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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.
30th International Conference on Artificial Neural Networks (ICANN 2021). Bratislava, Slovakia (14.-17.09.2021)
doi: 10.1007/978-3-030-86383-8_32
Conference or Workshop Item, Bibliographie

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Kaufhold, Marc-André ; Bayer, Markus ; Hartung, Daniel ; Reuter, Christian
Type of entry: Bibliographie
Title: Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter
Language: English
Date: 7 September 2021
Publisher: Springer
Book Title: Artificial Neural Networks and Machine Learning - ICANN 2021
Series: LNCS
Series Volume: 12895
Event Title: 30th International Conference on Artificial Neural Networks (ICANN 2021)
Event Location: Bratislava, Slovakia
Event Dates: 14.-17.09.2021
DOI: 10.1007/978-3-030-86383-8_32
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
Date Deposited: 18 Nov 2021 08:27
Last Modified: 18 Nov 2021 08:27
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