Call for Papers -- Application of Big Data in Optimizing Algorithms for Social Network

2024-11-26

Social media network is a marketing technique that enables advertisers to reach specific groups of users on social media platforms. This is achieved by using various data points to create an ideal customer profile and then displaying ads to users who match that profile. Furthermore, social media has revolutionized the way industries and organizations operate, with many enterprises using social media platforms to generate significant data every day. For instance, when a person searches for something on a website and then leaves, they might see related ads the following day when they visit a social network. This happens because optimization algorithms in big data predict the audience's preferences. It is an excellent way for organizations to gather customer information and analyse customer data to understand their behaviour, which influences social network marketing and sales technology recommendations to clients.

The rise of big data is driven by the increasing use of technology and the proliferation of data sources such as social media, the Internet of Things (IoT), and sensor networks. By analysing big data, organizations can obtain valuable insights into customer behaviour, market trends, operational efficiency, and more. This information can be used to make better decisions, improve processes, and develop new products and services. Big data is being used in various industries, such as the government sector, education, healthcare, media and entertainment, weather forecasting, transportation, banking, and more. In the government sector, big data is used to analyse welfare schemes, cybersecurity, and unemployment, and to catch tax evaders. In education, big data can be used to provide customized learning programs, efficient grading and career systems, and e-learning environments. In healthcare, big data can be used to prevent unnecessary diagnoses, detect diseases in their early stages, and generate electronic reports with the history of patients. One of the most powerful applications of big data in social media is to predict the interests of audiences, optimize media streams for distribution platforms, gather information from customers, and target the realm of advertisements.

The emergence of big data, driven by technology and the Internet, had a significant impact on the digital world, particularly in marketing. With big data, organizations can quickly and accurately analyse massive amounts of data from millions of clients to identify their target audience and offer relevant advertisements and marketing strategies. This technology helps improve products and provides new designs and advertisements based on client’s preferences. However, there are challenges related to data privacy, security, Ad fraud, and click fraud in digital advertising. Big data analytics can be used to detect and prevent fraudulent activity in social network advertising by analysing user behaviour patterns and identifying inconsistencies. Algorithms can flag suspicious activity, protecting advertising budgets from being wasted on fraudulent clicks or impressions. Big data is a game-changer in social network ad targeting, enabling more precise audience identification, personalized ad experiences, and continuous optimization. Nevertheless, it is vital to use this technology ethically and responsibly, respecting user privacy and avoiding discriminatory services.

Topics include, but are not limited to:

  • Data-Driven Model: AI-Optimized Big Data Ad Targeting
  • Real-Time Bidding (RTB): Advertisers, Publishers, Users, Corporates, and Social Media Platforms
  • The Evolution of Social Network Audience Insights to Optimize Campaigns
  • AI and Machine Learning Techniques in Detecting Advertisement Fraud
  • The Synergy of Big Data and AI in Ad Fraud Detection
  • Social Media Advertising Platforms: Facebook, Instagram, LinkedIn, Twitter, and More in Organic Marketing
  • Big Data Analytics for Risk Management in Fraud Prevention
  • Social Media Targeting Strategy: Audience Insights and Intelligence
  • New Ad Target Audience: Market Research, Industry Trends, Social Media, and Analytics Data
  • Enhanced Data Analysis with AI in Real-Time Programmatic Advertising
  • The Impact of Technological Advancements on Digital Marketing and Advertising
  • Leveraging Technology for Social Networks: The Power of Digital Marketing Strategies
  • Precision Marketing Unleashed: AI-Driven Audience Targeting
  • Applications of Big Data Analytics with AI in Internet of Things (IoT) Ad Marketing
  • The Role of Big Data Analytics in Modern Marketing
  • Big Data in Social Media: The Realm of Advertising
  • Big Data Optimization: Recent Advancements and Challenges

 

Guest Editors:
Dr. Abdul Aziz, Universidad de Zaragoza, Zaragoza, Spain
Dr. Moiz Khan Sherwani, University of Copenhagen, Denmark
Dr. Waseem Akram, Khalifa University, UAE

 

Tentative dates:
Submission Deadline: 31 March 2025
Authors Notification: 25 June 2025
Revised Version Submission: 25 August 2025
Final Decision Notification: 05 October 2025