Predicting tweet impact using a novel evidential reasoning prediction method

FEBDNN: fusion embedding-based deep neural network for user retweeting behavior prediction on social networks

“…Shi et al [ 28 ] proposed a framework that related five major components involved in a social communication: (1) the information source, (2) the stimuli, (3) the information receiver, (4) the relationship between the source and the receiver and (5) the contextual factor, and an analysis on panel dataset indicates that all these components had significant impacts on individual retweeting decision. Rivadeneira et al [ 29 ] presented a novel evidential reasoning (ER) prediction model called MAKER-RIMER to analyze the impact of different features. Jia et al [ 30 ] extracted 19 features to predict by analyzing the relationship between high-retweeted microblog and low-retweeted microblog, the relationship between high-retweeted users and low-retweeted users and the relationship between high-retweeting users and low-retweeting users.…”

Combating the COVID-19 infodemic using Prompt-Based curriculum learning

“…The COVID-19 pandemic has had a significant impact on societies worldwide ( Woźniak, Siłka, & Wieczorek, 2020 ). Amid the massive number of cases of COVID-19, a similarly massive amount of online information about the disease emerged, especially on social media platforms, blogs, and online forums where people discuss and share information and opinions ( Rivadeneira et al, 2021 , Zheng et al, 2021 ). It has been reported that two-thirds of adults in the United States regularly use social media to post their status, opinions, and other information ( Weissenbacher, Sarker, Magge, Daughton, O’Connor, Paul, & Gonzalez-Hernandez, 2019 ).…”

Retweet Prediction Using Multi-Layer Perceptron Optimized by The Swarm Intelligence Algorithm

“…Someone tends to retweet tweets whose content is similar to the tweets they usually upload [9] [8]. The content of the uploaded tweet influences whether a tweet will receive a retweet or not, such as the sentiment of the tweet [4], the arrangement of words used [10] [11], the presence of words that reflect the emotions of the uploader or the presence of emoticons [12] [13]. When a tweet is uploaded will influence whether the tweet will get retweets or not, tweets uploaded at midnight will get fewer retweets than tweets uploaded in the afternoon [4].…”

“…The classification method used to predict whether a tweet will get a retweet or not, among other things, Bayesian Poisson Factorization (BPF) Model [9], Log-Linear Regression [10], Artificial Neural Network [14] [11] , Deep Neural Network [1] [3], Support Vector Machine(SVM) optimized by Cuckoo Search algorithm [5], XGBoost [2][13], Logistic Regression [8][11] [7], MAKER-RIMER Prediction Model [12], SVM [11][7][4], Random Forest [11] [13] [7] [4], Naive Bayes [11] [4], Probabilistic Matrix Factorization Method [6], Decision Tree [7], K-Nearest Neighbors (KNN) [7] .…”