Analysis of Unwanted Advertising Traffic: An Economic and User Experience Perspective

The current general business model of the Internet is based on the idea that content and services provided for free are monetized through online advertisement [1]. Such a model relies on the implicit agreement between providers and users that will access the content. In such a direction, it is well-known that providers’ load and redirects users to unwanted advertising. This can impact both economic and performance aspects such as bandwidth wasted to load the ads and more time to access the content itself [2].

The goal of this thesis is to develop a tool to measure traffic waste, economic aspects, and impacts on user experience by unwanted advertising. For this, the student should understand the tools available to identify and block ads on the Internet (e.g., Pi-hole [3] and Ad-Block). Next, a simulator of different Internet surfing behaviors [4] [5] is to be designed and implemented (e.g., mobile, domestic, and corporate users). Finally, the advertising traffic has to be analyzed to understand the impact on costs and navigation experience concerning the data Internet plans available in the market.

References:

[1] Daniel G. Goldstein, R. Preston McAfee, and Siddharth Suri. 2015. The cost of annoying ads. SIGecom Exch. 13, 2 (January 2015), 47-52. 

[2] Enric Pujol, Oliver Hohlfeld, and Anja Feldmann. 2015. Annoyed Users: Ads and Ad-Block Usage in the Wild. In Proceedings of the 2015 Internet Measurement Conference (IMC '15). ACM, New York, NY, USA, 93-106.

[3] Pi-hole: A black hole for Internet advertisements. Available at https://pi-hole.net/ Accessed 14 February 2019.

[4] M. Kihl, P. Ödling, C. Lagerstedt and A. Aurelius, "Traffic analysis and characterization of Internet user behavior," International Congress on Ultra Modern Telecommunications and Control Systems, Moscow, 2010, pp. 224-231.

[5] J. Yang, Y. Qiao, X. Zhang, H. He, F. Liu, and G. Cheng, "Characterizing User Behavior in Mobile Internet," in IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 1, pp. 95-106, March 2015.