Curbing Crowdturfing in Online Social Networks

Submission Deadline: 31 May 2017

IEEE Access invites manuscript submissions in the area of Curbing Crowdturfing in Online Social Networks.

Online social media is reshaping the way businesses manage their sales and marketing assets. Unlike traditional media, such as TV, radio or newspapers, social media is characterized by user contributions, sharing, decentralization, and being free. In addition to gaining phenomenal popularity as the Web becomes accessible via all sorts of devices, they also have a strong influence on brands, making social media a force that many organizations can no longer ignore.

Public relations companies have hired people 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 “crowdturfing” or “cyber-gossips”, which can mislead online users, and put the individuals or a business in a compromising position or at serious risk.

The goal of this Special Section in IEEE Access is to solicit the latest theoretical and research application output for curbing crowdturfing. We also welcome survey or tutorial style articles with clear application background. This Special Section will focus on the following topics, and but are not limited to:

(1) Content Based Methods: Opinion 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


We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Dr. Gang Li, Deakin University, Australia

Guest Editors:
1. Prof. Jianlong Tan, Chinese Academy of Sciences, China
2. Prof. Lynn Batten, Deakin University, Australia
3. Prof. Sohail S. Chaudhry, Villanova University, USA


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: Bora M. Onat, Managing Editor, IEEE Access (Phone: (732) 562-6036,