Scalable and Robust Truth Discovery in Big Data Social Media Sensing Applications

Scalable and Robust Truth Discovery in Big Data Social Media Sensing Applications

Aditya Sharma, Aditi Mittal, Pooja V Deshmukh

Department of Electronics and Telecommunication, Bharati Vidyapeeth (Deemed To Be) University, College Of Engineering Pune

ABSTRACT: In today’s growing world, there is misinformation spread in the form of noisy data which contributes to the false claims. Be it on social media platforms, websites, applications or in television media. In our daily routine, we watch news channels on our television sets where most of the information is hidden. It gives false impression and this is how confusion among the people starts. This gave us an idea to approach to the big picture of all- social media platform where we made “twitter” as our choice. We see daily a number of tweets where people without any proofs make a statement and people out there consider it as a “true fact”. In order to resolve this problem, we devised a method using SRTD (Scalable and Robust Truth Discovery) algorithm through which we can identify the truthfulness of a claim or a statement made via tweets. We tackled three major problems namely “misinformation spread”, “data sparsely” and “scalability”. In particular, the SRTD method uses a principled approach to jointly quantify the credibility of statements and the trustworthiness of sources.

KEYWORDS: Big Data, Twitter, Sparse Social Media Sensing, Truth Discovery, Scalable