  In this study, we aimed to address the growing concern of trolling behavior
on social media by developing and evaluating a set of model architectures for
the automatic detection of troll tweets. Utilizing deep learning techniques and
pre-trained word embedding methods such as BERT, ELMo, and GloVe, we evaluated
the performance of each architecture using metrics such as classification
accuracy, F1 score, AUC, and precision. Our results indicate that BERT and ELMo
embedding methods performed better than the GloVe method, likely due to their
ability to provide contextualized word embeddings that better capture the
nuances and subtleties of language use in online social media. Additionally, we
found that CNN and GRU encoders performed similarly in terms of F1 score and
AUC, suggesting their effectiveness in extracting relevant information from
input text. The best-performing method was found to be an ELMo-based
architecture that employed a GRU classifier, with an AUC score of 0.929. This
research highlights the importance of utilizing contextualized word embeddings
and appropriate encoder methods in the task of troll tweet detection, which can
assist social-based systems in improving their performance in identifying and
addressing trolling behavior on their platforms.
