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Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter

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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