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Sunday, October 22 • 2:20pm - 2:50pm
Fake News Detection Using Machine Learning and Blockchain Technology

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After the 2016 US presidential election, many people shocked with the outcome, looked for the cause of the “surprising” result. Some blamed the outcome on the fake news phenomenon during the election: it painted false images of candidates for better or for worse. A majority of the most viral news articles covering the election turned out to be fake stories . We created Geppetto. Geppetto is a platform that identifies valid contents in a news article. Geppetto can accurately identify and screen fake news in real-time, using tamper-proof and cost-effective methods that preserves trust in the process. Users can communicate with the Geppetto platform in three ways; (1) as a publisher who can publish its own article on the platform, (2) as a voter to validate/dis-validate a news, (3) as a consumer who searches a news on this platform.The platform requires a database to store the newly published news and verified news by the voters. There is a veracity engine which consists of several natural language processing and machine learning models that can score the legitimacy of the articles and the reliability of validators (voters) and publishers. Because fake news evolves rapidly, the performance of any model built on the historical data can decay rapidly. To address this, we use a continuous learning (CL) algorithm to update the model continuously. The CL component requires a knowledge base to store historical data and re-evaluate the models. Geppetto uses blockchain technology to ensure the system’s data is tamper-proof and decentralized.

avatar for Farshad Kheiri

Farshad Kheiri

Lead Data Scientist, BCG Digital Ventures
Farshad is a Lead Data Scientist at BCG DV; he has been working on several projects used machine learning including deep learning, natural language processing, and other data science techniques for several projects. On one of his last project, he has used data science and block-chain... Read More →

Sunday October 22, 2017 2:20pm - 2:50pm PDT
Room E Room E