Machine Learning Techniques for Analysis of Political Campaign
| dc.contributor.author | Xu, Ge | |
| dc.contributor.author | Hapikul, Suchada | |
| dc.date.accessioned | 2022-01-19T17:06:49Z | |
| dc.date.available | 2022-01-19T17:06:49Z | |
| dc.date.issued | 2021-04-28 | |
| dc.description.abstract | In the latest U.S. election of 2020, the voting results confirmed Biden as the 46th president of the United States. After the U.S. presidential election results have been announced, we have seen in some news reports, social media, and other online channels some voting data and public reactions. U.S politics has lately been in the center of the world’s attention with the defeat of controversial 45th US president, Donald Trump and his claims of election fraud in the latest US presidential election. During his government, Trump made extensive usage of social media platforms to share and promote his thoughts, actions and opinions claiming that major media channels failed to portray the truth about his government. As a result, popularizing the term “fake news” as a reference to those channels. We find big data analytic inextricably linked to U.S. elections. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12201 | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.subject | Machine learning | |
| dc.subject | Fake news | |
| dc.subject | Misinformation | |
| dc.title | Machine Learning Techniques for Analysis of Political Campaign | |
| dc.type | Working Paper |