Twitter social bots: The 2019 Spanish general election data.

Autor:	Pastor-Galindo J; Department of Information Engineering and Communications, University of Murcia, Murcia, Spain., Zago M; Department of Information Engineering and Communications, University of Murcia, Murcia, Spain., Nespoli P; Department of Information Engineering and Communications, University of Murcia, Murcia, Spain., López Bernal S; Department of Information Engineering and Communications, University of Murcia, Murcia, Spain., Huertas Celdrán A; Telecommunication Software & Systems Group, Waterford Institute of Technology, Cork Rd, Waterford, Ireland., Gil Pérez M; Department of Information Engineering and Communications, University of Murcia, Murcia, Spain., Ruipérez-Valiente JA; Department of Information Engineering and Communications, University of Murcia, Murcia, Spain., Martínez Pérez G; Department of Information Engineering and Communications, University of Murcia, Murcia, Spain., Gómez Mármol F; Department of Information Engineering and Communications, University of Murcia, Murcia, Spain.
Jazyk:	angličtina
Zdroj:	Data in brief [Data Brief] 2020 Jul 21; Vol. 32, pp. 106047. Date of Electronic Publication: 2020 Jul 21 (Print Publication: 2020).
DOI:	10.1016/j.dib.2020.106047
Abstrakt:	The term social bots refer to software-controlled accounts that actively participate in the social platforms to influence public opinion toward desired directions. To this extent, this data descriptor presents a Twitter dataset collected from October 4th to November 11th, 2019, within the context of the Spanish general election. Starting from 46 hashtags, the collection contains almost eight hundred thousand users involved in political discussions, with a total of 5.8 million tweets. The proposed data descriptor is related to the research article available at [1]. Its main objectives are: i) to enable worldwide researchers to improve the data gathering, organization, and preprocessing phases; ii) to test machine-learning-powered proposals; and, finally, iii) to improve state-of-the-art solutions on social bots detection, analysis, and classification. Note that the data are anonymized to preserve the privacy of the users. Throughout our analysis, we enriched the collected data with meaningful features in addition to the ones provided by Twitter. In particular, the tweets collection presents the tweets' topic mentions and keywords (in the form of political bag-of-words), and the sentiment score. The users' collection includes one field indicating the likelihood of one account being a bot. Furthermore, for those accounts classified as bots, it also includes a score that indicates the affinity to a political party and the followers/followings list. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article. (© 2020 The Authors.)
Databáze:	MEDLINE
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