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Corresponding Author

Kayhan Zrar Ghafoor

Authors ORCID

0000-0001-9046-9475

Document Type

Research Article

Abstract

In the past decade social media platforms growing rapidly and they are part of our routine life. Each platform has its own specification which uses for specific purposes. After this widely spread, those SMPs were targeted by the cybercriminals to cast their malicious activities. There are many different malicious activities in SMPs such as spamming, phishing, fake account. In these papers, Bots activities in SMPs one of those threats which include fake accounts, fake friends/followers, spreading misinformation by purpose, and many more. At the beginning of our work, we explain all terminology related to this topic to have a clear understanding of what is going on now. Then we reviewed the recent papers about this topic. We found out different models suggested by the researchers for recognizing those malicious activities. Until now most of the work focusing on Twitter as a platform, English as a language, and machine learning as a detection method but there are many gaps in this research area because Twitter is the 17th most used SMPs in 2020, also there are many malicious actions in other languages, and detection method needs lots of improvement in reliability, accuracy, real-time detection, and performance area. As a result, we are at the beginning of the game and we need lots of improvement for controlling the bot’s activities. Besides all technical term also people awareness has a big impact on controlling a bot because most of the times the botmaster use people ignorance to make their actions easy

Keywords

Social bots, Social media, malicious activities, Bot detectionRESE A RCHART I C L E

References

Richards D, Caldwell PHY, Go H. Impact of social media on the health of children and young people. J Paediatr Child Health 2015;51:1152–7. https://doi.org/10.1111/jpc.13023.

Karunakar E, Pavani VDR, Priya TNI, Sri V, Tiruvalluru K. Ensemble Fake Profile Detection Using Machine Learning ( ML ) n.d.;10:1071–7. (Volume 10 Issue 3 - 2020)

Ram A, Galav RK. Detection and identification of bogus profiles in online social network using machine learning methods. Eur J Mol Clin Med 2020;7:395–400.

Pulido CM, Villarejo-Carballido B, Redondo-Sama G, Gómez A. COVID-19 infodemic: More retweets for science-based information on coronavirus than for false information. Int Sociol 2020;35:377–92. https://doi.org/10.1177/0268580920914755.

Hanouna S, Neu O, Pardo S, Tsur O, Zahavi H. Sharp power in social media : Patterns from datasets across electoral campaigns. Aust New Zeal J Eur Stud 2019;11:95–111.

\Gallotti R, Valle F, Castaldo N, Sacco P, De Domenico M. Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics. Nat Hum Behav 2020. https://doi.org/10.1038/s41562-020-00994-6.

Ferrara E. What Types of Covid-19 Conspiracies Are Populated By Twitter Bots? ArXiv 2020. https://doi.org/10.5210/fm.v25i6.10633.

Khanday AMUD, Khan QR, Rabani ST. Identifying propaganda from online social networks during COVID-19 using machine learning techniques. Int J Inf Technol 2020. https://doi.org/10.1007/s41870-020-00550-5. Singh L, Bansal S, Bode L, Budak C, Chi G, Kawintiranon K, et al. A first look at COVID-19 information and misinformation sharing on Twitter. ArXiv 2020.

Cresci S. Detecting malicious social bots: Story of a neverending clash. Lect Notes Comput Sci (Including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2020;12021 LNCS:77–88. https://doi.org/10.1007/978-3-030-39627-5_7.

Okoli C, Schabram K. Working Papers on Information Systems A Guide to Conducting a Systematic Literature Review of Information Systems Research n.d.;10.

Adewole KS, Anuar NB, Kamsin A, Varathan KD, Razak SA. Malicious accounts: Dark of the social networks. J Netw Comput Appl 2017;79:41–67. https://doi.org/10.1016/j.jnca.2016.11.030.

Ferrara BYE, Varol O, Davis C, Menczer F, Flammini A. The Rise of Social Bots n.d. 2014 Grimme C, Preuss M, Adam L, Trautmann H. Social Bots: Human-Like by Means of Human Control? Big Data 2017;5:279–93. https://doi.org/10.1089/big.2017.0044.

Davis CA, Varol O, Ferrara E, Flammini A, Menczer F. BotOrNot: A System to Evaluate Social Bots 2016:14–6. https://doi.org/10.1145/2872518.2889302.

Karataş A, Şahin S. A Review on Social Bot Detection Techniques and Research Directions. Proc Int Secur Cryptol Conf Turkey 2017:156–61.

Stieglitz S, Brachten F, Ross B, Jung AK. Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accounts. ArXiv 2017:1–11.

Alothali E, Zaki N, Mohamed EA, Alashwal H. Detecting Social Bots on Twitter: A Literature Review. Proc 2018 13th Int Conf Innov Inf Technol IIT 2018 2019:175–80. https://doi.org/10.1109/INNOVATIONS.2018.8605995.

Cardoso Durier da Silva F, Vieira R, Garcia AC. Can Machines Learn to Detect Fake News? A Survey Focused on Social Media. Proc 52nd Hawaii Int Conf Syst Sci 2019;6:2763–70. https://doi.org/10.24251/hicss.2019.332.

Orabi M, Mouheb D, Al Aghbari Z, Kamel I. Detection of Bots in Social Media: A Systematic Review. Inf Process Manag 2020;57:102250. https://doi.org/10.1016/j.ipm.2020.102250.

Ferrara E, Varol O, Davis C, Menczer F, Flammini A. The rise of social bots. Commun ACM 2016;59:96–104. https://doi.org/10.1145/2818717.

Orabi M, Mouheb D, Aghbari Z Al, Kamel I. Detection of Bots in Social Media : A Systematic Review. Inf Process Manag 2020;57:102250. https://doi.org/10.1016/j.ipm.2020.102250.

Daya AA, Salahuddin MA, Limam N, Boutaba R. BotChase: Graph-Based Bot Detection Using Machine Learning. IEEE Trans Netw Serv Manag 2020;17:15–29. https://doi.org/10.1109/TNSM.2020.2972405.

Thesis M. Fraudulent social media users detection by a supervised machine learning technique 2018.

Khalil A, Hajjdiab H, Al-Qirim N. Detecting fake followers in twitter: A machine learning approach. Int J Mach Learn Comput 2017;7:198–202. https://doi.org/10.18178/ijmlc.2017.7.6.646.

Kudugunta S, Ferrara E. Deep neural networks for bot detection. Inf Sci (Ny) 2018;467:312–22. https://doi.org/10.1016/j.ins.2018.08.019.

Abu-El-Rub N, Mueen A. BotCamp: Bot-driven interactions in social campaigns. Web Conf 2019 - Proc World Wide Web Conf WWW 2019 2019:2529–35. https://doi.org/10.1145/3308558.3313420.

Dorri A, Abadi M, Dadfarnia M. SocialBotHunter: Botnet detection in twitter-like social networking services using semi- Polytechnic Journal ● Vol 12 ● No 2 ● 2022 | 228 supervised collective classification. Proc - IEEE 16th Int Conf Dependable, Auton Secur Comput IEEE 16th Int Conf Pervasive Intell Comput IEEE 4th Int Conf Big Data Intell Comput IEEE 3 2018:496–503. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec. 2018.00097.

Battur R, Yaligar N. Twitter Bot Detection using Machine Learning Algorithms 2019;8:304–7.

Yang KC, Torres-Lugo C, Menczer F. Prevalence of LowCredibility Information on Twitter During the COVID-19 Outbreak. ArXiv 2020. https://doi.org/10.36190/2020.16.

Pozzana I, Ferrara E. Measuring Bot and Human Behavioral Dynamics. Front Phys 2020;8:1–11. https://doi.org/10.3389/fphy.2020.00125.

Sahoo SR, Gupta BB. Popularity-based detection of malicious content in facebook using machine learning approach. Adv Intell Syst Comput 2020;1045:163–76. https://doi.org/10.1007/978-981-15-0029-9_13.

Chu Z, Gianvecchio S, Wang H, Jajodia S. Detecting automation of Twitter accounts: Are you a human, bot, or cyborg? IEEE Trans Dependable Secur Comput 2012;9:811– 24. https://doi.org/10.1109/TDSC.2012.75.

Yang KC, Varol O, Hui PM, Menczer F. Scalable and generalizable social bot detection through data selection. ArXiv 2019. https://doi.org/10.1609/aaai.v34i01.5460.

Iş H, Tuncer T. Interaction-based behavioral analysis of twitter social network accounts. Appl Sci 2019;9. https://doi.org/10.3390/app9204448.

Publication Date

2-1-2023

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