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ZENODO
Dataset . 2023
Data sources: Datacite; Sygma
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TRACES Bulgarian Twitter Dataset on Covid-19 Annotated with Linguistic Markers of Lies

Authors: Irina Temnikova; Silvia Gargova; Veneta Kireva; Tsvetelina Stefanova;

TRACES Bulgarian Twitter Dataset on Covid-19 Annotated with Linguistic Markers of Lies

Abstract

This dataset has been created within Project TRACES (more information: https://traces.gate-ai.eu/). The dataset contains 61411 tweet IDs of tweets, written in Bulgarian, with annotations. The dataset can be used for general use or for building lies and disinformation detection applications. Note: this dataset is not fact-checked, the social media messages have been retrieved via keywords. For fact-checked datasets, see our other datasets. The tweets (written between 1 Jan 2020 and 28 June 2022) have been collected via Twitter API under academic access in June 2022 with the following keywords: (Covid OR коронавирус OR Covid19 OR Covid-19 OR Covid_19) - without replies and without retweets (Корона OR корона OR Corona OR пандемия OR пандемията OR Spikevax OR SARS-CoV-2 OR бустерна доза) - with replies, but without retweets Explanations of which fields can be used as markers of lies (or of intentional disinformation) are provided in our forthcoming paper (please cite it when using this dataset): Irina Temnikova, Silvia Gargova, Ruslana Margova, Veneta Kireva, Ivo Dzhumerov, Tsvetelina Stefanova and Hristiana Nikolaeva (2023) New Bulgarian Resources for Detecting Disinformation. 10th Language and Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics (LTC'23). Poznań. Poland.

The project TRACES has indirectly received funding from the European Union's Horizon 2020 research and innovation action programme, via the AI4Media Open Call #1 issued and executed under the AI4Media project (Grant Agreement no. 951911).

Subjects by Vocabulary

EOSC: Twitter Data

Keywords

social media, Covid-19, Bulgarian

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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Funded by
EC| AI4Media
Project
AI4Media
A European Excellence Centre for Media, Society and Democracy
  • Funder: European Commission (EC)
  • Project Code: 951911
  • Funding stream: H2020 | RIA
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