RTPMF: Leveraging User and Message Embeddings for Retweeting Behavior Prediction

Liang, Jinsong; Jiang, Bo; Yin, Rongchao; Wang, Chonghua; Tan, Jun; Bai, Shuo

doi:10.1016/j.procs.2016.05.351

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Several studies have contributed to this field by proposing methods for predicting the results of important events, such as games, and political elections, using data on the volume of retweet ( Abdullah et al, 2015 ; Liang et al, 2016 ). Some studies explored the reasons why users retweet certain information without applying machine learning techniques for prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic

Mahdikhani

2022

International Journal of Information Management Data Insights

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In this study, public opinion and emotions regarding different stages of the Covid-19 pandemic from the outbreak of the disease to the distribution of vaccines were analyzed to predict the popularity of tweets. More than 1.25 million English tweets were collected, posted from January 20, 2020, to May 29, 2021. Five sets of content features, including topic analysis, topics plus TF-IDF vectorizer, bag of words (BOW) by TF-IDF vectorizer, document embedding, and document embedding plus TF-IDF vectorizer, were extracted and applied to supervised machine learning algorithms to generate a predictive model for the retweetability of posted tweets. The analysis showed that tweets with higher emotional intensity are more popular than tweets containing information on Covid-19 pandemic. This study can help to detect the public emotions during the pandemic and after vaccination and predict the retweetability of posted tweets in different stages of Covid-19 pandemic.

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Section: Literature Reviewmentioning

confidence: 99%