• Ch. Shravya
• S. Naga Raju

Detecting trustworthy information within the sight of noisy data contributed by different unvetted sources from online social media (e.g., Twitter, Facebook, and Instagram) has been a pivotal errand in the period of big data. This assignment, alluded to as truth discovery, focuses at identifying the unwavering quality of the sources and the truthfulness of cases they make without knowing either apriori. In this work, we distinguished three important difficulties that have not been all around tended to in the present truth discovery writing. The first is "misinformation spread" where an important number of sources are contributing to false claims, making the ID of truthful claims troublesome. For instance, on Twitter, gossipy tidbits, tricks, and influence bots are basic instances of sources colluding, either intentionally or unintentionally, to spread misinformation and darken the truth. The second test is "data sparsity" or the "long-tail phenomenon" where a dominant part of sources just contributes few cases, providing insufficient proof to determine those sources' trustworthiness. For instance, in the Twitter datasets that we gathered during certifiable occasions, over 90% of sources just added to a single case. Third, many current arrangements are not scalable to large-scale social sensing occasions on account of the brought together nature of their truth discovery calculations. In this paper, we build up a Scalable and Robust Truth Discovery (SRTD) plan to address the over three difficulties. Specifically, the SRTD conspire jointly measures both the unwavering quality of sources and the believability of cases using a principled methodology. We further build up a disseminated framework to actualize the proposed truth discovery conspire using Work Queue in a HTCondor framework.

"Efficient Truth Detection in BigData Online Social Media Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.507-510, May-2019, Available :http://www.jetir.org/papers/JETIR1905R76.pdf

"Efficient Truth Detection in BigData Online Social Media Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp507-510, May-2019, Available at : http://www.jetir.org/papers/JETIR1905R76.pdf