Abstract
Twitter is a social platform that helps share ideas quickly and concisely. Although the network offers equal rights to post short texts, the attention these messages attract frequently depends on a user’s status in the real world. Thus the tweets of real life high-profile opinion makers will a priori have a higher probability of spurring the interest of society than the messages from the so-called grassroots. The paper elaborates on the developed classifier that detects automatically such opinion makers on Twitter. The approach exploits the Mixed Effect Random Forests method combined with the features engineered from the Twitter data. The accuracy and the sensitivity of the proposed technique outperform the results of the other machine learning classifiers on the out-of-sample data.
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Galeshchuk, S., Qiu, J. (2021). Identification of Opinion Makers on Twitter. In: Mariani, P., Zenga, M. (eds) Data Science and Social Research II. DSSR 2019. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-51222-4_15
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DOI: https://doi.org/10.1007/978-3-030-51222-4_15
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