Attention-Based Context Boosted Cyberbullying Detection in Social Media
(pp418-433)
Nabi Rezvani and Amin
Beheshti
doi:
https://doi.org/10.26421/JDI2.4-2
Abstracts:
Cyberbullying
detection is a rising research topic due to its paramount impact on
social media users, especially youngsters and adolescents. While
there has been an enormous amount of progress in
utilising
efficient machine learning and
NLP
techniques for tackling this task, recent methods have not fully
addressed contextualizing the textual content to the highest
possible extent. The textual content of social media posts and
comments is normally long, noisy and mixed with lots of irrelevant
tokens and characters, and therefore utilizing an attention-based
approach that can focus on more relevant parts of the text can be
quite pertinent. Moreover, social media information is normally
multi-modal in nature and may
contain various
metadata
and contextual information that can contribute to enhancing the
Cyberbullying prediction
system. In this research, we propose a novel machine learning method
that, (i) fine tunes a variant of BERT, a deep attention-based
language model, which is capable of detecting patterns in long and
noisy bodies of text; (ii)~extracts contextual information from
multiple sources including
metadata
information, images and even external knowledge sources and uses
these features to complement the learner model; and (iii)
efficiently combines textual and contextual features using boosting
and a wide-and-deep architecture. We compare our proposed
method with state-of-the-art methods and highlight how our approach
significantly outperforming the quality of results compared to those
methods in most cases.
Key words:
Cyberbullying,
contextualization, deep learning, attention-based models