User identification is the most important entity to recognize users in information and computer networks. The significant goal of user identification is to maintain the security function of the system. In rapidly developing and dynamic technology, it is necessary to design a credible user identification in information and computer networks. Credible user identification collects unique and reliable information of each user to identify the valid user. Social networking and generated user content help build credible user identification features.
Social network structure and user-generated content are the additional sources to obtain user-s interaction with an organization or individual and the requirement of the user. The deep learning model possesses the ability to handle heterogeneous data, including the social network structure of user and user-generated content, and automatically learn task-specific features from such data. Credible user identification is efficiently performed under the guidance of deep learning using social network structure and user-generated content.
• Social networking imparts a vigorous platform to confer real-world events. The source and users of content are important to maximize the relevancy, credibility, and quality of information generated.
• A trusted social network of users for specific events or situations is enabled by a fine-grained understanding of credible sources and users of the generated information.
• Exploiting deep learning models for credible user identification facilitates trust and credibility in social media analytics.
• Deep learning models learn appropriate features from the huge amount of data generated by social networking.
• Several criteria of user activity in social media are analyzed using deep learning models to determine the credible user of generated content.
• Deep learning assists credible user identification using social network structure, and user-generated content produces feasible outcomes by analyzing the huge data and networking activity of users.
