Abstract
Social bot detection is a challenging task and receives extensive attention in social security. Previous researches for this task often assume the labeled samples are abundant, which neglects the fact that labels of social bots are usually hard to derive from the real world. Meanwhile, graph neural networks (GNNs) have recently been applied to bot detection. Whereas most GNNs are based on the homophily assumption, where nodes of the same type are more likely to connect to each other. So methods relying on these two assumptions will degrade while encountering graphs with heterophily or lack of labeled data. To solve these challenges above, we analyze human-bot networks and propose SIRAN, which combines relation attention with initial residual connection to reduce and prevent the noise aggregated from neighbors to improve the capability of distinguishing different kinds of nodes on social graphs with heterophily. Then we use a consistency loss to boost the detection performance of the model for limited annotated data. Extensive experiments on two publicly available and independent social bot detection datasets illustrate SIRAN achieves state-of-the-art performance. Finally, further studies demonstrate the effectiveness of our model as well. We have deployed SIRAN online: https://botdetection.aminer.cn/robotmain.
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References
Abu-El-Haija, S., et al.: MixHop: higher-order graph convolutional architectures via sparsified neighborhood mixing. In: International Conference on Machine Learning, pp. 21–29. PMLR (2019)
Ali Alhosseini, S., Bin Tareaf, R., Najafi, P., Meinel, C.: Detect me if you can: spam bot detection using inductive representation learning. In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 148–153 (2019)
Alothali, E., Zaki, N., Mohamed, E.A., Alashwal, H.: Detecting social bots on twitter: a literature review. In: 2018 International Conference on Innovations in Information Technology (IIT), pp. 175–180. IEEE (2018)
Bojchevski, A., Shchur, O., Zügner, D., Günnemann, S.: NetGAN: generating graphs via random walks. In: International Conference on Machine Learning, pp. 610–619. PMLR (2018)
Chen, J., Ma, T., Xiao, C.: FastGCN: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018)
Chien, E., Peng, J., Li, P., Milenkovic, O.: Adaptive universal generalized pagerank graph neural network. arXiv preprint arXiv:2006.07988 (2020)
Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S.: Detecting automation of twitter accounts: are you a human, bot, or cyborg? IEEE Trans. Dependable Secure Comput. 9(6), 811–824 (2012)
Ciotti, V., Bonaventura, M., Nicosia, V., Panzarasa, P., Latora, V.: Homophily and missing links in citation networks. EPJ Data Sci. 5, 1–14 (2016)
Cresci, S.: A decade of social bot detection. Commun. ACM 63(10), 72–83 (2020)
Feng, S., Tan, Z., Li, R., Luo, M.: Heterogeneity-aware twitter bot detection with relational graph transformers. arXiv preprint arXiv:2109.02927 (2021)
Feng, S., et al.: TwiBot-22: towards graph-based twitter bot detection. arXiv preprint arXiv:2206.04564 (2022)
Feng, S., Wan, H., Wang, N., Li, J., Luo, M.: TwiBot-20: a comprehensive twitter bot detection benchmark. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 4485–4494 (2021)
Feng, W., et al.: Grand+: scalable graph random neural networks. arXiv preprint arXiv:2203.06389 (2022)
Feng, W., et al.: Graph random neural networks for semi-supervised learning on graphs. In: NeurIPS2020, pp. 22092–22103 (2020)
Ferrara, E.: Disinformation and social bot operations in the run up to the 2017 French presidential election. arXiv preprint arXiv:1707.00086 (2017)
Gallicchio, C., Micheli, A.: Graph echo state networks. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2010)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 922–929 (2019)
Himelein-Wachowiak, M., et al.: Bots and misinformation spread on social media: implications for COVID-19. J. Med. Internet Res. 23(5), e26933 (2021). https://doi.org/10.2196/26933. www.jmir.org/2021/5/e26933
Jarynowski, A.: Conflicts driven pandemic and war issues in social media via multi-layer approach of German twitter (2022)
Kelman, H.C.: Compliance, identification, and internalization three processes of attitude change. J. Conflict Resolut. 2(1), 51–60 (1958)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection. Inf. Sci. 467, 312–322 (2018)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)
Lim, D., et al.: Large scale learning on non-homophilous graphs: new benchmarks and strong simple methods. In: Advances in Neural Information Processing Systems 34 (2021)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Tech. Rep, Stanford InfoLab (1999)
Ping, H., Qin, S.: A social bots detection model based on deep learning algorithm. In: 2018 IEEE 18th International Conference on Communication Technology (ICCT), pp. 1435–1439. IEEE (2018)
Purtill, J.: Twitter bot network amplifying Russian disinformation about Ukraine war, researcher says (2022). www.abc.net.au/news/science/2022-03-30/ukraine-war-twitter-bot-network-amplifies-russian-disinformation/100944970. Accessed 05 Feb 2023
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Shi, W., Liu, D., Yang, J., Zhang, J., Wen, S., Su, J.: Social bots’ sentiment engagement in health emergencies: a topic-based analysis of the COVID-19 pandemic discussions on twitter. Int. J. Environ. Res. Public Health 17(22), 8701 (2020)
Simonovsky, M., Komodakis, N.: GraphVAE: towards generation of small graphs using variational autoencoders. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 412–422. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01418-6_41
Smart, B., Watt, J., Benedetti, S., Mitchell, L., Roughan, M.: # istandwithputin versus# istandwithukraine: the interaction of bots and humans in discussion of the Russia/Ukraine war. arXiv preprint arXiv:2208.07038 (2022)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 807–816 (2009)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4–24 (2020)
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.i., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: International Conference on Machine Learning, pp. 5453–5462. PMLR (2018)
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI Conference on Artificial Intelligence (2018)
Yang, K.C., Hui, P.M., Menczer, F.: Bot electioneering volume: visualizing social bot activity during elections. In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 214–217 (2019)
Zheng, X., Liu, Y., Pan, S., Zhang, M., Jin, D., Yu, P.S.: Graph neural networks for graphs with heterophily: a survey. arXiv preprint arXiv:2202.07082 (2022)
Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57–81 (2020)
Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., Koutra, D.: Beyond homophily in graph neural networks: current limitations and effective designs. Adv. Neural. Inf. Process. Syst. 33, 7793–7804 (2020)
Acknowledgments
This work was supported by Natural Science Foundation of China (NSFC) 61836013, 61825602, 62276148, and China Postdoctoral Science Foundation (2022 M711814).
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This work aims to propose a semi-supervised bot detection framework with heterophily, which will protect people from being disturbed by bots and ensure social security. We have deployed SIRAN online and it has been widely used. To the best of our knowledge, SIRAN currently ranks No.1 in Baidu (https://www.baidu.com/) and Google (https://www.google.com/) searches. However, as we know that “A coin has two sides", bot creators can also use SIRAN to improve their bots’ performance. To alleviate this problem, we have strengthened the authentication management of APIs.
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Zhou, M., Feng, W., Zhu, Y., Zhang, D., Dong, Y., Tang, J. (2023). Semi-Supervised Social Bot Detection with Initial Residual Relation Attention Networks. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14174. Springer, Cham. https://doi.org/10.1007/978-3-031-43427-3_13
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