Detection of Violent Speech Against Women in Mexican Tweets Using an Active Learning Approach
Fecha
2024-03-14Autor
García, Vicente
Miranda-Piña, Grisel
Alejo, Roberto
Rendón-Lara, Eréndira
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In Latin American and Caribbean States, the verbal
violence against women on social networks, such as X (formerly
known as Twitter), is a serious threat that has been addressed
through the implementation of social norms, public policies, and
social movements. Nevertheless, a challenge is the effective and
automatic real-time detection of violent tweets. In this sense,
traditional machine learning algorithms have been proposed
to tackle social issues where the training process is performed
in a static manner. However, considering that X is a dynamic
environment where a vast number of tweets are generated each
second, it requires powerful machine learning algorithms that
could exploit this pool of unlabeled data to be incorporated into
the model through continuous updates. This paper explores an
active learning method based on uncertainty sampling, which
identifies the most confusing tweets to be labeled by an expert
in real-time. This focused selection prioritizes which data can be
used to train a multilayer perceptron that can achieve a better
performance with fewer training samples. Experimental results
show that including new samples yields promising results,
increasing the AUC from 0.8712 to 0.8833.