Summary information

Study title

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

Creator

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 ( GESIS - Leibniz Institute for the Social Sciences Cologne & Heinrich-Heine-University Düsseldorf, Germany)

Study number / PID

10.7802/2434 (GESIS)

10.7802/2434 (DOI)

Data access

Information not available

Series

Not available

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 (references can either be direct, e.g., DOI, title of a paper or indirect, e.g., a link to an article that includes a direct reference), and (3) Related to scientific research in general: Tweets that mention a scientific research context (e.g., mention a scientist, scientific research efforts, research findings).
Further, the annotations include the annotators' confidence scores as well as labels for compound claims and ironic tweets.

Topics

Not available

Methodology

Data collection period

01/01/2013 - 01/12/2020

Country

Time dimension

Not available

Analysis unit

Not available

Universe

Sampling procedure

Not available

Kind of data

Not available

Data collection mode

Not available

Access

Publisher

GESIS Data Archive for the Social Sciences

Publication year

2022

Terms of data access

Free access (without registration) - The research data can be downloaded directly by anyone without further limitations.

Related publications

Not available