PPT-Finding Deceptive Opinion Spam by Any Stretch of the Imagin

Myle Ott Yejin Choi Claire Cardie Jeffrey T Hancock Cornell University Yu Huang 1 What Does It Mean Finding Deceptive Opinion Spam by Any . Emma Kazaryan . Street Law . Introduction to Consumer Law. Consumers are people who buy or use goods or services. In the US federal and state laws exist to protect consumers from businesses that want to trick or take advantage of them. Filtering. Service. Kurt Thomas. , Chris Grier, Justin Ma,. Vern . Paxson. , Dawn Song. University of California, Berkeley. International Computer Science Institute. Motivation. Social Networks. (. Facebook, Twitter). (FA9550-11-1-0120). PI: Julia Hirschberg (Columbia University). Co-PI: Andrew Rosenberg (CUNY). Co-PI: Michelle Levine (Columbia University). AFOSR Program Review:. Trust and Influence (June 16 – 19, 2014, Arlington, VA). Zhenhai Duan. Department of Computer Science. Florida State University. Outline. Motivation and background. SPOT algorithm on detecting compromised machines. Performance evaluation . Summary. 2. Motivation. Hongyu. . Gao. , . Yan . Chen, Kathy Lee, Diana . Palsetia. . and . Alok. . Choudhary. Lab for Internet and Security Technology (LIST). Department of EECS. Northwestern University. Background. 2. Background. Distortion of search results . by “spam farms” and “hijacked” links. (aka. search engine optimization). page to be. “. promoted. ”. . boosting. pages. (spam farm). Susceptibility to manipulation and lack of trust model . Fernando. Sanchez, . Zhenhai Duan. Florida State University. Yingfei. Dong. University of Hawaii. Problem Statement. Email header forgery. But to what degree and how well they do it?. Why this is important?. . 1. Sai Koushik Haddunoori. Problem:. E-mail provides a perfect way to send . millions . of advertisements at no cost for the sender, and this unfortunate fact is nowadays extensively exploited by several . Authors. Abu Awal Md Shoeb, Dibya Mukhopadhyay, Shahid Al Noor, Alan Sprague, and Gary Warner. Dec 14, 2014, Harvard University. 1. Introduction. Why spam detection is important. Why it is difficult to detect. rhetorical context?. SPAM . is a set of tools we’ll use to look at . rhetorical context . and make decisions about how to best communicate our ideas. .. S. ituation:. P. urpose:. A. udience:. M. ode:. Spam:. Spam is unsolicited or undesired electronic junk mail. Characteristics of spam are:. Mass . mailing to large number of recipients. Usually a commercial advertisement. Annoying but usually harmless unless coupled with a fraud based phishing scam . Zhang . Yanbin. , SG17/Q5 Rapporteur. Geneva, Switzerland . March, 2016. M. ain Content. C. hapter 1. Introduction of Spam . C. hapter 2. The Objective and Mission for Q5. C. hapter 3. The Position of Specific Projects in Q5. Agenda. Next Generation Antispam Protection . Forefront Overview. Forefront Security for Exchange Server. Forefront Online Security for Exchange. Hybrid Software + Services Solution. Summary. Q&A. Atliay. Betül . Delalic. Nijaz. PS Kryptographie und IT Sicherheit SS17. 1. INHALT. Definition . . Arten . Funktionsweise . T. echnische Voraussetzungen. . Auswirkungen . . Kosten . Rechtslage .