  Online user privacy and tracking have been extensively studied in recent
years, especially due to privacy and personal data-related legislations in the
EU and the USA, such as the General Data Protection Regulation, ePrivacy
Regulation, and California Consumer Privacy Act. Research has revealed novel
tracking and personal identifiable information leakage methods that first- and
third-parties employ on websites around the world, as well as the intensity of
tracking performed on such websites. However, for the sake of scaling to cover
a large portion of the Web, most past studies focused on homepages of websites,
and did not look deeper into the tracking practices on their topical subpages.
The majority of studies focused on the Global North markets such as the EU and
the USA. Large markets such as India, which covers 20% of the world population
and has no explicit privacy laws, have not been studied in this regard.
  We aim to address these gaps and focus on the following research questions:
Is tracking on topical subpages of Indian news websites different from their
homepage? Do third-party trackers prefer to track specific topics? How does
this preference compare to the similarity of content shown on these topical
subpages? To answer these questions, we propose a novel method for automatic
extraction and categorization of Indian news topical subpages based on the
details in their URLs. We study the identified topical subpages and compare
them with their homepages with respect to the intensity of cookie injection and
third-party embeddedness and type. We find differential user tracking among
subpages, and between subpages and homepages. We also find a preferential
attachment of third-party trackers to specific topics. Also, embedded
third-parties tend to track specific subpages simultaneously, revealing
possible user profiling in action.
