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2021 • Prediction of COVID-19 Related Information Spreading on Twitter

Authors: Karlo Babić, Milan Petrović, Slobodan Beliga, Sanda Martinčić-Ipšić, Marko Pranjic, Ana Meštrović

Abstract: In this paper, we explore the influence of COVID-19 related content in tweets on their spreadability. The experiment is performed in two steps on the dataset of tweets in the Croatian language posted during the COVID-19 pandemics. In the first step, we train a feedforward neural network model to predict if a tweet is highly-spreadable or not. The trained model achieves 62.5\% accuracy on the binary classification problem. In the second step, we use this model in a set of experiments for predicting the average spreadability of tweets. In these e (...) (Read More)

External links: OpenAIRE

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Prediction of COVID-19 Related Information Spreading on Twitter

Karlo Babic, Milan Petrovic, Slobodan Beliga, Sanda Martincic-Ipsic, Marko Pranjic, Ana Mestrovic

2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO) · 2021

Artificial intelligence | Natural language processing |

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