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    <titleInfo>
        <title>Tackling the Myriads of Collusion Scams on YouTube Comments of Cryptocurrency Videos</title>
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        <namePart type="given">Sadat</namePart>
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            <title>Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>Despite repeated measures, YouTube’s comment section has been a fertile ground for scammers. With the growth of the cryptocurrency market and obscurity around it, a new form of scam, namely “Collusion Scam” has emerged as a dominant force within YouTube’s comment space. Unlike typical scams and spams, collusion scams employ a cunning persuasion strategy, using the facade of genuine social interactions within comment threads to create an aura of trust and success to entrap innocent users. In this research, we collect 1,174 such collusion scam threads and perform a detailed analysis, which is tailored towards the successful detection of these scams. We find that utilization of the collusion dynamics can provide an accuracy of 96.67% and an F1-score of 93.04%. Furthermore, we demonstrate the robust predictive power of metadata associated with these threads and user channels, which act as compelling indicators of collusion scams. Finally, we show that modern LLM, like chatGPT, can effectively detect collusion scams without the need for any training.</abstract>
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%0 Conference Proceedings
%T Tackling the Myriads of Collusion Scams on YouTube Comments of Cryptocurrency Videos
%A Shahriar, Sadat
%A Mukherjee, Arjun
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F shahriar-mukherjee-2023-tackling
%X Despite repeated measures, YouTube’s comment section has been a fertile ground for scammers. With the growth of the cryptocurrency market and obscurity around it, a new form of scam, namely “Collusion Scam” has emerged as a dominant force within YouTube’s comment space. Unlike typical scams and spams, collusion scams employ a cunning persuasion strategy, using the facade of genuine social interactions within comment threads to create an aura of trust and success to entrap innocent users. In this research, we collect 1,174 such collusion scam threads and perform a detailed analysis, which is tailored towards the successful detection of these scams. We find that utilization of the collusion dynamics can provide an accuracy of 96.67% and an F1-score of 93.04%. Furthermore, we demonstrate the robust predictive power of metadata associated with these threads and user channels, which act as compelling indicators of collusion scams. Finally, we show that modern LLM, like chatGPT, can effectively detect collusion scams without the need for any training.
%U https://aclanthology.org/2023.ranlp-1.114
%P 1066-1075

Markdown (Informal)

[Tackling the Myriads of Collusion Scams on YouTube Comments of Cryptocurrency Videos](https://aclanthology.org/2023.ranlp-1.114) (Shahriar & Mukherjee, RANLP 2023)

• Tackling the Myriads of Collusion Scams on YouTube Comments of Cryptocurrency Videos (Shahriar & Mukherjee, RANLP 2023)

ACL

• Sadat Shahriar and Arjun Mukherjee. 2023. Tackling the Myriads of Collusion Scams on YouTube Comments of Cryptocurrency Videos. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 1066–1075, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.