| dc.description.abstract | Mining and exploitation of data in social networks has been the focus of many efforts,
but despite the resources and energy invested, still remains a lot for doing given its
complexity, which requires the adoption of a multidisciplinary approach . Specifically,
on what concerns to this research, the content of the texts published regularly, and
at a very rapid pace, at sites of microblogs (eg Twitter.com) can be used to analyze
global and local trends. These trends are marked by microblogs emerging topics that are
distinguished from others by a sudden and accelerated rate of posts related to the same
topic; in other words, by an increment of popularity in relatively short periods, a day or
a few hours, for example Wanner et al. . The problem, then, is twofold, first to extract
the topics, then to identify which of those topics are trending. A recent solution, known
as Bursty Biterm Topic Model (BBTM) is an algorithm for identifying trending topics,
with a good level of performance in Twitter, but it requires great amount of computer
processing. Hence, this research aims to determine if it is possible to reduce the amount
of processing required and getting equally good results. This reduction carry out by a
discrimination of co-occurrences of words (biterms) used by BBTM to model trending
topics. In contrast to our previous work, in this research, we carry on a more complete
and exhaustive set of experiments. | |
