Predicting tweet impact using a novel evidential reasoning prediction method
نویسندگان
چکیده
This study presents a novel evidential reasoning (ER) prediction model called MAKER-RIMER to examine how different features embedded in Twitter posts (tweets) can predict the number of retweets achieved during an electoral campaign. The tweets posted by two most voted candidates official campaign for 2017 Ecuadorian Presidential election were used this research. For each tweet, five including type emotion, URL, hashtag, and date are identified coded if either high or low impact. main contributions new proposed include its suitability analyse tweet datasets based on likelihood analysis data. is interpretable, process relies only use available experimental results show that performed better, terms misclassification error, when compared against other predictive machine learning approaches. In addition, allows observing which candidates’ linked Tweets containing allusions contender candidate, with positive negative connotations, without hashtags, written towards end campaign, persistently those highest URLs, hand, variable performs differently achieving provide campaigners political parties tool measure predictors their impact, be useful tailor content campaigns.
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ژورنال
عنوان ژورنال: Expert Systems With Applications
سال: 2021
ISSN: ['1873-6793', '0957-4174']
DOI: https://doi.org/10.1016/j.eswa.2020.114400