Enhancing Immoral Post Detection on Social Networks Using Knowledge Graph and Attention-Based BiLSTM Framework

Abstract

Preserving a secure and morally safe online environment on social media is a challenging task. It is essential to find immoral or unsuitable information in user-generated postings to safeguard users and enforce community standards. Various Natural Language Processing (NLP) approaches are being employed to detect subtle immoral posts; however, there remains a research gap due to the semantic and contextual complexity of natural language. To bridge this gap, this work proposes the use of a Knowledge Graph (KG) for entity recognition and the extraction of semantic relationships in Social Network (SN) posts. By doing so, the KG helps provide a deeper contextual understanding, enabling the detection of negative interactions between entities that are often present in immoral content. KG allows us to extract these associations from the text, enabling the model to recognize language that leads to immoral behavior. By utilizing a KG, the model can more easily identify connections between entities, verify statements made in postings, and classify material more precisely. GloVe (Global Vector) word embedding is used to transform the enriched text data into numerical representations. An attention-based Bidirectional Long Short-Term Memory (BiLSTM) network performs the classification task. The BiLSTM concurrently analyses the input sequence in both directions, enabling the network to recognize not only the context that is present at the moment but also the context in which each word in the sequence comes before and after. To validate the model performance, we used benchmark datasets Self-Annotated Reddit Corpus (SARC), and Hate Evaluation (HatEval) dataset. We achieved a higher F1-score of 82.79% and 84.06% on both datasets and outperformed state-of-the-art works.

Keywords

• Immoral posts
• post detection
• NLP
• social media
• knowledge graph
• BiLSTM network