  From popular uprisings to pandemics, the Web is an essential source consulted
by scientists and historians for reconstructing and studying past events.
Unfortunately, the Web is plagued by reference rot which causes important Web
resources to disappear. Web archive collections help reduce the costly effects
of reference rot by saving Web resources that chronicle important
stories/events before they disappear. These collections often begin with URLs
called seeds, hand-selected by experts or scraped from social media. The
quality of social media content varies widely, therefore, we propose a
framework for assigning multi-dimensional quality scores to social media seeds
for Web archive collections about stories and events. We leveraged
contributions from social media research for attributing quality to social
media content and users based on credibility, reputation, and influence. We
combined these with additional contributions from the Web archive research that
emphasizes the importance of considering geographical and temporal constraints
when selecting seeds. Next, we developed the Quality Proxies (QP) framework
which assigns seeds extracted from social media a quality score across 10 major
dimensions: popularity, geographical, temporal, subject expert, retrievability,
relevance, reputation, and scarcity. We instantiated the framework and showed
that seeds can be scored across multiple QP classes that map to different
policies for ranking seeds such as prioritizing seeds from local news,
reputable and/or popular sources, etc. The QP framework is extensible and
robust. Our results showed that Quality Proxies resulted in the selection of
quality seeds with increased precision (by ~0.13) when novelty is and is not
prioritized. These contributions provide an explainable score applicable to
rank and select quality seeds for Web archive collections and other domains.
