{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T13:37:52Z","timestamp":1773754672325,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3612569","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:26:54Z","timestamp":1698391614000},"page":"3908-3916","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Multi-modal Social Bot Detection: Learning Homophilic and Heterophilic Connections Adaptively"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0565-3420","authenticated-orcid":false,"given":"Shilong","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences &amp; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2973-4970","authenticated-orcid":false,"given":"Boyu","family":"Qiao","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences &amp; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0911-5985","authenticated-orcid":false,"given":"Kun","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences &amp; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0155-1683","authenticated-orcid":false,"given":"Qianqian","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences &amp; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3101-1670","authenticated-orcid":false,"given":"Meng","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences &amp; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3622-3970","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences &amp; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308560.3316504"},{"key":"e_1_3_2_1_2_1","unstructured":"Jonathon M Berger and Jonathon Morgan. 2015. The ISIS Twitter Census: Defining and describing the population of ISIS supporters on Twitter. (2015)."},{"key":"e_1_3_2_1_3_1","volume-title":"Conference paper. SBP-BRiMS: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation","volume":"3","author":"Beskow David M","year":"2018","unstructured":"David M Beskow and Kathleen M Carley. 2018. Bot-hunter: a tiered approach to detecting & characterizing automated activity on twitter. In Conference paper. SBP-BRiMS: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation, Vol. 3. 3."},{"key":"e_1_3_2_1_4_1","volume-title":"Its all in a name: detecting and labeling bots by their name. Computational and mathematical organization theory","author":"Beskow David M","year":"2019","unstructured":"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, Vol. 25, 1 (2019), 24--35."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"e_1_3_2_1_6_1","volume-title":"GCCAD: Graph Contrastive Learning for Anomaly Detection","author":"Chen Bo","year":"2022","unstructured":"Bo Chen, Jing Zhang, Xiaokang Zhang, Yuxiao Dong, Jian Song, Peng Zhang, Kaibo Xu, Evgeny Kharlamov, and Jie Tang. 2022. GCCAD: Graph Contrastive Learning for Anomaly Detection. IEEE Transactions on Knowledge and Data Engineering (2022)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3409116"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2015.09.003"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2016.29"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411903"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3102498"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20314"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481949"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482019"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3487351.3488336"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Emilio Ferrara. 2017. Disinformation and social bot operations in the run up to the 2017 French presidential election. (2017).","DOI":"10.5210\/fm.v22i8.8005"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2818717"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308560.3317599"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583268"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 315--323","author":"Glorot Xavier","year":"2011","unstructured":"Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 315--323."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.81"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1177\/0165551516684296"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/UBMK.2017.8093483"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.08.019"},{"key":"e_1_3_2_1_27_1","volume-title":"BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency. arXiv preprint arXiv:2208.08320","author":"Lei Zhenyu","year":"2022","unstructured":"Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Qinghua Zheng, and Minnan Luo. 2022. BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency. arXiv preprint arXiv:2208.08320 (2022)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_1_29_1","volume-title":"International Conference on Machine Learning. PMLR, 13242--13256","author":"Li Xiang","year":"2022","unstructured":"Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian. 2022. Finding global homophily in graph neural networks when meeting heterophily. In International Conference on Machine Learning. PMLR, 13242--13256."},{"key":"e_1_3_2_1_30_1","unstructured":"Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy Mike Lewis Luke Zettlemoyer and Veselin Stoyanov. [n. d.]. RoBERTa: A Robustly Optimized BERT Pretraining Approach. ([n. d.])."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467350"},{"key":"e_1_3_2_1_32_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_33_1","volume-title":"Geom-GCN: Geometric Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Pei Hongbin","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. [n.,d.]. Geom-GCN: Geometric Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_34_1","first-page":"101771","article-title":"Bot2Vec: A general approach of intra-community oriented representation learning for bot detection in different types of social networks","volume":"5","author":"Pham P.","year":"2021","unstructured":"P. Pham, Ltt Nguyen, B. Vo, and U. Yun. 2021. Bot2Vec: A general approach of intra-community oriented representation learning for bot detection in different types of social networks. Information Systems 5 (2021), 101771.","journal-title":"Information Systems"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512195"},{"key":"e_1_3_2_1_36_1","unstructured":"Shuhao Shi Kai Qiao Jian Chen Shuai Yang Jie Yang Baojie Song Linyuan Wang and Bin Yan. 2023 a. MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark. (2023)."},{"key":"e_1_3_2_1_37_1","volume-title":"2023 b. Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection. arXiv e-prints","author":"Shi Shuhao","year":"2023","unstructured":"Shuhao Shi, Kai Qiao, Jie Yang, Baojie Song, Jian Chen, and Bin Yan. 2023 b. Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection. arXiv e-prints (2023), arXiv-2302."},{"key":"e_1_3_2_1_38_1","volume-title":"Feature engineering for social bot detection. Feature engineering for machine learning and data analytics","author":"Varol Onur","year":"2018","unstructured":"Onur Varol, Clayton A Davis, Filippo Menczer, and Alessandro Flammini. 2018. Feature engineering for social bot detection. Feature engineering for machine learning and data analytics, Vol. 311 (2018)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPS-ISA48467.2019.00021"},{"key":"e_1_3_2_1_40_1","volume-title":"International conference on machine learning. PMLR, 6861--6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861--6871."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00169"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5460"},{"key":"e_1_3_2_1_43_1","volume-title":"RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search. ACM Transactions on the Web","author":"Yang Yingguang","year":"2022","unstructured":"Yingguang Yang, Renyu Yang, Yangyang Li, Kai Cui, Zhiqin Yang, Yue Wang, Jie Xu, and Haiyong Xie. 2022. RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search. ACM Transactions on the Web (2022)."},{"key":"e_1_3_2_1_44_1","unstructured":"Xin Zheng Yixin Liu Shirui Pan Miao Zhang Di Jin and Philip S Yu. 2022. Graph neural networks for graphs with heterophily: A survey. (2022)."},{"key":"e_1_3_2_1_45_1","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume":"33","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems, Vol. 33 (2020), 7793--7804.","journal-title":"Advances in Neural Information Processing Systems"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","location":"Ottawa ON Canada","acronym":"MM '23","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612569","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3612569","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T23:57:19Z","timestamp":1755820639000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612569"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":45,"alternative-id":["10.1145\/3581783.3612569","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3612569","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}