An Enhanced Classification Model for Detecting Deceptive Content in Social Media using Natural Language Processing Techniques
Keywords:
Social Media, Deceptive Content, Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), Natural Language ProcessingAbstract
People may now express their thoughts on products, services, motion pictures and other media thanks to the rise of social networking sites. The emotion of the user is their viewpoint or viewpoint on any issue, event, occasion, or service. People's choices have always been influenced by their mental condition in general. Emotions have been extensively studied in natural language in recent years, but many issues need to be addressed. One of its most serious issues is a lack of exact categorization resources. Researchers discovered an unintentionally bias and unfairness generated by data sets used for training, which resulted in the inaccurate classification of harmful terms in context. Several ways to discover toxicity in text are evaluated and reported in this research, with the goal of improving the general standard of text categorization. Suggested methods included a deep learning model of Long- and Short-Term Memory (LSTM) with Glove word embedding and the LSTM with word embedding created by the representations of Bidirectional Encoder Representation from Transformers (BERT). The results showed that LSTM with BERT, as the word embedding attained a satisfactory precision of 94% and a F1 score of 0.89 in the binary categorization of comments (dangerous and nontoxic). The combined use of LSTM and BERT, as the outperformed both LSTM alone and LSTM with Multimodal word anchoring. This work attempts to overcome the challenge of accurately categorizing comments by relating models to bigger corpus of text (good-quality keyword anchoring) rather than training information alone.
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