Identification of Source of Misleading Information and Stop the Dissemination through Blocking the User

Abstract

Introduction: At present, people are more dependent on Internet sources for any sort of information or news. So, the news/information needs to be preserved and should not be modified by any user. Providing security for the news data is a major concern. The decentralized approach of a chain of blocks is used in order to strengthen the security of the news. The existing blockchain framework that offers openness, tamper-proofing, privacy, controlling information, and monitoring is inherited in the proposed work. Precisely, the idea is to build a safe platform that can detect bogus news on social media platforms. Even if the environment is fragile, the chain of blocks-based decentralised peer-to-peer environment provides security to the published information.

Objectives: As a result of recent innovations and advancements in the field of computer technology, social media networks have emerged as one of the most crucial aspects of contemporary human existence. Social media has developed into a well-known platform for information dissemination and news, as well as for daily reports. There are a variety of benefits associated with social media; but, on the converse, there is a great deal of misleading news and data that can mislead the reader. One of the major issues with social media is that there is a dearth of information that can be relied on as well as real world news. Because of misleading news on social media, users are misled. So, to build a trustful environment, early detection of misleading news is necessary. Innovative machine learning methodologies are useful to identify and recognize misleading news more accurately.

Methods: Misleading news is more viral than real news. People instantly believe on the false information. So, there is a need to reduce the dissemination of misleading information on social media. In order to minimize the spread of misleading news, the source of the news needs to be traced. In overall, proposed system utilizes the chain of blocks and applies proposed machine learning methodologies in order to identify misleading news and thereafter reduce the propagation of misleading information by blocking the fake user.  

Results: An experimental analysis reveals that the proposed classification algorithm obtains a better accuracy rate. In order to produce useful training rules and evaluate the test classifier, a number of features are extracted like TF-IDF, N-Gram features, and dependency-oriented NLP features from the data input.

Conclusions: The proposed method analyzes every user's uploaded information, identify fraudulent users and reduce the propagation of false information by blocking the user.