Methods and Challenges in Social Bots Detection: A Systematic Review

  • Daniel Marques de Morais USP
  • Luciano Antonio Digiampietri USP

Resumo


Social bots are automated users who make use of social networks to produce content and interact with network users, in order to mimic or attempt to alter user behaviors, with the purposes, among others, of spreading spam and malicious content, violate users privacy or mislead information in order to influence financial markets or electoral disputes, causing numerous losses. Detecting these bots is a major challenge since, as detection mechanisms evolve, its hiding properties are also enhanced to avoid such mechanisms, either by more sophisticated strategies for emulating real users or by organizing groups of bots in sophisticated networks with the same purpose (botnets). This paper presents a survey about social bot detection approaches, considering the techniques used, the set of characteristics considered for the classification as well as the target of identification (individual or botnets). The main open points identified as well as possible advances in research in the area are also discussed.
Palavras-chave: Bots detection, Social networks, Social bots

Referências

Seyed Ali Alhosseini, Raad Bin Tareaf, Pejman Najafi, and Christoph Meinel. 2019. Detect Me If You Can: Spam Bot Detection Using Inductive Representation Learning. In Companion Proceedings of The 2019 World Wide Web Conference(WWW ’19). Association for Computing Machinery, New York, NY, USA, 148–153. https://doi.org/10.1145/3308560.3316504

Naif Radi Aljohani, Ayman Fayoumi, and Saeed-Ul Hassan. 2020. Bot prediction on social networks of Twitter in altmetrics using deep graph convolutional networks. Soft Computing 24, 15 (Aug. 2020), 11109–11120. https://doi.org/10.1007/s00500-020-04689-y

M. Alsaleh, A. Alarifi, A. M. Al-Salman, M. Alfayez, and A. Almuhaysin. 2014. TSD: Detecting Sybil Accounts in Twitter. In 2014 13th International Conference on Machine Learning and Applications. IEEE, New York, New Yourk, USA, 463–469. https://doi.org/10.1109/ICMLA.2014.81

Hamidreza Alvari, Elham Shaabani, Soumajyoti Sarkar, Ghazaleh Beigi, and Paulo Shakarian. 2019. Less is More: Semi-Supervised Causal Inference for Detecting Pathogenic Users in Social Media. In Companion Proceedings of The 2019 World Wide Web Conference(WWW ’19). ACM, New York, NY, USA, 154–161. https://doi.org/10.1145/3308560.3316500

P. Andriotis and A. Takasu. 2018. Emotional Bots: Content-based Spammer Detection on Social Media. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, New York, New Yourk, USA, 1–8. https://doi.org/10.1109/WIFS.2018.8630760

Alessandro Balestrucci, Rocco De Nicola, Omar Inverso, and Catia Trubiani. 2019. Identification of Credulous Users on Twitter. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing(SAC ’19). Association for Computing Machinery, New York, NY, USA, 2096–2103. https://doi.org/10.1145/3297280.3297486

David M. Beskow and Kathleen M. Carley. 2019. Its all in a name: detecting and labeling bots by their name. Computational and Mathematical Organization Theory 25, 1 (March 2019), 24–35. https://doi.org/10.1007/s10588-018-09290-1

B. Boreggah, A. Alrazooq, M. Al-Razgan, and H. AlShabib. 2018. Analysis of Arabic Bot Behaviors. In 2018 21st Saudi Computer Society National Computer Conference (NCC). IEEE, New York, New Yourk, USA, 1–6. https://doi.org/10.1109/NCG.2018.8592980

C. Cai, L. Li, and D. Zengi. 2017. Behavior enhanced deep bot detection in social media. In 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, New York, New Yourk, USA, 128–130. https://doi.org/10.1109/ISI.2017.8004887

Samara Castillo, Héctor Allende-Cid, Wenceslao Palma, Rodrigo Alfaro, Heitor S. Ramos, Cristian Gonzalez, Claudio Elortegui, and Pedro Santander. 2019. Detection of Bots and Cyborgs in Twitter: A Study on the Chilean Presidential Election in 2017. In Social Computing and Social Media. Design, Human Behavior and Analytics, Gabriele Meiselwitz (Ed.). Springer International Publishing, Cham, 311–323.

N. Chavoshi, H. Hamooni, and A. Mueen. 2016. DeBot: Twitter Bot Detection via Warped Correlation. In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, New York, New Yourk, USA, 817–822. https://doi.org/10.1109/ICDM.2016.0096

Aditya Chetan, Brihi Joshi, Hridoy Sankar Dutta, and Tanmoy Chakraborty. 2019. CoReRank: Ranking to Detect Users Involved in Blackmarket-Based Collusive Retweeting Activities. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining(WSDM ’19). Association for Computing Machinery, New York, NY, USA, 330–338. https://doi.org/10.1145/3289600.3291010

Zi Chu, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. 2010. Who is Tweeting on Twitter: Human, Bot, or Cyborg?. In Proceedings of the 26th Annual Computer Security Applications Conference(ACSAC ’10). Association for Computing Machinery, New York, NY, USA, 21–30. https://doi.org/10.1145/1920261.1920265

Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2015. Fame for sale: Efficient detection of fake Twitter followers. Decision Support Systems 80 (Dec. 2015), 56–71. https://doi.org/10.1016/j.dss.2015.09.003

S. Cresci, R. D. Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi. 2018. Social Fingerprinting: Detection of Spambot Groups Through DNA-Inspired Behavioral Modeling. IEEE Transactions on Dependable and Secure Computing 15, 4 (July 2018), 561–576. https://doi.org/10.1109/TDSC.2017.2681672

C. A. S. d. Freitas, F. Benevenuto, and A. Veloso. 2014. Socialbots: Implications on the Safety and Reliability of Twitter-Based Services. In 2014 Brazilian Symposium on Computer Networks and Distributed Systems. IEEE, New York, New Yourk, USA, 302–309. https://doi.org/10.1109/SBRC.2014.36

Kheir Eddine Daouadi, Rim Zghal Rebaï, and Ikram Amous. 2019. Bot Detection on Online Social Networks Using Deep Forest. In Artificial Intelligence Methods in Intelligent Algorithms, Radek Silhavy (Ed.). Springer International Publishing, Cham, 307–315.

J. P. Dickerson, V. Kagan, and V. S. Subrahmanian. 2014. Using sentiment to detect bots on Twitter: Are humans more opinionated than bots?. In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014). IEEE, New York, New Yourk, USA, 620–627. https://doi.org/10.1109/ASONAM.2014.6921650

A. Dorri, M. Abadi, and M. Dadfarnia. 2018. SocialBotHunter: Botnet Detection in Twitter-Like Social Networking Services Using Semi-Supervised Collective Classification. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). IEEE, New York, New Yourk, USA, 496–503. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00097

Rodrigo Igawa e Alex Almeida e Bruno Zarpelão e Sylvio Barbon Jr. 2016. Recognition on Online Social Network by user’s writing style. iSys - Revista Brasileira de Sistemas de Informação 8, 3(2016), 64–85.

Juan Echeverria and Shi Zhou. 2017. Discovery, Retrieval, and Analysis of the “Star Wars” Botnet in Twitter. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017(ASONAM ’17). Association for Computing Machinery, New York, NY, USA, 1–8. https://doi.org/10.1145/3110025.3110074

Y. Feng, J. Li, L. Jiao, and X. Wu. 2019. BotFlowMon: Learning-based, Content-Agnostic Identification of Social Bot Traffic Flows. In 2019 IEEE Conference on Communications and Network Security (CNS). IEEE, New York, New Yourk, USA, 169–177. https://doi.org/10.1109/CNS.2019.8802706

J. Fernquist, L. Kaati, and R. Schroeder. 2018. Political Bots and the Swedish General Election. In 2018 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, New York, New Yourk, USA, 124–129. https://doi.org/10.1109/ISI.2018.8587347

Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2016. The Rise of Social Bots. Commun. ACM 59, 7 (June 2016), 96–104. https://doi.org/10.1145/2818717

Eric Ferreira Dos Santos, Danilo Carvalho, Livia Ruback, and Jonice Oliveira. 2019. Uncovering Social Media Bots: A Transparency-Focused Approach. In Companion Proceedings of The 2019 World Wide Web Conference(WWW ’19). Association for Computing Machinery, New York, NY, USA, 545–552. https://doi.org/10.1145/3308560.3317599

Zafar Gilani, Ekaterina Kochmar, and Jon Crowcroft. 2017. Classification of Twitter Accounts into Automated Agents and Human Users. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017(ASONAM ’17). Association for Computing Machinery, New York, NY, USA, 489–496. https://doi.org/10.1145/3110025.3110091

Rodrigo Igawa, Alex Almeida, Bruno Zarpelão, and Sylvio Jr. 2015. Recognition of Compromised Accounts on Twitter. In Anais do XI Simpósio Brasileiro de Sistemas de Informação. SBC, Porto Alegre, Brazil, 9–14. 

M. Kantepe and M. C. Ganiz. 2017. Preprocessing framework for Twitter bot detection. In 2017 International Conference on Computer Science and Engineering (UBMK). IEEE, New York, New Yourk, USA, 630–634. https://doi.org/10.1109/UBMK.2017.8093483

Jyoti Kaubiyal and Ankit Kumar Jain. 2019. A Feature Based Approach to Detect Fake Profiles in Twitter. In Proceedings of the 3rd International Conference on Big Data and Internet of Things(BDIOT 2019). Association for Computing Machinery, New York, NY, USA, 135–139. https://doi.org/10.1145/3361758.3361784

Ansgar Kellner, Christian Wressnegger, and Konrad Rieck. 2020. What’s All That Noise: Analysis and Detection of Propaganda on Twitter. In Proceedings of the 13th European Workshop on Systems Security(EuroSec ’20). Association for Computing Machinery, New York, NY, USA, 25–30. https://doi.org/10.1145/3380786.3391399

S. Khaled, N. El-Tazi, and H. M. O. Mokhtar. 2018. Detecting Fake Accounts on Social Media. In 2018 IEEE International Conference on Big Data (Big Data). IEEE, New York, New Yourk, USA, 3672–3681.

Barbara Kitchenham. 2004. Procedures for Performing Systematic Reviews. Keele, UK, Keele Univ. 33 (08 2004).

Sneha Kudugunta and Emilio Ferrara. 2018. Deep neural networks for bot detection. Information Sciences 467 (Oct. 2018), 312–322. https://doi.org/10.1016/j.ins.2018.08.019

G. Lingam, R. Ranjan Rout, and D. V. L. N. Somayajulu. 2019. Deep Q-Learning and Particle Swarm Optimization for Bot Detection in Online Social Networks. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, New York, New Yourk, USA, 1–6. https://doi.org/10.1109/ICCCNT45670.2019.8944493

G. Lingam, R. R. Rout, and D. Somayajulu. 2018. Detection of Social Botnet using a Trust Model based on Spam Content in Twitter Network. In 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS). IEEE, New York, New Yourk, USA, 280–285. https://doi.org/10.1109/ICIINFS.2018.8721318

Greeshma Lingam, Rashmi Ranjan Rout, and D. V. L. N. Somayajulu. 2019. Adaptive deep Q-learning model for detecting social bots and influential users in online social networks. Applied Intelligence 49, 11 (Nov. 2019), 3947–3964. https://doi.org/10.1007/s10489-019-01488-3

O. Loyola-González, R. Monroy, J. Rodríguez, A. López-Cuevas, and J. I. Mata-Sánchez. 2019. Contrast Pattern-Based Classification for Bot Detection on Twitter. IEEE Access 7(2019), 45800–45817. https://doi.org/10.1109/ACCESS.2019.2904220

Samuel Maurus and Claudia Plant. 2017. Let’s See Your Digits: Anomalous-State Detection Using Benford’s Law. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD ’17). Association for Computing Machinery, New York, NY, USA, 977–986. https://doi.org/10.1145/3097983.3098101

Amanda Minnich, Nikan Chavoshi, Danai Koutra, and Abdullah Mueen. 2017. BotWalk: Efficient Adaptive Exploration of Twitter Bot Networks. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017(ASONAM ’17). Association for Computing Machinery, New York, NY, USA, 467–474. https://doi.org/10.1145/3110025.3110163

F. Morstatter, L. Wu, T. H. Nazer, K. M. Carley, and H. Liu. 2016. A new approach to bot detection: Striking the balance between precision and recall. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, New York, New Yourk, USA, 533–540. https://doi.org/10.1109/ASONAM.2016.7752287

V. Natarajan, S. Sheen, and R. Anitha. 2015. Multilevel Analysis to Detect Covert Social Botnet in Multimedia Social Networks. Comput. J. 58, 4 (April 2015), 679–687. https://doi.org/10.1093/comjnl/bxu063

H. Ping and S. Qin. 2018. A Social Bots Detection Model Based on Deep Learning Algorithm. In 2018 IEEE 18th International Conference on Communication Technology (ICCT). IEEE, New York, New Yourk, USA, 1435–1439.

Pandu Gumelar Pratama and Nur Aini Rakhmawati. 2019. Social Bot Detection on 2019 Indonesia President Candidate’s Supporter’s Tweets. Procedia Computer Science 161 (Jan. 2019), 813–820. https://doi.org/10.1016/j.procs.2019.11.187

Gayathri Rajendran, Arjun Ram, Vishnu Vijayan, and Prabaharan Poornachandran. 2020. Deep Temporal Analysis of Twitter Bots. In Machine Learning and Metaheuristics Algorithms, and Applications, Sabu M. Thampi, Ljiljana Trajkovic, Kuan-Ching Li, Swagatam Das, Michal Wozniak, and Stefano Berretti(Eds.). Springer Singapore, Singapore, 38–48.

Jorge Rodríguez-Ruiz, Javier Israel Mata-Sánchez, Raúl Monroy, Octavio Loyola-González, and Armando López-Cuevas. 2020. A one-class classification approach for bot detection on Twitter. Computers & Security 91 (April 2020), 101715. https://doi.org/10.1016/j.cose.2020.101715

Bjorn Ross, Laura Pilz, Benjamin Cabrera, Florian Brachten, German Neubaum, and Stefan Stieglitz. 2019. Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks. European Journal of Information Systems 28, 4 (2019), 394–412.

R. R. Rout, G. Lingam, and D. V. L. N. Somayajulu. 2020. Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network. IEEE Transactions on Computational Social Systems 7, 4 (2020), 1004–1018. https://doi.org/10.1109/TCSS.2020.2992223

S. Sadiq, Y. Yan, A. Taylor, M. Shyu, S. Chen, and D. Feaster. 2017. AAFA: Associative Affinity Factor Analysis for Bot Detection and Stance Classification in Twitter. In 2017 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, New York, New Yourk, USA, 356–365. https://doi.org/10.1109/IRI.2017.25

E. Shaabani, R. Guo, and P. Shakarian. 2018. Detecting Pathogenic Social Media Accounts without Content or Network Structure. In 2018 1st International Conference on Data Intelligence and Security (ICDIS). IEEE, New York, New Yourk, USA, 57–64. https://doi.org/10.1109/ICDIS.2018.00016

P. Shi, Z. Zhang, and K. R. Choo. 2019. Detecting Malicious Social Bots Based on Clickstream Sequences. IEEE Access 7(2019), 28855–28862. https://doi.org/10.1109/ACCESS.2019.2901864

Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos. 2017. DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD ’17). Association for Computing Machinery, New York, NY, USA, 1057–1066. https://doi.org/10.1145/3097983.3098087

H. Siddiqui, E. Healy, and A. Olmsted. 2017. Bot or not. In 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST). IEEE, New York, New York, USA, 462–463. https://doi.org/10.23919/ICITST.2017.8356448

Monika Singh, Divya Bansal, and Sanjeev Sofat. 2016. A Novel Technique to Characterize Social Network Users: Comparative Study. In Proceedings of the 6th International Conference on Communication and Network Security(ICCNS ’16). Association for Computing Machinery, New York, NY, USA, 75–79. https://doi.org/10.1145/3017971.3017977

Neharika Singh and Madhumita Chatterjee. 2019. A Novel Scheme for Bot Detection in Online Social Media: BotDefender. In International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018, Jude Hemanth, Xavier Fernando, Pavel Lafata, and Zubair Baig (Eds.). Springer International Publishing, Cham, 126–133.

V. S. Subrahmanian, A. Azaria, S. Durst, V. Kagan, A. Galstyan, K. Lerman, L. Zhu, E. Ferrara, A. Flammini, and F. Menczer. 2016. The DARPA Twitter Bot Challenge. Computer 49, 6 (June 2016), 38–46. https://doi.org/10.1109/MC.2016.183

Gabriel Tavares, Saulo Mastelini, and Sylvio Jr.2017. User Classification on Online Social Networks by Post Frequency. In Anais do XIII Simpósio Brasileiro de Sistemas de Informação. SBC, Porto Alegre, Brazil, 464–471.

E. Van Der Walt and J. Eloff. 2018. Using Machine Learning to Detect Fake Identities: Bots vs Humans. IEEE Access 6(2018), 6540–6549. https://doi.org/10.1109/ACCESS.2018.2796018

Onur Varol, Emilio Ferrara, Clayton A. Davis, Filippo Menczer, and Alessandro Flammini. 2017. Online Human-Bot Interactions: Detection, Estimation, and Characterization. arxiv:cs.SI/1703.03107

Yahan Wang, Chunhua Wu, Kangfeng Zheng, and Xiujuan Wang. 2018. Social Bot Detection Using Tweets Similarity. In Security and Privacy in Communication Networks, Raheem Beyah, Bing Chang, Yingjiu Li, and Sencun Zhu (Eds.). Springer International Publishing, Cham, 63–78.
Publicado
07/06/2021
DE MORAIS, Daniel Marques; DIGIAMPIETRI, Luciano Antonio. Methods and Challenges in Social Bots Detection: A Systematic Review. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 17. , 2021, Uberlândia. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 .