Description:FIELD OF THE INVENTION
This invention relates to Profiling Irony and Stereotype Spreaders on Twitter using NLP
BACKGROUND OF THE INVENTION
Twitter have certain effects on the discourse system. Disturbing and stereotyping other users is rather difficult as the content often depends on the formal or informal context of communication. Most models have difficulties in differentiating what might be true statements and sunders from what could be statements that might be laced with irony or stereotype. As a result, this study’s purpose is to design an effective NLP model that will effectively capture irony and stereotype spreaders in Twitter using new-generation approaches such as machine learning and deep
• Sentiment analysis systems for identifying positive, negative, or neutral sentiments in social media content.
• Irony and sarcasm detection models using Natural Language Processing (NLP) to analyze textual context.
• Hate speech detection systems designed to detect and flag offensive or harmful language.
• Content moderation tools employing AI to monitor and filter stereotypical or biased language.
• Context-aware language models that attempt to understand deeper linguistic nuances, though often with limitations in accurately detecting irony and stereotypes.

The existing system needs to:
• Lack of specialized models for profiling irony and stereotype spreaders on Twitter.
• Inability to effectively analyze the nuanced context of sarcastic and biased content using Natural Language Processing (NLP).
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The proposed invention is an NLP-based system for the identification of the irony and stereotype sprees on the Twitter platform. This system uses machine learning and deep learning to compute irony propagation and stereotype situations from the content shared on the social media platforms.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The proposed invention is an NLP-based system for the identification of the irony and stereotype sprees on the Twitter platform. This system uses machine learning and deep learning to compute irony propagation and stereotype situations from the content shared on the social media platforms.
The core components of the system include:
1. Text Pre-processing and Contextual Analysis:
o It preprocesses the textual reads the data from the tweeter, extracts it and then pre-process it.
o Reviews are analysed using BERT or GPT techniques where the meaning of the text is taken into account through contextual embeddings.
2. Irony and Stereotype Detection Module:
o Special attention is paid to the creation of a focused classifier using the methods of supervised and even semi-supervised learning to address the problem of detecting sarcastic or stereotype statements.
o The text, hashtags and emojis that enter the system are also used to boost the detection capability.
3. User Profiling and Behavior Analysis:
o The system collects data related to regular and power users in order to detect patterns of irony and stereotype content sharing.
o The kind of influence to be analyzed and the user intent is determined by sentiment analysis and user network analysis.
4. Explainable AI (XAI) Integration:
• Explainability methods are used to explain the outcome in a clear and understandable form so as to be able to trust the outcome.
5. Real-Time Monitoring and Reporting:
• It can also operate in real-time to prevent and, in case of violations, notify the corresponding social media platforms of the need for content moderation and non-constructive behaviour.
This innovative system addresses the challenges of sarcasm detection and stereotype identification, ensuring better online discourse analysis and fostering healthier digital
Environments.
NOVELTY:
The proposed system presents a new NLP-based model of interference based on analyzing context, multimodality, and xAI to identify irony and stereotype spreaders on Twitter more effectively than current sentiment analysis and hate speech models.
ADVANTAGES OF THE INVENTION
Enhanced Contextual Understanding: In contrast with previous methods of sentiment analysis, the work presented in this paper utilizes transformer models and is able to detect irony and stereotype-related content.
Multimodal Integration: It entails text, emojis and other image-based information for analysis, often left out by prior solutions.
Real-Time Detection: It is an online system, which can detect and respond to incidents quicker than the current batch process-based methods.
Explainable AI: It also resolves the ‘black box’ problem of previous solutions by providing the results in an interpretable format that users can comprehend.
Cross-Platform Adaptability: Compared with existing subjective safety models, this work is more focused on a particular type of social space, but the proposed solution is anomorphic and can be applied to different social media platforms and different cultures.
, Claims:1. An NLP-based system for identifying irony and stereotype content, comprising: a text acquisition module, an AI module, a user profiling and behaviour analysis module and a real-time monitoring and reporting module.
2. The system as claimed as claim 1, wherein the system configured to clean and tokenize the extracted data, and to generate contextual embeddings using pretrained deep learning models including BERT or GPT.
3. The system as claimed as claim 1, wherein the text acquisition module configured to extract textual content, including posts, hashtags, and emojis, from user-generated data on the social media platform.
4. The system as claimed as claim 1, wherein the AI module configured to provide interpretable justifications for classification outcomes using explainability techniques including LIME or SHAP.
5. The system as claimed as claim 1, wherein the system configured to detect policy-violating content and generate alerts or moderation recommendations to social media platforms.