Abstract: Disclosed is a method and system to assess the veracity of textual input. Upon receiving user-provided content, the system segments the input into fact-oriented textual fragments. Subsequently, search strings are crafted from the fragments and employed to fetch pertinent documents from a designated database. Each extracted document undergoes filtration to distill fact-based content. The initial fragments are then juxtaposed against the distilled content to discern similarities or discrepancies. Culminating the process, the system classifies the content of user into specific veracity categories, ranging from absolute terms like “true” to negations like “false”, with additional nuanced classifications like “misleading” or “outdated” further enhancing the precision of the assessment.

Abstract: A real-time editorial guideline compliance tool is provided. The real-time editorial guideline compliance tool comprises a server to acquire textual content from a user through a computing device, segment the acquired textual content into multiple textual segments, and analyse each extracted textual segment based on a predefined editorial compliance dataset to determine content compliance status. The predefined editorial compliance dataset comprises approved editorial guidelines for textual content writing. The server generates real-time feedback based on the determined compliance status and renders the generated real-time feedback at the computing device, thereby enabling the user to adhere to the approved editorial guidelines.

Abstract: A system to analyze contents from multiple uniform resource locators (URLs) is disclosed. The system comprises a server to acquire URLs from a computing device, each URL corresponding to a unique website. The server renders a minimum processing charge for each URL on a user interface of the computing device. Upon receiving an analysis confirmation input for each URL, the server accesses and generates a data corpus for each webpage. Utilizing a machine learning model, the server computes a billable amount for each URL and renders the computed billable amount on the computing device. Upon receiving an analysis input for each URL, the server executes content analysis to generate and render an analysis outcome for each URL on the computing device.

Abstract: The present disclosure provides a search engine optimization (SEO) system comprising a remote server. The remote server comprises a memory with a set of executable routines and a search engine database with multiple fields of applications, each associated with multiple URLs indexed with a user engagement matrix, written data, and a search engine ranking. A processor acquires a web link from a computing device, extracts textual content, analyzes relevancy, and retrieves relevant URLs. The processor evaluates a thematic score, a readability score, and an emotional tone data using NLP techniques, analyzes written data of each URL to determine a topic weight, a legibility weight, and a sentiment tone data, develops a machine learning model, applies the model to recommend content alterations, and renders the alterations at the computing device.

Abstract: Disclosed is a method and system to assess the veracity of textual input. Upon receiving user-provided content, the system segments the input into fact-oriented textual fragments. Subsequently, search strings are crafted from the fragments and employed to fetch pertinent documents from a designated database. Each extracted document undergoes filtration to distill fact-based content. The initial fragments are then juxtaposed against the distilled content to discern similarities or discrepancies. Culminating the process, the system classifies the content of user into specific veracity categories, ranging from absolute terms like “true” to negations like “false”, with additional nuanced classifications like “misleading” or “outdated” further enhancing the precision of the assessment.

Abstract: The disclosed method and system focus on the analysis and validation of text. The analysis discerns if the text originates from artificial intelligence (AI) mechanisms. The text is then segmented, with each segment undergoing a comparative analysis against indexed content in search engine databases to derive a plagiarism score. The factual statements within the text are isolated and matched with pre-existing data in factual text repositories and the search engine database to ascertain factual accuracy. Furthermore, the system evaluates the readability of the text, while linguistic evaluations, encompassing both grammar and spelling, provide a linguistic correctness score, ensuring the credibility of the text.