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Inferring Follower Preferences in the 2016 U.S. Presidential Primaries with Sparse Learning

机译:推断在2016年美国总统初级初探的2016年美国总统初级偏好

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In this paper, we propose a framework to infer Twitter follower preferences for the 2016 U.S. presidential primaries. Using Twitter data collected from Sept. 2015 to Mar. 2016, we first uncover the tweeting tactics of the candidates and then exploit the variations in the number of 'likes' to infer followers' preference. With sparse learning, we are able to reveal neutral topics as well as positive and negative ones, Methodologically, we are able to achieve a higher predictive power with sparse learning. Substantively, we show that for Hillary Clinton the (only) positive issue area is women's rights. We demonstrate that Hillary Clinton's tactic of linking herself to President Obama resonates well with her supporters but the same is not true for Bernie Sanders. In addition, we show that Donald Trump is a major topic for all the other candidates, and that the women's rights issue is equally emphasized in Sanders' campaign as in Clinton's. Lessons from the primaries can help inform the general election and beyond. We suggest two ways that politicians can use the feedback mechanism in social media to improve their campaign: (1) use feedback from social media to improve campaign tactics within social media; (2) formulate policies and test the public response from the social media.
机译:在本文中,我们提出了一个框架,以推断推断2016年美国总统初级初级初级的追随者偏好。从2016年9月到2016年3月收集的推特数据,我们首先揭示了候选人的发布策略,然后利用“喜欢”的数量来推断出追随者的偏好。通过稀疏的学习,我们能够揭示中性主题以及正面和消极的方法,我们能够实现更高的预测力,稀疏学习。实质性地,我们表明,对于希拉里克林顿(仅限)的积极问题面积是妇女的权利。我们展示了希拉里克林顿对奥巴马总统将自己联系起来的策略与她的支持者共鸣,但对于伯尼桑德斯而言也不是真的。此外,我们表明,唐纳德特朗普是所有其他候选人的主要话题,妇女的权利在桑顿的竞选中同样强调的是克林顿。初学者的课程可以帮助通知大选及更远。我们建议政治家可以使用社交媒体中的反馈机制来改善他们的运动:(1)使用社交媒体的反馈来改善社交媒体内的竞选战术; (2)制定政策,并从社交媒体上测试公众回应。

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