In the present era of the distributed system where almost the complete world has been engaged in social networking, how one can claim that he/she get the real authenticated content. Since the content on the internet is not verified especially the social media content where the people post mostly the doubtful information. The main difficult problem is the filtering of truth from such contents. In such a situation social media find the new challenge of establishing veracity (doubtable data). So a new system PHEME is going to establish for analyzing the content in social sites, blogs and socially related posts based on the language and determine the uncertainty or the doubts in the content. This system will help not only in medical information systems (where causes serious damages if the wrong information held) but also in digital journalism.
Inhaltsverzeichnis (Table of Contents)
- Introduction
- Identification
- Classification of Information
- Architecture
- Detection
- Conclusion
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
The primary objective of this work is to develop a system capable of detecting and classifying false information disseminated on social media platforms. The system aims to analyze social media posts, blogs, and other related content to identify uncertain or dubious information, thereby promoting the dissemination of truthful and reliable content.
- Detection of false information in social media content
- Analysis of language and semantics to identify dubious information
- Development of a system for identifying and classifying misinformation
- Application of natural language processing (NLP) and data mining techniques
- Contribution to the advancement of truth verification in online environments
Zusammenfassung der Kapitel (Chapter Summaries)
- Introduction: This chapter introduces the problem of false information spreading on social media platforms and highlights the need for a system to verify the authenticity of online content. It presents the PHEME system, which aims to analyze social media posts and identify uncertain or dubious information.
- Identification: This chapter details the techniques used by PHEME to identify false information. These techniques include NLP, data mining, web technologies, social networking analysis, and information visualization. The chapter describes how PHEME combines these techniques to analyze the realism of content and identify potential misinformation.
- Classification of Information: This chapter discusses the classification of false information into four categories based on semantics and language context: speculation, rumor, propaganda, and hoax. It provides a brief overview of each category and its characteristics.
Schlüsselwörter (Keywords)
The main keywords and focus topics of this work are social media, misinformation, truth verification, natural language processing (NLP), data mining, information visualization, PHEME system, semantic analysis, content authenticity, online content validation, and digital journalism.
- Arbeit zitieren
- M.Tech Hemant Kumar Saini (Autor:in), Nitesh Chouhan (Autor:in), 2014, Detecting Veracity, München, GRIN Verlag, https://www.grin.com/document/285280
