Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

In this system first one is “misinformation spread” where a significant number of sources are contributing to false claims, making the identification of truthful claims difficult. For example, on, Instagram, rumors, Twitter scams, and influence bots are common examples of sources colluding, either intentionally or unintentionally, to spread misinformation and obscure the truth. The challenge is “data sparsity” or the “long-tail phenomenon” where a majority of sources only contribute a small number of claims, providing insufficient evidence to determine those sources’ trustworthiness. For example, in the Twitter datasets that we collected during real-world events, more than 90only contributed to a single claim. Third, many current solutions are not scalable to large-scale social sensing events because of the centralized nature of their truth discovery algorithms. We are going develop a Scalable and Robust Truth Discovery (SRTD) scheme to address the above all challenges. In this, the SRTD scheme jointly quantifies both the reliability of sources and the credibility of claims using a principled approach.

Keywords: Database Management, Query Processing, Scalable and Robust Truth Discovery (SRTD), Truth Discovery