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Paper Abstract and Keywords
Presentation 2022-11-24 12:30
A Comprehensive System for Social Robot Detection Using RoBERTa Classifier and Random Forest Regressor with Similarity Analysis
Yeyang Chen, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.) SeMI2022-51
Abstract (in Japanese) (See Japanese page) 
(in English) As one of the most popular social media platforms, Twitter has skyrocketed over the past few years. At the same time, the number of social robots on Twitter has also increased significantly. To avoid being detected, social robots not only disguise themselves as normal users in the profile but also post tweets like real users to manipulate public opinions and affect the normal communication of users. Despite extensive research efforts, bot accounts on Twitter are still evolving to evade detection. Extracting reliable features is considered an effective method, but given the complexity of social robot behaviors, characteristics of the social robots extracted from a single perspective cannot accurately identify different types of robots. In our research, we propose a comprehensive system for social robot detection. The system comprehensively considers the information of different feature groups including profile-level features and tweet-level features. In addition, we propose the similarity of tweets and the predictions of the RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach) classifier as two new feature groups. We used the RoBERTa classifier to analyze the text of the tweets and a random forest regressor to analyze other features. We conduct experiments using the Twibot-20 dataset, and the results show that the accuracy of our system is up to 0.8588, which is higher than all other baseline methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Social Media / Bot Detection / Similarity / Voting Classifier / Random Forest Regressor / RoBERTa / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 278, SeMI2022-51, pp. 12-17, Nov. 2022.
Paper # SeMI2022-51 
Date of Issue 2022-11-17 (SeMI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SeMI2022-51

Conference Information
Committee SRW SeMI CNR  
Conference Date 2022-11-24 - 2022-11-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Epinard Nasu 
Topics (in Japanese) (See Japanese page) 
Topics (in English) IoT Workshop 
Paper Information
Registration To SeMI 
Conference Code 2022-11-SRW-SeMI-CNR 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Comprehensive System for Social Robot Detection Using RoBERTa Classifier and Random Forest Regressor with Similarity Analysis 
Sub Title (in English)  
Keyword(1) Social Media  
Keyword(2) Bot Detection  
Keyword(3) Similarity  
Keyword(4) Voting Classifier  
Keyword(5) Random Forest Regressor  
Keyword(6) RoBERTa  
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Keyword(8)  
1st Author's Name Yeyang Chen  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Mondher Bouazizi  
2nd Author's Affiliation Keio University (Keio Univ.)
3rd Author's Name Tomoaki Ohtsuki  
3rd Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2022-11-24 12:30:00 
Presentation Time 20 minutes 
Registration for SeMI 
Paper # SeMI2022-51 
Volume (vol) vol.122 
Number (no) no.278 
Page pp.12-17 
#Pages 6 
Date of Issue 2022-11-17 (SeMI) 

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