Naslov
Topic Modeling for Tracking COVID-19 Communication on Twitter

Autori
Bogović, Petar Kristijan ; Meštrović, Ana ; Martinčić-Ipšić, Sanda

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Information and Software Technologies. ICIST 2022. Communications in Computer and Information Science / Lopata, A. ; Gudonienė, D. ; Butkienė, R. - Cham : Springer, 2022, 248-258

ISBN
978-3-031-16301-2

Skup
Information and Software Technologies (ICIST 2022)

Mjesto i datum
Kaunas, Litva, 12.10.2022. - 14.10.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Topic modeling ; Latent Dirichlet Allocation ; Coherence score ; Croatian tweets ; COVID-19 infodemic

Sažetak
In this study, we analyze the trends of COVID-19 related communication in Croatian language on Twitter. First, we prepare a dataset of 147, 028 tweets about COVID-19 posted during the first three waves of the pandemic, and then perform an analysis in three steps. In the first step, we train the LDA model and calculate the coherence values of the topics. We identify seven topics and report the ten most frequent words for each topic. In the second step, we analyze the proportion of tweets in each topic and report how these trends change over time. In the third step, we study spreading properties for each topic. The results show that all seven topics are evenly distributed across the three pandemic waves. The topic “vaccination” stands out with the change in percentage from 14.6% tweets in the first wave to 25.7% in the third wave. The obtained results contribute to a better understanding of pandemic communication in social media in Croatia.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti

POVEZANOST RADA