Detection of Truth Discovery in Big Data Social Media Sensing Applications
A Hemadri Naidu1, J Naga Muneiah2
1Mr. A Hemadri Naidu, M. Tech Student, Dept. of CSE, Chadalawada Ramanamma Engineering College, Tirupati, India.
2Prof. J Naga Muneiah, Head Department, of CSE, Chadalawada Ramanamma Engineering College, Tirupati, India.
Manuscript received on November 23, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1502-1507 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3557129219/2020©BEIESP | DOI: 10.35940/ijeat.B3557.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A With the rapid growth of online social media and ubiquitous Internet connectivity, social sensing has emerged as a new crowd sourcing application paradigm of collecting observations (often called claims) about the physical environment from humans or devices on their behalf. A fundamental problem in social sensing applications lies in effectively ascertaining the correctness of claims and the reliability of data sources with out knowing either of them a priori, which is referred to as truth discovery. While significant progress has been made to solve the truth discovery problem, some important challenges have not been well addressed yet. First, existing truth discovery solutions did not fully solve the dynamic truth discovery problem where the ground truth of claims changes over time. Second, many current solutions are not scalable to large-scale social sensing events because of the centralized nature of their truth discovery algorithms. Third, the heterogeneity and unpredictability of the social sensing data traffic pose additional challenges to the resource allocation and system responsiveness. In this paper, we develop a Scalable and Robust Truth Discovery (SRTD) scheme to address the above three challenges. In particular, the SRTD scheme jointly quantifies both the reliability of sources and the credibility of claims using a principled approach. The evaluation results on three real-world data traces (e., Boston Bombing, Paris Shooting and College Football) show that the SSTD scheme is scalable and outperforms the state-of-the- art truth discovery methods in terms of both effectiveness and efficiency.
Keywords: Big Data, SRTD, Data Sparsity, Robust, Social Media Sensing.