@inproceedings{fernandez-de-landa-etal-2024-uncovering,
    title = "Uncovering Social Changes of the {B}asque Speaking {T}witter Community During {COVID}-19 Pandemic",
    author = "Fernandez de Landa, Joseba  and
      Garc{\'\i}a-Ferrero, Iker  and
      Salaberria, Ander  and
      Campos, Jon Ander",
    editor = "Melero, Maite  and
      Sakti, Sakriani  and
      Soria, Claudia",
    booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.sigul-1.44",
    pages = "363--371",
    abstract = "The aim of this work is to study the impact of the COVID-19 pandemic on the Basque speaking Twitter community by applying Natural Language Processing unsupervised techniques. In order to carry out this study, we collected and publicly released the biggest dataset of Basque tweets containing up to 8M tweets from September 2019 to February 2021. To analyze the impact of the pandemic, the variability of the content over time was studied through quantitative and qualitative analysis of words and emojis. For the quantitative analysis, the shift at the frequency of the terms was calculated using linear regression over frequencies. On the other hand, for the qualitative analysis, word embeddings were used to study the changes in the meaning of the most significant words and emojis at different periods of the pandemic. Through this multifaceted approach, we discovered noteworthy alterations in the political inclinations exhibited by Basque users throughout the course of the pandemic.",
}

The aim of this work is to study the impact of the COVID-19 pandemic on the Basque speaking Twitter community by applying Natural Language Processing unsupervised techniques. In order to carry out this study, we collected and publicly released the biggest dataset of Basque tweets containing up to 8M tweets from September 2019 to February 2021. To analyze the impact of the pandemic, the variability of the content over time was studied through quantitative and qualitative analysis of words and emojis. For the quantitative analysis, the shift at the frequency of the terms was calculated using linear regression over frequencies. On the other hand, for the qualitative analysis, word embeddings were used to study the changes in the meaning of the most significant words and emojis at different periods of the pandemic. Through this multifaceted approach, we discovered noteworthy alterations in the political inclinations exhibited by Basque users throughout the course of the pandemic.
Fern ez de L a, Joseba Garc{\'\i}a-Ferrero, Iker Salaberria, Ander Campos, Jon Ander
Uncovering Social Changes of the Basque Speaking Twitter Community During COVID-19 Pandemic