Discovering the Fake Followers in the Micro-blogging via Machine Learning Yi Shen Jianjun Yu October 16, 2013 Chinese Academy of Sciences Computer Network.

Slides:

Advertisements

Similar presentations

Influence and Passivity in Social Media Daniel M. Romero, Wojciech Galuba, Sitaram Asur, and Bernardo A. Huberman Social Computing Lab, HP Labs.

Advertisements

Role of Online Social Networks during disasters & political movements Saptarshi Ghosh Department of Computer Science and Technology Bengal Engineering.

Twitter By: Lauren Price. History Founded by Jack Dorsey, Evan Williams, and Programmers who worked at a podcasting company.

Review Spam Detection via Temporal Pattern Discovery Sihong Xie, Guan Wang, Shuyang Lin, Philip S. Yu Department of Computer Science University of Illinois.

Fighting Fire With Fire: Crowdsourcing Security Solutions on the Social Web Christo Wilson Northeastern University

Classifying Business Messages on Facebook Bei Yu 1 and Linchi Kwok 2 School of Information Studies 1 David B. Falk College of Sport and Human Dynamics.

Social Networking: Adoption and Impact on Libraries and Information Centers By Kristin Falls, Jonathan Gazdecki, Shannon McDermitt, and Catherine Sossi.

Introduction to machine learning

Team HauntQuant October 25th, 2013

Large-Scale Cost-sensitive Online Social Network Profile Linkage.

Automated malware classification based on network behavior

Presented by Karen Porter UM School of Business Administration & ImpactOnlineMarketing.com Google + and Twitter for Biz ImpactOnlineMarketing.com.

Social Networking and On-Line Communities: Classification and Research Trends Maria Ioannidou, Eugenia Raptotasiou, Ioannis Anagnostopoulos.

Social Media Attacks By Laura Jung. How the Attacks Start Popularity of these sites with millions of users makes them perfect places for cyber attacks.

Network and Systems Security By, Vigya Sharma (2011MCS2564) FaisalAlam(2011MCS2608) DETECTING SPAMMERS ON SOCIAL NETWORKS.

FaceBook and Your Business Women in Technology in Nigeria Presented by Mrs M.O Alade Women in Technology in Nigeria

C HAPTER Social Networking Using Facebook: Advanced Techniques 3 Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall.

Contents Percentages Percentage Change Percentages.

Microblogs: Information and Social Network Huang Yuxin.

Week-5_Ratios, Proportions, and Percent. Topic: Finding the final amount given the original amount and a percentage increase or decrease.

Sound Detection Derek Hoiem Rahul Sukthankar (mentor) August 24, 2004.

Date: 2012/4/23 Source: Michael J. Welch. al(WSDM’11) Advisor: Jia-ling, Koh Speaker: Jiun Jia, Chiou Topical semantics of twitter links 1.

1 Pattern Recognition Pattern recognition is: 1. A research area in which patterns in data are found, recognized, discovered, …whatever. 2. A catchall.

Spam Detection Ethan Grefe December 13, 2013.

Prediction of Influencers from Word Use Chan Shing Hei.

Advisor : Prof. Sing Ling Lee Student : Chao Chih Wang Date :

Twitter Games: How Successful Spammers Pick Targets Vasumathi Sridharan, Vaibhav Shankar, Minaxi Gupta School of Informatics and Computing, Indiana University.

HootSuite #1 Integrating Your Social Media. HootSuite: Integrating & Automating Your Social Media #1 Integrated Dashboard View LI + TW + FB WPB #2 Automated.

Consistency in the spatial structure of surfaces Yukio SADAHIRO Department of Urban Engineering University of Tokyo Analysis of similarity among surfaces.

Date: 2015/11/19 Author: Reza Zafarani, Huan Liu Source: CIKM '15

Recognizing Stances in Online Debates Unsupervised opinion analysis method for debate-side classification. Mine the web to learn associations that are.

Intelligent Database Systems Lab Presenter : WU, MIN-CONG Authors : YUNG-MING LI, TSUNG-YING LI 2013, DSS Deriving market intelligence from microblogs.

Using Social Media for Networking and Marketing Inga Külmoja 25 November 2011.

Click to Add Title A Systematic Framework for Sentiment Identification by Modeling User Social Effects Kunpeng Zhang Assistant Professor Department of.

7-3 DEPRECIATION OBJECTIVES: 1) TO FIND ACCUMULATED DEPRECIATION OF A VEHICLE 2) TO CALCULATE AVERAGE ANNUAL DEPRECIATION OF A VEHICLE 3) TO DETERMINE.

This week on social media Oct 29 th -Nov 4 th. General stats.

HootSuite #1 Integrating Your Social Media. HootSuite: Integrating & Automating Your Social Media #1 Integrated Dashboard View LI + TW + FB WPB #2 Automated.

 Definition of Social Media - forms of electronic communication (as Web sites for social networking and microblogging) through which users create online.

Computational Linguistics Courses Experiment Test.

Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas Cyber Security Research on Engineering Solutions Dr. Bhavani.

Don’t Follow me : Spam Detection in Twitter January 12, 2011 In-seok An SNU Internet Database Lab. Alex Hai Wang The Pensylvania State University International.

Miloš Kotlar 2012/115 Single Layer Perceptron Linear Classifier.

Painting Classification by Artist and Period Using Neural Network Pattern Classification Techniques Stuart Rowan 12/12/2008.

Target Classification in Wireless Distributed Sensor Networks (WSDN) Using AI Techniques Can Komar

CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks Jenny (Bom Yi) Lee.

How To Upload YouTube Video In Facebook?. YouTube Everybody loves YouTube videos. You can watch NASA images from space, daredevils parachuting.

Hawaii Property Management Companies

Best Property Management Company in Oahu

Price List Template -

Social Media Attacks.

Social Networking through New Media

Rosta Farzan and Keyang Zheng, School of Computing and Information

Twitter Augmented Android Malware Detection

Mining the Data Charu C. Aggarwal, ChengXiang Zhai

Marketer Academy: Digital Marketing Course in Ghaziabad

Dieudo Mulamba November 2017

Frontiers of Computer Science, 2015, 9(4):608–622

Deep Learning Research & Application Center

A Network Science Approach to Fake News Detection on Social Media

T H E P U B G P R O J E C T.

Binghui Wang, Le Zhang, Neil Zhenqiang Gong

SOCIAL NETWORK 82% 60% 45% 26% 12% INSTAGRAM FACEBOOK TWITTER GOOGLE+

Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation Binghui Wang, Jinyuan Jia, and Neil.

Ernest Valveny Computer Vision Center

Machine Learning Algorithms – An Overview

Unit 2, Lesson 5 Social Media Marketing for Events

To Start: 10 Points!!! 66 is what percent of 122? 66=x*122 54% What is 32% of 91? x=.32*91 x=29.1.

Yining ZHAO Computer Network Information Center,

When Machine Learning Meets Security – Secure ML or Use ML to Secure sth.? ECE 693.

Presentation transcript:

Discovering the Fake Followers in the Micro-blogging via Machine Learning Yi Shen Jianjun Yu October 16, 2013 Chinese Academy of Sciences Computer Network Information Center, Chinese Academy of Sciences

2 Micro-blogging

3 The Celebrities in Twitter

Purchasing Fake Followers

Fake followers Markets Market Link Price For 1K Followers intertwitter.com$9 solarank.com$6.95 purchase-twitter-followers.net$7.5 fakefollowerstwitter.com$20

39% followers are fake 34% followers are fake 31% followers are fake 32% followers are fake 32% followers are fake 33% followers are fake 27% followers are fake A Data Report

Troubles Caused by Fake Followers Noise for Social Network analysis Privacy and Security Problem Spam Problem

Method of Detection Binary Classification Problem Extract discriminative features Voting-SVM as the classifier

How to get ground-truth data? Purchase from different merchants Keep tracking them for a long period

The Features for Classification The Ratio of Followee Count and Follower Count (RFF) The Percentage of Bidirectional Friends (PBF) Average Repost Frequency of the Posts (ARF) Ratio of the Original Posts (ROP) Proportion of Nighttime Posts (PNP) Topic Diversity The standard deviation of post-count(σ post ). The general slope of post-count(g post ). The standard deviation of followee-count(σ followee ) The decrease frequency of followee-count(DF followee ). The standard deviation of follower-count (σ follower ).

Result AccuracyPrecisionRecallF1 98.1%97.7%96.6%0.964

Thank you!