Predicting tweet impact using a novel evidential reasoning prediction methodCitation formats

Standard

Predicting tweet impact using a novel evidential reasoning prediction method. / Rivadeneira, Lucía; Yang, Jian-bo; López-ibáñez, Manuel.

In: Expert Systems with Applications, Vol. 169, 01.05.2021, p. 114400.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Rivadeneira, Lucía ; Yang, Jian-bo ; López-ibáñez, Manuel. / Predicting tweet impact using a novel evidential reasoning prediction method. In: Expert Systems with Applications. 2021 ; Vol. 169. pp. 114400.

Bibtex

@article{e17b0b8e2f6b4d7a8343d93674d54404,
title = "Predicting tweet impact using a novel evidential reasoning prediction method",
abstract = "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 the two most voted candidates during the official campaign for the 2017 Ecuadorian Presidential election were used for this research. For each tweet, five features including type of tweet, emotion, URL, hashtag, and date are identified and coded to predict if tweets are of either high or low impact. The main contributions of the new proposed model include its suitability to analyse tweet datasets based on likelihood analysis of data. The model is interpretable, and the prediction process relies only on the use of available data. The experimental results show that MAKER-RIMER performed better, in terms of misclassification error, when compared against other predictive machine learning approaches. In addition, the model allows observing which features of the candidates{\textquoteright} tweets are linked to high and low impact. Tweets containing allusions to the contender candidate, either with positive or negative connotations, without hashtags, and written towards the end of the campaign, were persistently those with the highest impact. URLs, on the other hand, is the only variable that performs differently for the two candidates in terms of achieving high impact. MAKER-RIMER can provide campaigners of political parties or candidates with a tool to measure how features of tweets are predictors of their impact, which can be useful to tailor Twitter content during electoral campaigns.",
author = "Luc{\'i}a Rivadeneira and Jian-bo Yang and Manuel L{\'o}pez-ib{\'a}{\~n}ez",
year = "2021",
month = may,
day = "1",
doi = "10.1016/j.eswa.2020.114400",
language = "English",
volume = "169",
pages = "114400",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Predicting tweet impact using a novel evidential reasoning prediction method

AU - Rivadeneira, Lucía

AU - Yang, Jian-bo

AU - López-ibáñez, Manuel

PY - 2021/5/1

Y1 - 2021/5/1

N2 - 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 the two most voted candidates during the official campaign for the 2017 Ecuadorian Presidential election were used for this research. For each tweet, five features including type of tweet, emotion, URL, hashtag, and date are identified and coded to predict if tweets are of either high or low impact. The main contributions of the new proposed model include its suitability to analyse tweet datasets based on likelihood analysis of data. The model is interpretable, and the prediction process relies only on the use of available data. The experimental results show that MAKER-RIMER performed better, in terms of misclassification error, when compared against other predictive machine learning approaches. In addition, the model allows observing which features of the candidates’ tweets are linked to high and low impact. Tweets containing allusions to the contender candidate, either with positive or negative connotations, without hashtags, and written towards the end of the campaign, were persistently those with the highest impact. URLs, on the other hand, is the only variable that performs differently for the two candidates in terms of achieving high impact. MAKER-RIMER can provide campaigners of political parties or candidates with a tool to measure how features of tweets are predictors of their impact, which can be useful to tailor Twitter content during electoral campaigns.

AB - 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 the two most voted candidates during the official campaign for the 2017 Ecuadorian Presidential election were used for this research. For each tweet, five features including type of tweet, emotion, URL, hashtag, and date are identified and coded to predict if tweets are of either high or low impact. The main contributions of the new proposed model include its suitability to analyse tweet datasets based on likelihood analysis of data. The model is interpretable, and the prediction process relies only on the use of available data. The experimental results show that MAKER-RIMER performed better, in terms of misclassification error, when compared against other predictive machine learning approaches. In addition, the model allows observing which features of the candidates’ tweets are linked to high and low impact. Tweets containing allusions to the contender candidate, either with positive or negative connotations, without hashtags, and written towards the end of the campaign, were persistently those with the highest impact. URLs, on the other hand, is the only variable that performs differently for the two candidates in terms of achieving high impact. MAKER-RIMER can provide campaigners of political parties or candidates with a tool to measure how features of tweets are predictors of their impact, which can be useful to tailor Twitter content during electoral campaigns.

U2 - 10.1016/j.eswa.2020.114400

DO - 10.1016/j.eswa.2020.114400

M3 - Article

VL - 169

SP - 114400

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -