Prediction of COVID-19 related information spreading on Twitter (CROSBI ID 708044)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Babić, Karlo ; Petrović, Milan ; Beliga, Slobodan ; Martinčić-Ipšić, Sanda ; Pranjić, Marko ; Meštrović, Ana
engleski
Prediction of COVID-19 related information spreading on Twitter
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 experiments, we separate the original dataset into two disjoint subsets: one composed of tweets filtered using COVID- 19 related keywords and the other that contains the rest of the tweets. Additionally, we modified these two subsets by adding and removing tokens into tweets and thus making them artificially COVID-19 related or not related. Our preliminary results indicate that tweets that are semantically related to COVID-19 have on average higher spreadability than the tweets that are not semantically related to COVID-19.
information spreading ; neural networks ; NLP ; Twitter ; COVID-19
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Podaci o prilogu
395-399.
2021.
objavljeno
10.23919/MIPRO52101.2021.9596693
Podaci o matičnoj publikaciji
44th International convention on Information, Communication and Electronic Technology (MIPRO) – proceedings
Skala, Karolj
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO
2623-8764
Podaci o skupu
MIPRO 2021
predavanje
27.09.2021-01.10.2021
Opatija, Hrvatska