A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers Model

Abstract

The volume of audio visual content produced on social networks has increased tremendously in recent decades, and this information is quickly spread and consumed by a large number of people. The disruption of false news sources and bot accounts for disseminating fake news is a possibility in this scenario. Applied research has been supported by promotional information as well as sensitive stuff over the network. Artificial Intelligence will be used to automatically assess the trustworthiness of social media accounts (AI). In this research, we describe a multilingual strategy to using Deep Learning to solve the bot identification problem on Twitter. End-users can utilise machine learning (ML) methodologies to assess the trustworthiness of a Twitter account. To achieve so, a number of tests were carried out using cutting-edge Multilingual Language Models. Construct an encoding of the user account's text-based features, which is then concatenated with the rest of the metadata to build a potential input vector on top of a Bot-DenseNet Dense Network. As a result, this article evaluates the language constraint from prior experiments where the encoding of the language was limited. Only the metadata information or the metadata information along with some other information was examined by the user account. properties of fundamental semantic text The Bot-DenseNet also generates a low-dimensional representation of the data. Within the Information Retrieval (IR) framework, a user account can be utilised for any application.