Abstract:

Social networking platforms have become a vital medium for advertisement campaigns due to their extensive user base and engagement potential. Social media platforms such as Facebook, Twitter, and WhatsApp have gained immense popularity in recent years as a means of marketing communication. These platforms, commonly known as social networking sites, have become a prominent part of the online world. Facebook, in particular, positions itself as an ideal marketing tool, offering an advertising system that enables businesses to access and target the information of every Facebook user. However, despite the widespread use of Facebook for advertising, this study reveals that users' purchasing decisions are not influenced by ads, and they do not actively rely on Facebook as a source of information. Nevertheless, Facebook provides an excellent platform for facilitating communication between organizations and users. This research paper presents an experimental study that investigates the effectiveness of utilizing neural networks and Support Vector Machine (SVM) classification techniques for social networking advertisement. The study aims to compare the results obtained from these two approaches and analyze their performance in terms of accuracy, precision, and recall.

Description:

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