This repository hosts the appendix used in the paper Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter [1].
1: Marc-André Kaufhold, Markus Bayer, Daniel Hartung, Christian Reuter (2021) Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter, 30th International Conference on Artificial Neural Networks (ICANN). https://doi.org/10.1007/978-3-030-86383-8_32
@inproceedings{KaufholdBayerHartungReuter2021,
address = {Bratislava},
title = {Design and {Evaluation} of {Deep} {Learning} {Models} for {Real}-{Time} {Credibility} {Assessment} in {Twitter}},
url = {https://link.springer.com/chapter/10.1007%2F978-3-030-86383-8_32},
doi = {https://doi.org/10.1007/978-3-030-86383-8_32},
booktitle = {30th {International} {Conference} on {Artificial} {Neural} {Networks} ({ICANN})},
author = {Kaufhold, Marc-André and Bayer, Markus and Hartung, Daniel and Reuter, Christian},
year = {2021}
}
- Marc-André Kaufhold
- Markus Bayer
- Daniel Hartung
- Christian Reuter
Copyright (c) 2021 Marc-André Kaufhold, Technical University Darmstadt
Permission to use, copy, modify, and distribute the appendix for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies.