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In this study, we aimed to address the growing concern of trolling behavioron social media by developing and evaluating a set of model architectures forthe automatic detection of troll tweets. Utilizing deep learning techniques andpre-trained word embedding methods such as BERT, ELMo, and GloVe, we evaluatedthe performance of each architecture using metrics such as classificationaccuracy, F1 score, AUC, and precision. Our results indicate that BERT and ELMoembedding methods performed better than the GloVe method, likely due to theirability to provide contextualized word embeddings that better capture thenuances and subtleties of language use in online social media. Additionally, wefound that CNN and GRU encoders performed similarly in terms of F1 score andAUC, suggesting their effectiveness in extracting relevant information frominput text. The best-performing method was found to be an ELMo-basedarchitecture that employed a GRU classifier, with an AUC score of 0.929. Thisresearch highlights the importance of utilizing contextualized word embeddingsand appropriate encoder methods in the task of troll tweet detection, which canassist social-based systems in improving their performance in identifying andaddressing trolling behavior on their platforms.

A Context-Sensitive Word Embedding Approach for The Detection of Troll Tweets