Date of Award
Santa Clara : Santa Clara University, 2020.
Computer Science and Engineering
With the introduction of social media, the internet is filled with an excess of data and content. Users feeds are cluttered with fake, malicious, and unnecessary information, polluting their page and wasting their time. As observed in the 2016 US election, spam accounts posting fake news were successfully able to sway political opinion and misinform the general population. Additionally, with social media becoming one of the biggest advertising markets, there is a rise in the number of fake accounts with a large number of followers that mainly consist of bots. It should be a priority to protect the public from false information and businesses that want to make money on social media platforms. Through utilizing Natural Language Processing, image recognition, and recommendation systems, powered through AI and Machine Learning, the goal of our project is to provide the user with content that is verified and tailored to their liking. This report details our plan to mitigate the amount of unnecessary content displayed in front of the user, and the rationale for our designs. It provides a guide for all the completed work, future iterations, performance results, and the reliability of our model as an efficient solution.
Sehgal, Hetesh; Chen, Xinyu; and Jiang, Kyle, "Mitigating Fake Digital Media and Quality Assurance" (2020). Computer Science and Engineering Senior Theses. 171.