Abstract
Artificial Intelligence (AI) in the form of social text bots has emerged online in social media platforms such as Reddit, Facebook, Twitter, and Instagram. The increased cultural dependency on information and online interaction has given rise to bad actors who use text bots to generate and post texts on these platforms. Using the influence of social media, these actors are able to quickly disseminate misinformation and disinformation to change public perception on controversial political, economic, and social issues. To detect such AI-bot-based misinformation, we build a machine-learning-based algorithm and test it against the popular text generation algorithm, Generative Pre-trained Transformer (GPT), to show its effectiveness for distinguishing between AI-generated and human generated texts. Using a Neural Network with three hidden layers and Small BERT, we achieve a high accuracy performance between \(97\%\) and \(99\%\) depending on the loss function utilized for detection classification. This paper aims to facilitate future research in text bot detection in order to defend against misinformation and explore future research directions.
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References
Knight, W.: Ai can write disinformation now-and dupe human readers, Wired, May 2021
Lyons, K.: A college student used GPT-3 to write fake blog posts and ended up at the top of hacker news, The Verge, August 2020
McGuffie, K., Newhouse, A.: The radicalization risks of GPT-3 and advanced neural language models, arXiv preprint arXiv:2009.06807 (2020)
Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection. Inf. Sci. 467, 312–322 (2018)
Efthimion, P.G., Payne, S., Proferes, N.: Supervised machine learning bot detection techniques to identify social twitter bots. SMU Data Sci. Rev. 1(2), 5 (2018)
Guo, B., Ding, Y., Yao, L., Liang, Y., Yu, Z.: The future of misinformation detection: new perspectives and trends, arXiv preprint arXiv:1909.03654 (2019)
Gehrmann, S., Strobelt, H., Rush, A.M.: Gltr: Statistical detection and visualization of generated text (2019)
Wei, F., Nguyen, U.T.: Twitter bot detection using bidirectional long short-term memory neural networks and word embeddings. In: 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 101–109 (2019)
Dukić, D., Keča, D., Stipić, D.: Are you human? detecting bots on twitter using bert. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 631–636. IEEE (2020)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805 (2018)
McInnes, L., Healy, J., Melville, J.: Umap: uniform manifold approximation and projection for dimension reduction, arXiv preprint arXiv:1802.03426 (2018)
Yarovaya, L.: Gamestop: Wallstreetbets trader army is back for a second share rally - here’s how to make sense of it, The Conversation, February 2021
Dhamija, A.R., Günther, M., Boult, T.E.: Reducing network agnostophobia, arXiv preprint arXiv:1811.04110 (2018)
Song, L., Sehwag, V., Bhagoji, A.N., Mittal, P.: A critical evaluation of open-world machine learning, arXiv preprint arXiv:2007.04391 (2020)
Eisner, B., Rocktäschel, T., Augenstein, I., Bošnjak, M., Riedel, S.: emoji2vec: Learning emoji representations from their description, arXiv preprint arXiv:1609.08359 (2016)
Kovaleva, O., Romanov, A., Rogers, A., Rumshisky, A.: Revealing the dark secrets of bert, arXiv preprint arXiv:1908.08593 (2019)
Clark, K., Khandelwal, U., Levy, O., Manning, C.D.: What does bert look at? an analysis of bert’s attention, arXiv preprint arXiv:1906.04341 (2019)
Acknowledgment
This material is based upon work supported by the National Science Foundation under Grant No. 1922410.
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Najee-Ullah, A., Landeros, L., Balytskyi, Y., Chang, SY. (2022). Towards Detection of AI-Generated Texts and Misinformation. In: Parkin, S., Viganò, L. (eds) Socio-Technical Aspects in Security. STAST 2021. Lecture Notes in Computer Science, vol 13176. Springer, Cham. https://doi.org/10.1007/978-3-031-10183-0_10
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DOI: https://doi.org/10.1007/978-3-031-10183-0_10
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