Mhd Mousa Hamad, Marcin Skowron, Markus Schedl,
"Regressing Controversy of Music Artists from Microblogs"
: Proceedings of the 30th International Conference on Tools with Artificial Intelligence (ICTAI 2018), 11-2018
Original Titel:
Regressing Controversy of Music Artists from Microblogs
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 30th International Conference on Tools with Artificial Intelligence (ICTAI 2018)
Original Kurzfassung:
Social media represents a valuable data source for
researchers to analyze how people feel about a variety of topics,
from politics to products to entertainment. This paper addresses
the detection of controversies involving music artists, based on
microblogs. In particular, we develop a new controversy detection
dataset consisting of 53,441 tweets related to 95 music artists, and
we devise and evaluate a comprehensive set of user- and contentbased feature candidates to regress controversy. The evaluation
results show a strong performance of the presented approach in
the controversy detection task: F1 score of 0.811 in a classification
task and RMSE of 0.688 in a regression task, using controversy
scores in the range [1, 4].
In addition, the results obtained in applying the presented
approach on a dataset from a different domain (CNN news
controversy) demonstrate transferability of the developed feature
set, with a significant improvement over prior approaches. A
combination of the adopted Gradient Boosting based classifier
and the developed feature set results in an F1 score of 0.775,
which represents an improvement of 9.8% compared to the best
prior result on this dataset
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