  A growing number of people are using catch-up TV services rather than
watching simultaneously with other audience members at the time of broadcast.
However, computational support for such catching-up users has not been well
explored. In particular, we are observing an emerging phenomenon in online
media consumption experiences in which speculation plays a vital role. As the
phenomenon of speculation implicitly assumes simultaneity in media consumption,
there is a gap for catching-up users, who cannot directly appreciate the
consumption experiences. This conversely suggests that there is potential for
computational support to enhance the consumption experiences of catching-up
users. Accordingly, we conducted a series of studies to pave the way for
developing computational support for catching-up users. First, we conducted
semi-structured interviews to understand how people are engaging with
speculation during media consumption. As a result, we discovered the
distinctive aspects of speculation-based consumption experiences in contrast to
social viewing experiences sharing immediate reactions that have been discussed
in previous studies. We then designed two prototypes for supporting catching-up
users based on our quantitative analysis of Twitter data in regard to reaction-
and speculation-based media consumption. Lastly, we evaluated the prototypes in
a user experiment and, based on its results, discussed ways to empower
catching-up users with computational supports in response to recent
transformations in media consumption.
