Identifying Fake Reviews in Relation with Property and Political Data Using Deep Learning

Abstract

Modern deep learning algorithms have achieved tremendous success in many visual applications by training a model with all relevant task-specific data. In this module fake reviews evolving in online political and property data will be discussed. Regarding political information there are many differences while switching from offline to online mode. The views and comments of the politicians in social media act as a question mark “whether it is true or not?”. In some cases, it appears to be real and sometimes it may be a false/fake statement. All because, the online information is often considered as the most important source of info for the users these kinds of reviews and comments affects the trust factors of social media. Now days in social media, more of toxic contents and unwanted information were present rather than the useful information. These toxic comments acts as a threat for users using the application. Hence users do not come forward to post their thoughts and actions or to share the information. This module is used to distinguish reviews, identify the factor in online mode of political and property comments in social media. The LSTM and BERT (Bidirectional Encoder Representations from Transformers) algorithms are used in the first module because they offer a wide variety of parameters such as learning rates, input and output biases, and text categorization. Additionally, GPT2 (Generative Pre-Trained Transformer 2) is implemented which helps in text generation, increasing the size of dataset for training in different classification models. Thus it is important to understand for us that ultimately the model will be getting trained in such a way that it is able to give accurate results while we are undergoing classification of text and also while we are undergoing the identification of fake/authentic comments that are there in the source of our data.