Byte-Pair and N-Gram Convolutional Methods of Analysing Automatically Disseminated Content on Social Platforms

Houjun Liu

doi:10.20944/preprints202004.0214.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 April 2020 / Approved: 13 April 2020 / Online: 13 April 2020 (13:08:26 CEST)

In this experiment, an efficient and accurate network of detecting automatically disseminated (bot) content on social platforms is devised. Through the utilisation of parallel convolutional neural network (CNN) which processes variable n-grams of text 15, 20, and 25 tokens in length encoded by Byte Pair Encoding (BPE), the complexities of linguistic content on social platforms are effectively captured and analysed. With validation on two sets of previously unexposed data, the model was able to achieve an accuracy of around 96.6% and 97.4% respectively — meeting or exceeding the performance of other comparable supervised ML solutions to this problem. Through testing, it is concluded that this method of text processing and analysis proves to be an effective way of classifying potentially artificially synthesized user data — aiding the security and integrity of social platforms.

bot detection; machine learning; natural language processing; computation linguistics

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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Byte-Pair and N-Gram Convolutional Methods of Analysing Automatically Disseminated Content on Social Platforms

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