Kaufhold, Marc-André ; Bayer, Markus ; Hartung, Daniel ; Reuter, Christian (2021):
Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter.
In: LNCS, 12895, In: Artificial Neural Networks and Machine Learning - ICANN 2021, pp. 396-408,
Springer, 30th International Conference on Artificial Neural Networks (ICANN 2021), Bratislava, Slovakia, 14.-17.09.2021, ISBN 978-3-030-86382-1,
DOI: 10.1007/978-3-030-86383-8_32,
[Conference or Workshop Item]
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2021 |
| Creators: | Kaufhold, Marc-André ; Bayer, Markus ; Hartung, Daniel ; Reuter, Christian |
| Title: | Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter |
| Language: | English |
| Book Title: | Artificial Neural Networks and Machine Learning - ICANN 2021 |
| Series: | LNCS |
| Series Volume: | 12895 |
| Publisher: | Springer |
| ISBN: | 978-3-030-86382-1 |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Science and Technology for Peace and Security (PEASEC) Forschungsfelder Forschungsfelder > Information and Intelligence Forschungsfelder > Information and Intelligence > Cybersecurity & Privacy |
| Event Title: | 30th International Conference on Artificial Neural Networks (ICANN 2021) |
| Event Location: | Bratislava, Slovakia |
| Event Dates: | 14.-17.09.2021 |
| Date Deposited: | 18 Nov 2021 08:27 |
| DOI: | 10.1007/978-3-030-86383-8_32 |
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