  Filter lists play a large and growing role in protecting and assisting web
users. The vast majority of popular filter lists are crowd-sourced, where a
large number of people manually label resources related to undesirable web
resources (e.g., ads, trackers, paywall libraries), so that they can be blocked
by browsers and extensions. Because only a small percentage of web users
participate in the generation of filter lists, a crowd-sourcing strategy works
well for blocking either uncommon resources that appear on "popular" websites,
or resources that appear on a large number of "unpopular" websites. A
crowd-sourcing strategy will perform poorly for parts of the web with small
"crowds", such as regions of the web serving languages with (relatively) few
speakers. This work addresses this problem through the combination of two novel
techniques: (i) deep browser instrumentation that allows for the accurate
generation of request chains, in a way that is robust in situations that
confuse existing measurement techniques, and (ii) an ad classifier that
uniquely combines perceptual and page-context features to remain accurate
across multiple languages. We apply our unique two-step filter list generation
pipeline to three regions of the web that currently have poorly maintained
filter lists: Sri Lanka, Hungary, and Albania. We generate new filter lists
that complement existing filter lists. Our complementary lists block an
additional 3,349 of ad and ad-related resources (1,771 unique) when applied to
6,475 pages targeting these three regions. We hope that this work can be part
of an increased effort at ensuring that the security, privacy, and performance
benefits of web resource blocking can be shared with all users, and not only
those in dominant linguistic or economic regions.
