2026-04-30T08:24:11Z https://datacatalogue.cessda.eu/oai-pmh/v0/oai

376855bcc8e37f46cd7b06efa2220f4572ed2cf04f81b5c2bce5a6519d9db1c6 2025-06-17T03:16:08Z language:enopenaire_data

DDI study level documentation for study 10.7802/2434 SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse 10.7802/243410.7802/243410.7802/243410.7802/2434 Hafid, Salim Hafid, Salim Schellhammer, Sebastian Schellhammer, Sebastian Bringay, Sandra Bringay, Sandra Todorov, Konstantin Todorov, Konstantin Dietze, Stefan Dietze, Stefan [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)] GESIS Data Archive for the Social SciencesGESIS Datenarchiv für Sozialwissenschaften discoursefalse reporttwittersciencediscoursefalse reporttwitterscience 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: <br> (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). <br> Further, the annotations include the annotators' confidence scores as well as labels for compound claims and ironic tweets. <br>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: <br> (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). <br> Further, the annotations include the annotators' confidence scores as well as labels for compound claims and ironic tweets. <br> Free access (without registration) - The research data can be downloaded directly by anyone without further limitations.Freier Zugang (ohne Registrierung) - Die Forschungsdaten können von jedem direkt heruntergeladen werden. http://dbkapps.gesis.org/dbkoai/oai.asp oai:dbk.gesis.org:SDN/10.7802_2434 2023-03-11 ddi:codebook:2_5