论文标题
在预测选举结果时，对Twitter的双层评估：德里议会选举2020
A Bi-level assessment of Twitter in predicting the results of an election: Delhi Assembly Elections 2020
论文作者
论文摘要
选举是任何民主国家的骨干，选民选举候选人作为代表。社交网站的出现为政党及其候选人提供了与选民建立联系以传播其政治思想的平台。我们的研究旨在使用Twitter评估2020年德里议会选举的结果，以双层方法，即与政党及其候选人有关。我们分析了选举结果与Twitter上不同候选人和政党的活动的相关性，以及选民对他们的反应，尤其是选民对政党的提及和情感。比较候选人的Twitter概况在党派和候选人水平上，以评估他们与选举结果的联系。我们观察到，追随者的数量和候选推文的答复是预测实际选举结果的好指标。但是，我们观察到，提到一方的推文数量以及对推文中所示党派的选民的观点与选举结果不符。我们还使用机器学习模型在各种功能上，例如语言，单词嵌入和道德维度来预测选举结果（胜利或输）。使用Tweet功能的随机森林模型为预测该推文是否属于胜利或失败的候选人提供了有希望的结果。
Elections are the backbone of any democratic country, where voters elect the candidates as their representatives. The emergence of social networking sites has provided a platform for political parties and their candidates to connect with voters in order to spread their political ideas. Our study aims to use Twitter in assessing the outcome of Delhi Assembly elections held in 2020, using a bi-level approach, i.e., concerning political parties and their candidates. We analyze the correlation of election results with the activities of different candidates and parties on Twitter, and the response of voters on them, especially the mentions and sentiment of voters towards a party. The Twitter profiles of the candidates are compared both at the party level as well as the candidate level to evaluate their association with the outcome of the election. We observe that the number of followers and the replies to the tweets of candidates are good indicators for predicting actual election outcome. However, we observe that the number of tweets mentioning a party and the sentiment of voters towards the party shown in tweets are not aligned with the election result. We also use machine learning models on various features such as linguistic, word embeddings and moral dimensions for predicting the election result (win or lose). The random forest model using tweet features provides promising results for predicting if the tweet belongs to a winning or losing candidate.
