280个字符到白宫:根据推特数据预测2020年美国总统大选
Rodrigue Rizk1, Dominick Rizk2, Frederic Rizk2
1Department of Computer Science, University of South Dakota, Vermillion, SD 57069 USA.
Computational and mathematical organization theory
|June 26, 2023
概括
这项研究使用Twitter数据预测了2020年美国总统大选的结果. 拟议的模型准确地预测了乔·拜登.
科学领域:
- 计算社会科学 计算社会科学
- 政治科学 政治科学是指政治学.
- 数据科学数据科学数据科学
背景情况:
- 2020年美国大选的意义需要准确的预测模型.
- 社交媒体,特别是推特,是公共政治话语的关键平台.
- 以前的模型一直在努力准确模拟美国总统大选的动态.
研究的目的:
- 开发一个有效的模型来预测2020年美国总统大选的结果.
- 利用地理位置的Twitter数据分析公众对候选人的情绪.
- 通过将预测与实际选举结果进行比较来验证模型的准确性.
主要方法:
- 利用情绪分析,多项天真贝叶斯分类器和机器学习在地理位置的推特上.
- 进行了广泛的州对州选举投票分析和整体大众投票预测.
- 实施异常值和机器人/代理检测以确保数据完整性;使用网络分析和社区检测.
主要成果:
- 拟议的模型准确地预测了基于州的选举人投票和全国的民意投票.
- 一个由算法定义的立场计决策规则将乔·拜登确定为当选总统.
- 该模型在预测乔·拜登在选举团中的胜利方面达到89.9%的准确性.
结论:
- 开发的模型通过分析Twitter上的公众立场,有效地预测了美国总统选举的结果.
- 情绪分析与机器学习相结合,为选举预测提供了一个强大的方法.
- 该研究强调了社交媒体数据的价值,当正确处理时,可以理解政治结果.
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