Multi-level stacked ensemble learning for identifying hate speech spreaders on Twitter
Date Issued
2021
Author(s)
Tosev, Darko
Abstract
There are growing signs of discontent with the anti-social behavior expressed on social media
platforms. Harnessing the power of machine learning for the purpose of detecting and
mediating the spread of malicious behavior has received a heightened attention in the last
decade. In this paper, we report on an experiment that examines the predictive power of a
number of sparse and dense feature representations coupled with a multi-level ensemble
classifier. To address the research questions, we have used PAN 2021 Profiling Hate Speech
Spreaders on Twitter task for English language. The initial results are encouraging pointing
out to the robustness of the proposed model when evaluated on the test dataset.
platforms. Harnessing the power of machine learning for the purpose of detecting and
mediating the spread of malicious behavior has received a heightened attention in the last
decade. In this paper, we report on an experiment that examines the predictive power of a
number of sparse and dense feature representations coupled with a multi-level ensemble
classifier. To address the research questions, we have used PAN 2021 Profiling Hate Speech
Spreaders on Twitter task for English language. The initial results are encouraging pointing
out to the robustness of the proposed model when evaluated on the test dataset.
Subjects
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