Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Multimedia Benchmark Workshop 2020 |
| Untertitel | Working Notes Proceedings of the MediaEval 2020 Workshop |
| Publikationsstatus | Veröffentlicht - 2020 |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR WS |
| Band | 2882 |
| ISSN (Print) | 1613-0073 |
Abstract
Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifically, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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- RIS
Multimedia Benchmark Workshop 2020: Working Notes Proceedings of the MediaEval 2020 Workshop. 2020. (CEUR Workshop Proceedings; Band 2882).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung
}
TY - GEN
T1 - TIB's visual analytics group at MediaEval '20
T2 - Detecting fake news on corona virus and 5G conspiracy
AU - Cheema, Gullal S.
AU - Hakimov, Sherzod
AU - Ewerth, Ralph
N1 - This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no 812997 (CLEOPATRA ITN).
PY - 2020
Y1 - 2020
N2 - Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifically, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection.
AB - Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifically, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection.
UR - http://www.scopus.com/inward/record.url?scp=85103209858&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2101.03529
DO - 10.48550/arXiv.2101.03529
M3 - Conference contribution
AN - SCOPUS:85103209858
T3 - CEUR Workshop Proceedings
BT - Multimedia Benchmark Workshop 2020
ER -