Detect Toxic Content to Improve Online Conversations
This work addresses the issue of toxic content for social media users, but it is incremental as it applies existing methods to a known dataset.
The paper tackled the problem of detecting toxic content, specifically insincere questions, to improve online conversations, achieving results by training and comparing various machine learning and deep learning models on the Quora Insincere Questions Classification dataset.
Social media is filled with toxic content. The aim of this paper is to build a model that can detect insincere questions. We use the 'Quora Insincere Questions Classification' dataset for our analysis. The dataset is composed of sincere and insincere questions, with the majority of sincere questions. The dataset is processed and analyzed using Python and its libraries such as sklearn, numpy, pandas, keras etc. The dataset is converted to vector form using word embeddings such as GloVe, Wiki-news and TF-IDF. The imbalance in the dataset is handled by resampling techniques. We train and compare various machine learning and deep learning models to come up with the best results. Models discussed include SVM, Naive Bayes, GRU and LSTM.