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C3 2014 - The 2014 International Workshop on Curbing Collusive Cyber-gossips in Social Networks

Date2014-08-17

Deadline2014-05-25

VenueBeijing, China China

Keywords

Websitehttp://www.tulip.org.au/research/c3-2014

Topics/Call fo Papers

The emergence of social media is now reshaping the way businesses manage their sales and marketing assets. Unlike traditional media such as the TV, radio or newspaper, the social media (e.g., FaceBook, YouTube, Twitter, TripAdvisor, VirtualTourist, Houzz, Sina Weibo, Tencent Weixin and many other Web 2.0 sites) is characterized by user contributions, sharing, decentralization and being free. Not only are they gaining phenomenal popularity as the Web becomes accessible via all sorts of devices, they also have a strong influence on a brand making it a force that many organizations can no longer ignore. Many of us would have sought online reviews before making a purchase decision or forming an opinion, so do the rest of the consumers.
Unlike traditional media and sites, social media rely on user-generated content. Unfortunately, many of the user-generated content may not be that genuine as expected. It has been found that online paid posters have been hired by public relationship companies to post product comments on different online communities and social networks, without even consuming the services or products. While online paid posters can be used as an efficient e-marketing strategy, they can also act maliciously by spreading gossip or negative information about competitors. More specifically, a group of paid posters could operate with well-coordinated attacks, and generate a desired result of positive or negative opinions, to attract attention or trigger curiosity. This is known as “cyber-gossips”, which can mislead online users, and put the individuals or a business in a compromising position or at serious risk.
So powerful are these online opinions that businesses cannot ignore their impact on its bottom line. If businesses do not manage their online reputation properly, they risk damaging their brand and sales assets. And the battlefield to do so is clearly played out in the social media arena.
Recent years have witnessed increasing research attention on spammer detection in social networks. This trend raises the need for launching the International Workshop on Curbing Collusive Cyber-gossips in Social Networks (C3). This workshop will be the premier forum in which curbing cyber gossips is promoted as a serious and important research field by its own with relevant challenging problems and emerging issues to be formally addressed.
C3-2014 aims to increase potential collaborations and partnerships by bringing together academic researchers and industry practitioners from data mining, network security, digital forensics, behavioral and psychology sciences with the objectives to present updated research efforts and progresses on foundational and emerging topics of C3, exchange new ideas and identify future research directions.
Topics of Interests
C3-2014 calls for original papers focus on but not limited to the following topics:
(1) Content Based Methods: Information / Opinion / Knowledge Modeling and Spread Analysis
Agent-based data retrieval
Complex sequence analysis
Content and Opinion analysis
Temporal-sequential pattern mining
Impact-oriented pattern mining
Event/activity/action filtering
Multi-granularity data visualization
(2) Behavior Based Methods: Behavior Modeling and Mining
Behavior structure extraction
Behavior life cycles
Sequential/Parallel/Distributed behavior modeling
Behavior dynamics
Cyber Criminal behavior analysis
Social networking behavior analysis
(3) Social Relation Based Methods: Cyber Analysis
Group and group behavior detection, tracking and recognition
Collusive crime/piracy detection
Graph-based behavior/social modeling
Dynamic/hidden group presentation
Collaborative social recommendation
Group interaction, collaboration, representation and profiling
Cyber-Gossip Spread Models
(4) Applications and Open Case Study
Poster spam detection
Blog spam detection
Click spam detection
Identity authentication
Botnets prevention
Datasets for cyber-gossips detection

Last modified: 2014-04-19 21:33:54