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SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse

Hafid, Salim; Schellhammer, Sebastian; Bringay, Sandra
GESIS, Cologne. Data File Version 1.0.0, https://doi.org/10.7802/2434
Abstract: This repository contains an expert-annotated dataset of 1261 tweets and the corresponding annotation framework from the publication "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse" (https://arxiv.org/abs/2206.07360). The tweets are annotated with three different categories of science-relatedness: (1) Scientific knowledge (scientifically verifiable claims): Tweets that include a claim or a question that could be scientifically verified, (2) Reference to scientific knowledge: Tweets that include at least one reference to scientific knowledge (reference ... more
Availability: Free access (without registration)
Subject area: [Other Fields of the Applied Social Sciences [=] Science and Engineering, Applied Sciences]
Topics: discourse | false report | twitter | science

Methodology

Date(s) of Data Collection: 2013-01; 2020-12

Bibliographic information

Primärforschende, Institution: Hafid, Salim; LIRMM, CNRS, University of Montpellier, Montpellier, France | Schellhammer, Sebastian; GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany | Bringay, Sandra; LIRMM, CNRS, University of Montpellier, Montpellier, France | Todorov, Konstantin; LIRMM, CNRS, University of Montpellier, Montpellier, France | Dietze, Stefan; G ... more
Publication year: 2022
DOI: 10.7802/2434
Study number: SDN-10.7802-2434
Project funder: This work is supported by the AI4Sci grant, co-funded by MESRI (France, grant UM-211745), BMBF (Germany, grant 01IS21086), and the French National Research Agency (ANR)
Publisher: GESIS, Cologne

Versions

Current Version: 1.0.0, https://doi.org/10.7802/2434
Version history: ... more

Cite

Hafid, Salim, Schellhammer, Sebastian, Bringay, Sandra, Todorov, Konstantin, & Dietze, Stefan (2022). SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse. GESIS, Cologne. Data File Version 1.0.0, https://doi.org/10.7802/2434.

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