as detecting malicious users or friend recommendation (i.e., link prediction) can be effectively performed using only minimum information. More importantly, it is demonstrated that unique user identification can be achieved using minimum information. Theoretical boundaries of unique user identification are obtained by introducing social signatures. Social signatures allow for user identification in any large-scale network on social media. The results on single-site user identification are generalized to multiple sites and it is shown how the same user can be uniquely identified across multiple sites using only minimum link or content information. The findings in this dissertation allows finding the same user across multiple sites, which in turn has multiple implications. In particular, by identifying the same users across sites, (1) patterns that users exhibit across sites are identified, (2) how user behavior varies across sites is determined, and (3) activities that are observed only across sites are identified and studied.Dissertation/ThesisDoctoral Dissertation Computer Science 201