—This paper is devoted to predicting trends on the
social media. Typical methods in the literature are based on
temporal changes in usage of words or phrases on the media,
and try to find a rapid increase, called a burst, of them.
Therefore, these methods can be applied only after a burst is
emerging. In this paper, we propose an index, called the
infectious capacity, to detect potential trends on the social media
before they would emerge. To achieve this, we focus on labels
and items, and predict trends of a label, instead of those of a
target object, such as contents of a social media, where an item
is a concept represented by an object and a label categories
items. On a photo sharing service, for example, a photo is an
object, a tag is a label, and concepts represented by a photo are
items for the photo. Using labels and items, the infectious
capacity for a label is defined as the ratio of the variety of items
with the label to the number of occurrences of the label in given
data. That is, the larger value an infectious capacity of a label is,
more infectious the label is. Our experiments on real data
showed that the infectious capacities for most labels are
substantially constant over time. This result means that we can
forecast the variety per usage for a label just after the label is
used. Moreover, we found that infectious capacities for popular
labels have similar values. Combined with the first result, we
are able to predict latent trends before labels become popular.
In fact, this is also supported by experiments on tweets, where
we were able to find potentially popular hashtags, regarding
hashtags as labels, before they become popular. As far as the
authors know, this is the first result of future trend prediction
on the social media.
—Category, constant index, future trend
The authors are with the Department of Informatics, Kyushu University,
Japan (e-mail: email@example.com,
Cite: Yuki Sonoda and Ikeda Daisuke, "Predicting Latent Trends of Labels in the Social Media Using Infectious Capacity," International Journal of Future Computer and Communication vol. 4, no. 6, pp. 374-380, 2015.