The rise of ad-blockers is viewed as an economic threat by online publishers, especially those who primarily rely on advertising to support their services. To address this threat, publishers have started retaliating by employing ad-block detectors, which scout for ad-blocker users and react to them by restricting their content access and pushing them to whitelist the website or disabling ad-blockers altogether. The clash between ad-blockers and ad-block detectors has resulted in a new arms race on the web.In this paper, we present the first systematic measurement and analysis of ad-block detection on the web. We have designed and implemented a machine learning based technique to automatically detect ad-block detection, and use it to study the deployment of ad-block detectors on Alexa top-100K websites. The approach is promising with precision of 94.8% and recall of 93.1%. We characterize the spectrum of different strategies used by websites for ad-block detection. We find that most of publishers use fairly simple passive approaches for ad-block detection. However, we also note that a few websites use third-party services, e.g. PageFair, for ad-block detection and response. The third-party services use active deception and other sophisticated tactics to detect ad-blockers. We also find that the third-party services can successfully circumvent ad-blockers and display ads on publisher websites.

Abstract:The rise of ad-blockers is viewed as an economic threat by online publishers who primarily rely on online advertising to monetize their services. To address this threat, publishers have started to retaliate by employing anti ad-blockers, which scout for ad-block users and react to them by pushing users to whitelist the website or disable ad-blockers altogether. The clash between ad-blockers and anti ad-blockers has resulted in a new arms race on the Web. In this paper, we present an automated machine learning based approach to identify anti ad-blockers that detect and react to ad-block users. The approach is promising with precision of 94.8% and recall of 93.1%. Our automated approach allows us to conduct a large-scale measurement study of anti ad-blockers on Alexa top-100K websites. We identify 686 websites that make visible changes to their page content in response to ad-block detection. We characterize the spectrum of different strategies used by anti ad-blockers. We find that a majority of publishers use fairly simple first-party anti ad-block scripts. However, we also note the use of thirdparty anti ad-block services that use more sophisticated tactics to detect and respond to ad-blockers.

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Rapid economic growth is often accompanied by environmental depletion. Recent years have witnessed green growth as a renowned efficient approach to tracking the progress towards sustainable development. Green growth is imperative with the current energy depletion rate and environmental crisis. Green indicators and statistics can measure environmentally sustainable development; thus, they evaluate green growth and support its integration into policy. This study uses MCDM (Multi-criteria decision-making) techniques and OECD indicators for the world’s top 10 economies to evaluate their green growth. This research aims to rank the top 10 economies according to green growth. The top-ranked best-performing country will be further analyzed to assess its renewable energy production.

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