Detect me if you can
- Spam Bots have become a threat to online social networks with their malicious behavior, posting misinformation messages and influencing online platforms to fulfill their motives. As spam bots have become more advanced over time, creating algorithms to identify bots remains an open challenge. Learning low-dimensional embeddings for nodes in graph structured data has proven to be useful in various domains. In this paper, we propose a model based on graph convolutional neural networks (GCNN) for spam bot detection. Our hypothesis is that to better detect spam bots, in addition to defining a features set, the social graph must also be taken into consideration. GCNNs are able to leverage both the features of a node and aggregate the features of a node’s neighborhood. We compare our approach, with two methods that work solely on a features set and on the structure of the graph. To our knowledge, this work is the first attempt of using graph convolutional neural networks in spam bot detection.
Author details: | Seyed Ali Alhosseini Almodarresi YasinORCiDGND, Raad Bin TareafORCiD, Pejman NajafiORCiDGND, Christoph MeinelORCiDGND |
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DOI: | https://doi.org/10.1145/3308560.3316504 |
ISBN: | 978-1-4503-6675-5 |
Title of parent work (English): | Companion Proceedings of The 2019 World Wide Web Conference |
Subtitle (English): | Spam Bot Detection Using Inductive Representation Learning |
Publisher: | Association for Computing Machinery |
Place of publishing: | New York |
Publication type: | Other |
Language: | English |
Year of first publication: | 2019 |
Publication year: | 2019 |
Release date: | 2021/05/12 |
Tag: | Bot Detection; Graph Convolutional Neural Networks; Graph Embedding; Social Media Analysis |
Number of pages: | 6 |
First page: | 148 |
Last Page: | 153 |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer review: | Referiert |