Automating the Ranking of Article Visibility through Crowdsourced Trustworthiness; Reducing Online Political Polarization in America 481 views

Political polarization is rising in the United States. How may political polarization among Americans be reduced? How can machine learning and web programming languages improve the accuracy of consumer media? As Americans continue to rely on social media for news information, the presentation and fact verification of news spread online will be important. The spread of conspiracy theories and resulting violence indicate the harmful results of political polarization. Current social media platforms rely on labeling and corrections and are ineffective when not paired with other forms of anti-misinformation. An application incorporating methods of anti-misinformation like crowdsourced trustworthiness aims to decrease political polarization. Application user usage verifies that the application correctly identifies misinformation. If successful, the application adds to current research on alternative anti-misinformation methods. Since 2016, how have anti-disinformation proponents mobilized to counteract what they regard as disinformation in U.S. social media? Breaking apart echo chambers and increasing media literacy will decrease political polarization in the U.S. Echo chambers, filter bubbles, and opinion influencers confirm confirmation bias and politically polarize users. Researchers found that there are multiple techniques to combat confirmation bias and disband echo chambers. Social media platforms like Facebook, Twitter, Parler or Gab as well as lawmakers, conspiracy theorists and fact-checking organizations take part in the fortification of echo chambers and misinformation spread and prevention. If current methods of filtering information on social media platforms are found to markedly increase political polarization, it will be important to investigate methods to prevent this growing trend of political polarization and instability in the U.S.

BS (Bachelor of Science)

disinformation; misinformation; anti-misinformation; anti-disinformation; anti-polarization; political polarization; polarization; fact-checking; fact-checkers; fact-checking organizations; labeling; echo chambers; filter bubbles; confirmation bias; bias; social media; social networks; media; news media; media literacy; Gab; Parler; Twitter; Facebook

School of Engineering and Applied Sciences Bachelor of Science in Computer Science Technical Advisor: Upsorn Praphamontripong Technical Advisor: Daniel Graham STS Advisor: Peter Norton Technical Team Members: Christine Baca

English

2021-05-10