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|Abstract=In this paper, we present an approach for identifying Twitter bots based on their written tweets using a convolutional neural network. We experiment with various embedding methods (pretrained and trained on the training dataset) and convolutional neural network architectures and compare their performance. When evaluating our best performing approach on the actual test data set of theCLEF 2019 Bots Profiling Subtask (English language), we obtain an accuracy of 90.34%. We therefore see convolutional neural networks as a promising machine learning technique for Twitter bot detection.
 
|Abstract=In this paper, we present an approach for identifying Twitter bots based on their written tweets using a convolutional neural network. We experiment with various embedding methods (pretrained and trained on the training dataset) and convolutional neural network architectures and compare their performance. When evaluating our best performing approach on the actual test data set of theCLEF 2019 Bots Profiling Subtask (English language), we obtain an accuracy of 90.34%. We therefore see convolutional neural networks as a promising machine learning technique for Twitter bot detection.
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|Download=Bots_Classification_CLEF2019.pdf
 
|Link=http://ceur-ws.org/Vol-2380/paper_227.pdf
 
|Link=http://ceur-ws.org/Vol-2380/paper_227.pdf
 
|Forschungsgruppe=Web Science
 
|Forschungsgruppe=Web Science
 
}}
 
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Aktuelle Version vom 18. November 2019, 08:15 Uhr


Identifying Twitter Bots Using a Convolutional Neural Network




Published: 2019

Buchtitel: Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum (CLEF'19)
Verlag: CEUR

Referierte Veröffentlichung

BibTeX

Kurzfassung
In this paper, we present an approach for identifying Twitter bots based on their written tweets using a convolutional neural network. We experiment with various embedding methods (pretrained and trained on the training dataset) and convolutional neural network architectures and compare their performance. When evaluating our best performing approach on the actual test data set of theCLEF 2019 Bots Profiling Subtask (English language), we obtain an accuracy of 90.34%. We therefore see convolutional neural networks as a promising machine learning technique for Twitter bot detection.

Download: Media:Bots_Classification_CLEF2019.pdf
Weitere Informationen unter: Link



Forschungsgruppe

Web Science


Forschungsgebiet