SOCIAL MEDIA POST & NEWS SUMMERIZER & AUTHENTICITY VERIFICATION

SOCIAL MEDIA POST & NEWS SUMMERIZER & AUTHENTICITY VERIFICATION

Charan Pote, Pranay Tembhurne, Lokesh Kadawe, Pratham Bawankar, Sahil Hatwar, Anshul

Department of Computer Technology Priyadarshini College Of Engineering, Nagpur, India

Abstract—A Social Media Post and News Summarizer with Authenticity Verification is an intelligent web-based system designed to address the growing challenge of misinformation in today’s digital ecosystem. With the exponential increase in online content generated through news platforms and social media, users are frequently exposed to vast amounts of information that may be incomplete, biased, or misleading. The rapid spread of such content creates significant challenges in identifying reliable information and increases the risk of misinformation influencing public perception and decision-making.

Existing systems primarily focus on either automatic content summarization or fake news detection independently, lacking a unified approach that ensures both clarity and credibility. To overcome this limitation, the proposed system integrates advanced Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to perform automated summarization and authenticity verification within a single framework. The system collects data from multiple trusted sources, preprocesses it to remove noise and redundancy, and generates concise and meaningful summaries using AI-based models.

In addition to summarization, the system incorporates a multi-layered authenticity verification mechanism that evaluates information reliability using parameters such as Credibility Score, Agreement Score, and Confidence Level. The Credibility Score assesses the trustworthiness of sources, the Agreement Score measures consistency across multiple sources, and the Confidence Level provides an overall reliability indicator. This structured evaluation enables users to quickly understand not only the content but also its level of trustworthiness.

The platform is developed using a modern and scalable architecture, ensuring efficient data processing and real-time response. It supports seamless user interaction, allowing users to input news articles or social media posts and instantly receive summarized content along with authenticity metrics. The transparent scoring mechanism enhances user trust by clearly indicating how the evaluation is performed.

Furthermore, the system significantly reduces information overload by presenting only essential and verified information, thereby saving time and improving user comprehension. By combining AI-driven summarization with multi-source verification, the proposed system enhances digital literacy, promotes critical thinking, and supports informed decision-making.

Furthermore, the system significantly reduces information overload by presenting only essential and verified information, thereby saving time and improving user comprehension. By combining AI-driven summarization with multi-source verification, the proposed system enhances digital literacy, promotes critical thinking, and supports informed decision-making.

Overall, this system represents a significant advancement toward building a reliable and transparent information ecosystem, where users can access concise, accurate, and trustworthy information in real time. The proposed approach not only addresses current challenges in misinformation detection but also provides a scalable foundation for future enhancements in intelligent information processing systems.