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Uncovering Social Network Sybils in the Wild
Uncovering Social Network Sybils in the Wild ZHI YANG, Peking University 2 CHRISTO WILSON, University of California, Santa Barbara XIAO WANG, Peking University TINGTING GAO,RenrenInc. BEN Y. ZHAO, University of California, Santa Barbara YAFEI DAI, Peking University Sybil accounts are fake identities created to unfairly increase the power or resources of a single malicious user. Researchers have long known about the existence of Sybil accounts in online communities such as file-sharing systems, but they have not been able to perform large-scale measurements to detect them or measure their activities. In this article, we describe our efforts to detect, characterize, and understand Sybil account activity in the Renren Online Social Network (OSN). We use ground truth provided by Renren Inc. to build measurement-based Sybil detectors and deploy them on Renren to detect more than 100,000 Sybil accounts. Using our full dataset of 650,000 Sybils, we examine several aspects of Sybil behavior. First, we study their link creation behavior and find that contrary to prior conjecture, Sybils in OSNs do not form tight-knit communities. Next, we examine the fine-grained behaviors of Sybils on Renren using clickstream data. Third, we investigate behind-the-scenes collusion between large groups of Sybils. Our results reveal that Sybils with no explicit social ties still act in concert to launch attacks. Finally, we investigate enhanced techniques to identify stealthy Sybils. In summary, our study advances the understanding of Sybil behavior on OSNs and shows that Sybils can effectively avoid existing community-based Sybil detectors. We hope that our results will foster new research on Sybil detection that is based on novel types of Sybil features. -
The Google Online Marketing Challenge: Real Clients, Real Money, Real Ads and Authentic Learning
Information Systems Education Journal (ISEDJ) 12 (6) ISSN: 1545-679X November 2014 The Google Online Marketing Challenge: Real Clients, Real Money, Real Ads and Authentic Learning John S. Miko [email protected] School of Business Saint Francis University Loretto, PA 15940 Abstract Search marketing is the process of utilizing search engines to drive traffic to a Web site through both paid and unpaid efforts. One potential paid component of a search marketing strategy is the use of a pay-per-click (PPC) advertising campaign in which advertisers pay search engine hosts only when their advertisement is clicked. This paper describes a class exercise utilizing the Google Online Marketing Challenge (GOMC) to teach search marketing and PPC concepts. The GOMC is a global collegiate competition in which student teams utilize a $250 budget provided by Google to design, implement, and monitor a PPC campaign for an actual small business client. This paper argues that the GOMC is an effective exercise to teach search marketing and PPC terminology, skills, and techniques and demonstrates many of the characteristics present in authentic learning environments. Keywords: Search marketing, pay-per-click advertising, search engine, authentic learning 1. INTRODUCTION model in which advertisers pay online hosts, typically but not exclusively, search engines, Search marketing is the process of gaining only when their advertisement is clicked by a traffic and visibility from search engines through potential customer (Sullivan, 2010). One of the both paid and unpaid efforts (Sherman, 2006). most successful and widely used pay-per-click Under search marketing, unpaid efforts generally search marketing programs in the world is fall under the category of Search Engine Google’s Adwords (Kiss, 2010). -
Svms for the Blogosphere: Blog Identification and Splog Detection
SVMs for the Blogosphere: Blog Identification and Splog Detection Pranam Kolari∗, Tim Finin and Anupam Joshi University of Maryland, Baltimore County Baltimore MD {kolari1, finin, joshi}@umbc.edu Abstract Most blog search engines identify blogs and index con- tent based on update pings received from ping servers1 or Weblogs, or blogs have become an important new way directly from blogs, or through crawling blog directories to publish information, engage in discussions and form communities. The increasing popularity of blogs has and blog hosting services. To increase their coverage, blog given rise to search and analysis engines focusing on the search engines continue to crawl the Web to discover, iden- “blogosphere”. A key requirement of such systems is to tify and index blogs. This enables staying ahead of compe- identify blogs as they crawl the Web. While this ensures tition in a domain where “size does matter”. Even if a web that only blogs are indexed, blog search engines are also crawl is inessential for blog search engines, it is still pos- often overwhelmed by spam blogs (splogs). Splogs not sible that processed update pings are from non-blogs. This only incur computational overheads but also reduce user requires that the source of the pings need to be verified as a satisfaction. In this paper we first describe experimental blog prior to indexing content.2 results of blog identification using Support Vector Ma- In the first part of this paper we address blog identifica- chines (SVM). We compare results of using different feature sets and introduce new features for blog iden- tion by experimenting with different feature sets. -
Online and Mobile Advertising: Current Scenario, Emerging Trends, and Future Directions
Marketing Science Institute Special Report 07-206 Online and Mobile Advertising: Current Scenario, Emerging Trends, and Future Directions Venkatesh Shankar and Marie Hollinger © 2007 Venkatesh Shankar and Marie Hollinger MSI special reports are in draft form and are distributed online only for the benefit of MSI corporate and academic members. Reports are not to be reproduced or published, in any form or by any means, electronic or mechanical, without written permission. Online and Mobile Advertising: Current Scenario, Emerging Trends, and Future Directions Venkatesh Shankar Marie Hollinger* September 2007 * Venkatesh Shankar is Professor and Coleman Chair in Marketing and Director of the Marketing PhD. Program at the Mays Business School, Texas A&M University, College Station, TX 77843. Marie Hollinger is with USAA, San Antonio. The authors thank David Hobbs for assistance with data collection and article preparation. They also thank the MSI review team and Thomas Dotzel for helpful comments. Please address all correspondence to [email protected]. Online and Mobile Advertising: Current Scenario, Emerging Trends and Future Directions, Copyright © 2007 Venkatesh Shankar and Marie Hollinger. All rights reserved. Online and Mobile Advertising: Current Scenario, Emerging Trends, and Future Directions Abstract Online advertising expenditures are growing rapidly and are expected to reach $37 billion in the U.S. by 2011. Mobile advertising or advertising delivered through mobile devices or media is also growing substantially. Advertisers need to better understand the different forms, formats, and media associated with online and mobile advertising, how such advertising influences consumer behavior, the different pricing models for such advertising, and how to formulate a strategy for effectively allocating their marketing dollars to different online advertising forms, formats and media. -
By Nilesh Bansal a Thesis Submitted in Conformity with the Requirements
ONLINE ANALYSIS OF HIGH-VOLUME SOCIAL TEXT STEAMS by Nilesh Bansal A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Computer Science University of Toronto ⃝c Copyright 2013 by Nilesh Bansal Abstract Online Analysis of High-Volume Social Text Steams Nilesh Bansal Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2013 Social media is one of the most disruptive developments of the past decade. The impact of this information revolution has been fundamental on our society. Information dissemination has never been cheaper and users are increasingly connected with each other. The line between content producers and consumers is blurred, leaving us with abundance of data produced in real-time by users around the world on multitude of topics. In this thesis we study techniques to aid an analyst in uncovering insights from this new media form which is modeled as a high volume social text stream. The aim is to develop practical algorithms with focus on the ability to scale, amenability to reliable operation, usability, and ease of implementation. Our work lies at the intersection of building large scale real world systems and developing theoretical foundation to support the same. We identify three key predicates to enable online methods for analysis of social data, namely : • Persistent Chatter Discovery to explore topics discussed over a period of time, • Cross-referencing Media Sources to initiate analysis using a document as the query, and • Contributor Understanding to create aggregate expertise and topic summaries of authors contributing online. The thesis defines each of the predicates in detail and covers proposed techniques, their practical applicability, and detailed experimental results to establish accuracy and scalability for each of the three predicates. -
Blogosphere: Research Issues, Tools, and Applications
Blogosphere: Research Issues, Tools, and Applications Nitin Agarwal Huan Liu Computer Science and Engineering Department Arizona State University Tempe, AZ 85287 fNitin.Agarwal.2, [email protected] ABSTRACT ging. Acknowledging this fact, Times has named \You" as the person of the year 2006. This has created a consider- Weblogs, or Blogs, have facilitated people to express their able shift in the way information is assimilated by the indi- thoughts, voice their opinions, and share their experiences viduals. This paradigm shift can be attributed to the low and ideas. Individuals experience a sense of community, a barrier to publication and open standards of content genera- feeling of belonging, a bonding that members matter to one tion services like blogs, wikis, collaborative annotation, etc. another and their niche needs will be met through online These services have allowed the mass to contribute and edit interactions. Its open standards and low barrier to publi- articles publicly. Giving access to the mass to contribute cation have transformed information consumers to produc- or edit has also increased collaboration among the people ers. This has created a plethora of open-source intelligence, unlike previously where there was no collaboration as the or \collective wisdom" that acts as the storehouse of over- access to the content was limited to a chosen few. Increased whelming amounts of knowledge about the members, their collaboration has developed collective wisdom on the Inter- environment and the symbiosis between them. Nonetheless, net. \We the media" [21], is a phenomenon named by Dan vast amounts of this knowledge still remain to be discovered Gillmor: a world in which \the former audience", not a few and exploited in its suitable way. -
Web Spam Taxonomy
Web Spam Taxonomy Zolt´an Gy¨ongyi Hector Garcia-Molina Computer Science Department Computer Science Department Stanford University Stanford University [email protected] [email protected] Abstract techniques, but as far as we know, they still lack a fully effective set of tools for combating it. We believe Web spamming refers to actions intended to mislead that the first step in combating spam is understanding search engines into ranking some pages higher than it, that is, analyzing the techniques the spammers use they deserve. Recently, the amount of web spam has in- to mislead search engines. A proper understanding of creased dramatically, leading to a degradation of search spamming can then guide the development of appro- results. This paper presents a comprehensive taxon- priate countermeasures. omy of current spamming techniques, which we believe To that end, in this paper we organize web spam- can help in developing appropriate countermeasures. ming techniques into a taxonomy that can provide a framework for combating spam. We also provide an overview of published statistics about web spam to un- 1 Introduction derline the magnitude of the problem. There have been brief discussions of spam in the sci- As more and more people rely on the wealth of informa- entific literature [3, 6, 12]. One can also find details for tion available online, increased exposure on the World several specific techniques on the Web itself (e.g., [11]). Wide Web may yield significant financial gains for in- Nevertheless, we believe that this paper offers the first dividuals or organizations. Most frequently, search en- comprehensive taxonomy of all important spamming gines are the entryways to the Web; that is why some techniques known to date. -
Robust Detection of Comment Spam Using Entropy Rate
Robust Detection of Comment Spam Using Entropy Rate Alex Kantchelian Justin Ma Ling Huang UC Berkeley UC Berkeley Intel Labs [email protected] [email protected] [email protected] Sadia Afroz Anthony D. Joseph J. D. Tygar Drexel University, Philadelphia UC Berkeley UC Berkeley [email protected] [email protected] [email protected] ABSTRACT Keywords In this work, we design a method for blog comment spam detec- Spam filtering, Comment spam, Content complexity, Noisy label, tion using the assumption that spam is any kind of uninformative Logistic regression content. To measure the “informativeness” of a set of blog com- ments, we construct a language and tokenization independent met- 1. INTRODUCTION ric which we call content complexity, providing a normalized an- swer to the informal question “how much information does this Online social media have become indispensable, and a large part text contain?” We leverage this metric to create a small set of fea- of their success is that they are platforms for hosting user-generated tures well-adjusted to comment spam detection by computing the content. An important example of how users contribute value to a content complexity over groupings of messages sharing the same social media site is the inclusion of comment threads in online ar- author, the same sender IP, the same included links, etc. ticles of various kinds (news, personal blogs, etc). Through com- We evaluate our method against an exact set of tens of millions ments, users have an opportunity to share their insights with one of comments collected over a four months period and containing another. -
Spam in Blogs and Social Media
ȱȱȱȱ ȱ Pranam Kolari, Tim Finin Akshay Java, Anupam Joshi March 25, 2007 ȱ • Spam on the Internet –Variants – Social Media Spam • Reason behind Spam in Blogs • Detecting Spam Blogs • Trends and Issues • How can you help? • Conclusions Pranam Kolari is a UMBC PhD Tim Finin is a UMBC Professor student. His dissertation is on with over 30 years of experience spam blog detection, with tools in the applying AI to information developed in use both by academia and systems, intelligent interfaces and industry. He has active research interest robotics. Current interests include social in internal corporate blogs, the Semantic media, the Semantic Web and multi- Web and blog analytics. agent systems. Akshay Java is a UMBC PhD student. Anupam Joshi is a UMBC Pro- His dissertation is on identify- fessor with research interests in ing influence and opinions in the broad area of networked social media. His research interests computing and intelligent systems. He include blog analytics, information currently serves on the editorial board of retrieval, natural language processing the International Journal of the Semantic and the Semantic Web. Web and Information. Ƿ Ȭȱ • Early form seen around 1992 with MAKE MONEY FAST • 80-85% of all e-mail traffic is spam • In numbers 2005 - (June) 30 billion per day 2006 - (June) 55 billion per day 2006 - (December) 85 billion per day 2007 - (February) 90 billion per day Sources: IronPort, Wikipedia http://www.ironport.com/company/ironport_pr_2006-06-28.html ȱȱǵ • “Unsolicited usually commercial e-mail sent to a large -
Advances in Online Learning-Based Spam Filtering
ADVANCES IN ONLINE LEARNING-BASED SPAM FILTERING A dissertation submitted by D. Sculley, M.Ed., M.S. In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science TUFTS UNIVERSITY August 2008 ADVISER: Carla E. Brodley Acknowledgments I would like to take this opportunity to thank my advisor Carla Brodley for her patient guidance, my parents David and Paula Sculley for their support and en- couragement, and my bride Jessica Evans for making everything worth doing. I gratefully acknowledge Rediff.com for funding the writing of this disserta- tion. D. Sculley TUFTS UNIVERSITY August 2008 ii Abstract The low cost of digital communication has given rise to the problem of email spam, which is unwanted, harmful, or abusive electronic content. In this thesis, we present several advances in the application of online machine learning methods for auto- matically filtering spam. We detail a sliding-window variant of Support Vector Machines that yields state of the art results for the standard online filtering task. We explore a variety of feature representations for spam data. We reduce human labeling cost through the use of efficient online active learning variants. We give practical solutions to the one-sided feedback scenario, in which users only give la- beling feedback on messages predicted to be non-spam. We investigate the impact of class label noise on machine learning-based spam filters, showing that previous benchmark evaluations rewarded filters prone to overfitting in real-world settings and proposing several modifications for combating these negative effects. Finally, we investigate the performance of these filtering methods on the more challenging task of abuse filtering in blog comments. -
2 Uncovering Social Network Sybils in the Wild
Uncovering Social Network Sybils in the Wild ZHI YANG, Peking University 2 CHRISTO WILSON,UniversityofCalifornia,SantaBarbara XIAO WANG, Peking University TINGTING GAO,RenrenInc. BEN Y. ZHAO,UniversityofCalifornia,SantaBarbara YAFEI DAI , Peking University Sybil accounts are fake identities created to unfairly increase the power or resources of a single malicious user. Researchers have long known about the existence of Sybil accounts in online communities such as file-sharing systems, but they have not been able to perform large-scale measurements to detect them or measure their activities. In this article, we describe our efforts to detect, characterize, and understand Sybil account activity in the Renren Online Social Network (OSN). We use ground truth provided by Renren Inc. to build measurement-based Sybil detectors and deploy them on Renren to detect more than 100,000 Sybil accounts. Using our full dataset of 650,000 Sybils, we examine several aspects of Sybil behavior. First, we study their link creation behavior and find that contrary to prior conjecture, Sybils in OSNs do not form tight-knit communities. Next, we examine the fine-grained behaviors of Sybils on Renren using clickstream data. Third, we investigate behind-the-scenes collusion between large groups of Sybils. Our results reveal that Sybils with no explicit social ties still act in concert to launch attacks. Finally, we investigate enhanced techniques to identify stealthy Sybils. In summary, our study advances the understanding of Sybil behavior on OSNs and shows that Sybils can effectively avoid existing community-based Sybil detectors. We hope that our results will foster new research on Sybil detection that is based on novel types of Sybil features. -
Part I Google Ads and PPC
The Ultimate Guide to Paid Media - Part I Google Ads and PPC Written by Manick Bhan, CTO of LinkGraph Google Ads and PPC Six Common Downfalls of 01 Fundamentals 05 Paid Media Campaigns Everything you Need to How to Measure the Success of 02 Know About Pay-Per-Click 06 your Google Ads Campaigns How to Design a Profitable Final Thoughts on Google 03 Google Ads Campaign 07 Ads Campaigns The Science of Keyword 60-Second Glossary of 04 Bidding 08 Google Ads Terminology 01 Google Ads and PPC Fundamentals Almost 90% of consumers turn to search engines when seeking out information on new products and services. With 60% also claiming to click on a search ad every week, digital marketers know that paid search advertising can be a powerful tool for brands to boost site visibility, generate leads, and get their business in front of those who are already searching for similar products and services. Unlike other forms of digital advertising, Google ads allows you to reach your target audience at the exact moment they are looking for a solution. The more relevant your ad is to their search query, the more likely they are to click, visit your website, and move down the sales funnel toward conversion. Although SEO is a great way to drive traffic and site visits to perpetuity, 46% of total clicks on Google go to the top three paid ads, meaning paid search ads intercept nearly half of the clicks away from organic search results. For this reason, brands that incorporate PPC marketing alongside search engine optimization are better positioned to increase market share and build their brand awareness in less time.