If you are having trouble accessing any of these papers online, this webpage from UW Library may be of assistance: https://uwaterloo.ca/library/services/get-access-anywhere Papers on Trust 1. J. Templeton and T. Tran; A cluster-based integrated trust establishment model for intelligent agents; Proceedings of Trust workshop at AAMAS 2021; 12 pages. https://ceur-ws.org/Vol-3022/paper8.pdf 2. S. Adhikari and P. Gmytrasiewicz; Telling friend from foe - towards a Bayesian approach to sincerity and deception; Proc. of Trust workshop at AAMAS 2021; 15 pages. https://ceur-ws.org/Vol-3022/paper7.pdf 3. S. Sebo et al. The ripple effects of vulnerability: the effects of a robot's vulnerable behavior on trust in human-robot teams; Proceedings of HRI 2018; 9 pages. https://scazlab.yale.edu/sites/default/files/files/strohkorb%20sebo_HRI18.pdf 4. J. Bentahar et al. Quantitative group trust: a two-stage verification approach; Proceedings of AAMAS 2022; 9 pages. https://dl.acm.org/doi/abs/10.5555/3535850.3535863 5. L. Zeynalvand et al.; COBRA: Context-aware Bernoulli neural networks for reputation assessment; Proceedings of AAAI 2020; 9 pages. https://arxiv.org/abs/1912.08446 6. H. Fang et al; A trust model stemmed from the diffusion theory for opinion evaluation; Proceedings of AAMS 2013; 8 pages. https://www.ifaamas.org/Proceedings/aamas2013/docs/p805.pdf 7. G. Guo et al; Trust SVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings; Proceedings of AAAI 2015; 7 pages; 2015. https://personal.ntu.edu.sg/zhangj/paper/aaai15-guo.pdf 8. Z. Zahedi et al; Trust-aware planning: modeling trust evolution in longitudinal human-robot interaction; Proceedings of XAIP workshop at ICAPS 2021; 8 pages; 2021. https://arxiv.org/abs/2105.01220 9. K. Yu et al; User trust dynamics: an investigation driven by differences in system performance; Proceedings of IUI 2017; 10 pagest; 2017. https://shlomo-berkovsky.github.io/files/pdf/IUI17b.pdf 10. G. Venkatadari et al.; Strengthening weak identities through inter-domain trust transfer; Proceedings of IW3C2 2016; 2016; 11 pages. https://www.lix.polytechnique.fr/~goga/papers/transfer_trust_WWW2016.pdf 11. D. Di Scala and P. Yolum; PCCART: Reinforcing trust in multiuser privacy agreement systems; Proceedings of AAMAS 2023; 9 pages; 2023. https://arxiv.org/pdf/2302.13650v1.pdf 12. P. Gutierrez et al; Trust-based community assessment; Pattern Recognition Letters; 2015; 11 pages. https://digital.csic.es/bitstream/10261/130828/1/PRL(67)_49-58.pdf 13. A. Coates et al; Simulating the impact of personality on fake news; Proceedings of Trust workshop at AAMAS 2021; 12 pages; 2021. https://ceur-ws.org/Vol-3022/paper6.pdf 14. D. Calvaresi et al; Reputation management in multi-agent systems using permissioned blockchain technology; Proceedings of 2018 Conference on Web Intelligence; 8 pages; 2018/ https://www.researchgate.net/publication/328692648_Reputation_Management_in_Mult i-Agent_Systems_Using_Permissioned_Blockchain_Technology/link/5bdc43034585150b2b 993e96/download 15. P. Zhou et al; A priori trust inference with context-aware stereotypical deep learning; Knowledge-Based Systems, Vol. 88, Iss. C, pp.97-106; 2015. https://dl.acm.org/doi/abs/10.1016/j.knosys.2015.08.003 https://personal.ntu.edu.sg/zhangj/paper/kbs-zhou.pdf 16. S. Jiang et al.; An evolutionary model for constructing robust trust networks; Proceedings of AAMAS 2013; 2013. https://www.ifaamas.org/Proceedings/aamas2013/docs/p813.pdf 17. M. Cheng et al; A general trust framework for multi-agent systems; Proceedings of AAMAS 2021; 2021. https://www.ifaamas.org/Proceedings/aamas2021/pdfs/p332.pdf 18. Y. Zhao et al.; Seeing isn't believing: towards more robust adversarial attack against real world object detectors; Proceedings of ACM SIGSAC conference 2019; 2019; 16 pages. https://arxiv.org/pdf/1812.10217.pdf 19. P. Shiwaswamy and D. Garcia-Garcia; Adversary or friend? An adversarial approach to improving recommender systems; Proceedings of ACM Rec Sys confefence 2022; 2022; 9 pages. https://dl.acm.org/doi/pdf/10.1145/3523227.3546784 20. Y. Zhang et al.; Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making; Proceedings of FAT 2020 conference; 11 pages. https://arxiv.org/pdf/2001.02114.pdf 21. Y. Che et al.; Efficient and trustworthy social navigation via explicit and implicit robot-human communication; IEEE Transactions on Robotics 36 (3), 692-707; 16 pages; 2020. https://arxiv.org/pdf/1810.11556.pdf 22. Z. Yang et al; Whom to trust? Elective learning for distributed Gaussian process regression; Proceedings of AAMAS 2024; 9 pages. https://arxiv.org/pdf/2402.03014.pdf 23. T. Eghtesad et al.; Hierarchical multi-agent reinforcement learning for assessing false-data injection attacks on transportation networks; Proceedings of AAMAS 2024; 8 pages. https://arxiv.org/pdf/2312.14625.pdf 24. M. do Carmo Alves et al.; It is among us: identifying adversaries in ad-hoc domains using Q-valued Bayesian estimations; Proceedings of AAMAS 2024; 9 pages. https://yelkhatib.github.io/papers/Alves2024amongus.pdf 25. M. Mechergui and S. Sreedharan; Goal alignment: re-analyzing value alignment problems using human-aware AI; ALA workshop at AAMAS 2023; 8 pages. https://arxiv.org/pdf/2302.00813.pdf 26. Z. Yu et al; KGTrust: evaluating trustworthiness of SIoT via knowledge enhanced graph neural networks; Proceedings of ACM TheWeb Conference 2023; 10 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583549 27. N. Scharowski et al.; Certification labels for trustworthy AI: insights from an empirical mixed-method study; Proceedings of FAccT 2023; 2023; 13 pages. https://arxiv.org/pdf/2305.18307.pdf 28. S. Sharma et al.; REFRESH: Responsible and efficient feature reselection guided by SHAP values; Proceedings of AIES 2023; 2023; 11 pages. https://dl.acm.org/doi/abs/10.1145/3600211.3604706 29. S. Beckers et al.; Quantifying harm; Proceedings of IJCAI 2023; 2023; 9 pages. https://www.ijcai.org/proceedings/2023/0041.pdf 30. Y. Cui et al.; Controllable universal fair representation learning; Proceedings of ACM TheWeb Conference 2023; 11 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583260 31. G. Rjoub et al.; Explainable trust-aware selection of autonomous vehicles using LIME for one-shot federated learning; Proc. of 2023 International Wireless Communications and Mobile Computing; https://ieeexplore.ieee.org/document/10182876 32. F. Cordoba et al.; Analyzing intentional behavior in autonomous agents under uncertainty; Proceedings of IJCAI 2023; 2023; 10 pages. https://www.ijcai.org/proceedings/2023/0042.pdf 33. Z. Lu et al.; Strategic adversarial attacks in AI-assisted decision making to reduce human trust and reliance; Proceedings of IJCAI 2023; 2023; 9 pages. https://www.ijcai.org/proceedings/2023/0337.pdf 34. H. Salman et al.; Raising the cost of malicious AI-powered image editing; Proceedings of ICML 2023; 2023; 11 pages plus appendices. https://openreview.net/pdf?id=mSKJS7YbwU 35. A. Khaddaj et al.; Rethinking backdoor attacks; Proceedings of ICML 2023; 11 pages plus appendix. https://openreview.net/pdf?id=mSKJS7YbwU 36. W. Cai et al.; Adaptive sampling strategies to construct equitable training datasets; Proceedings of FAccT 2022; 2022; 11 pages. https://dl.acm.org/doi/abs/10.1145/3531146.3533203 37. M. Dreyer et al.; From hope to safety: unlearning biases of deep models via gradient penalization in latent space; Proceedings of AAAI 2024; 2024; 13 pages plus appendices. https://arxiv.org/pdf/2308.09437.pdf Papers on Explainability 1. U. Soni et al; Not all users are the same: providing personalized explanations for sequential decision making problems; Proceedings of XAIP workshop at ICAPS 2021; 8 pages; 2021. https://arxiv.org/pdf/2106.12207.pdf 2. C. Wan et al.; Explainability's gain is optimality's loss? How explanations bias decision making; Proceedings of AI, Ethics and Society (AIES) 2022 conference; 10 pages; 2022. https://arxiv.org/pdf/2206.08705.pdf 3. E. Sklar and M. Zhar; Explanation through argumentation; Proceedings of HAI 2018; https://dl.acm.org/doi/pdf/10.1145/3284432.3284470 4. A. Bell et al; It's just not that simple: An empirical study of the accuracy-explainability trade-off in machine learning for public policy; Proceedings of the Fairness, Accountability and Transparency 2022 Conference; https://dl.acm.org/doi/pdf/10.1145/3531146.3533090 5. M. Persiani and T. Hellstrom; The mirror agent model: a Bayesian architecture for interpretable agent behaviour; Proceedings of Extraamas workshop at AAMAS 2022; 14 pages. https://www.diva-portal.org/smash/get/diva2:1656416/FULLTEXT02 6. V. Ordonez et al; Integration of local and global features explanation via CIU and explainable layers for improving global rules generation in ECLAIRE; Proc. Extraamas workshop at AAMAS 2022; 18 pages. https://www.researchgate.net/publication/361560732_Integration_of_local_and_global_features_explanation_with_global_rules_extraction_and_generation_tools 7. M. Brandao et al.; Explainability in multi-agent path/motion planning: user study-driven taxonomy and requirements; Proceedings of AAAMS 2022; 9 pages. https://www.martimbrandao.com/papers/Brandao2022-aamas.pdf https://dl.acm.org/doi/10.5555/3535850.3535871 8. A. Jacq et al; Lazy-MDPs: towards interpretable reinforcement learning by learning when to act; Proceedings of AAMAS 2022; 14 pages. https://arxiv.org/abs/2203.08542 9. P. Qian and V. Unhelkar; Evaluating the role of interactivity on improving transparency in autonomous agents; Proceedings of AAMAS 2022; 9 pages. https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1083.pdf 10. F. Barsotti et al.; Transparency, detection and imitation in strategic classification; Proceedings of IJCAI 2022; 9 pages; https://www.ijcai.org/proceedings/2022/0010.pdf 11. Q. Li et al; Optimal local explainer aggregration for interpretable prediction; Proceedings of AAAI 2022; 9 pages. https://arxiv.org/abs/2003.09466 https://ojs.aaai.org/index.php/AAAI/article/view/21458 12. D. Rajapaksha and C. Bergmeir; LIMREF: local interpretable model agnostic rule-based explanations for forecasting, with an application to electricity smart meter data; Proceedings of AAAI 2022; 9 pages. https://arxiv.org/abs/2202.07766 13. F. Mosca and J. Such; ELVIRA: an explainable agent for value and utility-driven nultiuser privacy; Proceedings of AAMAS 2021; 9 pages. https://www.ifaamas.org/Proceedings/aamas2021/pdfs/p916.pdf 14. X. Wang et al; A reinforcement learning framework for explainable recommendation; Proceedings of IEEE Conference on Data Mining 2018; 10 pages; 2018. https://www.microsoft.com/en-us/research/uploads/prod/2018/08/main.pdf 15. K. Framling et al; Comparison of contextual importance and utility with LIME and Shapley values; Proceedings of EXTRAAMAS workshop at AAMAS 2021; 15 pages; 2021. https://dl.acm.org/doi/abs/10.1007/978-3-030-82017-6_3 https://acris.aalto.fi/ws/portalfiles/portal/76979599/SCI_Fr_mling_Comparison_of _Contextual_Importance_and_Utility_with_LIME_and_Shapley_Values.pdf 16. X. Dai et al; Counterfactual explanations for prediction and diagnosis in XAI; Proceedings of AIES 2022; 2022; 12 pages. https://dl.acm.org/doi/pdf/10.1145/3514094.3534144 17. V. Contreras et al.; Explanation generation via decompositional rules extraction for head and neck cancer classification; Proceedings of Extraamas workshop at AAMAS 2023; p.187-212. https://link.springer.com/book/10.1007/978-3-031-40878-6 18. J.Zhang et al.; Model debiasing via gradient-based explanation on representation; Proceedings of AIES 2023; 11 pages. https://arxiv.org/pdf/2305.12178.pdf 19. A. Artelt and B. Hammer; Explain it in the same way: model-agnostic group fairness of counterfactual explanations; Proceedings of IJCAI 2023 XAI workshop; 13 pages. https://arxiv.org/pdf/2211.14858.pdf 20. W. Zhang et al.; Neuro-symbolic interpretable collaborative filtering for attribute-based recommendation; Proceedings of WWW 2022; 10 pages. https://dl.acm.org/doi/10.1145/3485447.3512042 https://web.archive.org/web/20220504023001id_/https://dl.acm.org/doi/pdf/10.1145/3485447.3512042 21. R. Fong and A. Vedaldi; Interpretable explanations of black boxes by meaningful perturbation; Proceedings of ICCV 2017; 9 pages. https://arxiv.org/pdf/1704.03296.pdf 22. P. Madumal et al.; Explainable reinforcement learning through a causal lens; Proceedings of AAAI 2020; 8 pages. https://arxiv.org/pdf/1905.10958.pdf 23. C-K. Yeh et al.; On the (in)fidelity and sensitivity of explanations; Proceedings of Neurips 2019; 12 pages plus appendices. https://arxiv.org/pdf/1901.09392.pdf 24. M. Dombrowski et al.; Trade-offs in fine-tuned diffusion models between accuracy and interpretability; Proceedings of AAAI 2024; 12 pages. https://arxiv.org/pdf/2303.17908.pdf 25. K. Boggess, S. Kraus and L. Feng; Explainable multi-agent reinforcement learning for temporal queries; Proceedings of IJCAI 2023; 9 pages. https://arxiv.org/pdf/2305.10378.pdf 26. Y. Du et al.; Towards explainable collaborative filtering with taste clusters learning; Proceedings of ACM TheWeb Conference 2023; 11 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583303 27. S. Zhang et al.; PaGE-Link: path-based graph neural network explanation for heterogeous link prediction; Proceedings of ACM TheWeb Conference 2023; 10 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583511 28. J. Shen et al.; "Why is this misleading?": Detecting news headline hallucinations with explanations; Proceedings of ACM TheWeb Conference 2023; 11 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583375 29. S. Chandramouli et al.; Interactive personalization of classifiers for explainability using multi-objective Bayesian optimization; Proceedings of UMAP 2023; 12 pages. https://dl.acm.org/doi/pdf/10.1145/3565472.3592956 30. A. Boggust et al.; Saliency cards: a framework to characterize and compare saliency methods; Proceedings of FaCCT Conference 2023; 12 pages. https://dl.acm.org/doi/pdf/10.1145/3593013.3593997 31. Z. Lin et al.; Contrastive explanations for reinforcement learning via embedded self predictions; Proceedings of ICLR 2021; 12 pages plus appendices. https://arxiv.org/pdf/2010.05180.pdf 32. S. Geng et al.; Improving personalized explanation generation through visualization; Proceedings of ACL 2022; 2022; 12 pages. https://aclanthology.org/2022.acl-long.20.pdf 33. M. Hee et al.; Decoding the underlying meaning of multimodal hateful memes; Proceedings of IJCAI 2023; 2023; 9 pages. https://www.ijcai.org/proceedings/2023/0665.pdf 34. H. Wang et al.; Evaluating GPT-3 generated explanations for hateful content moderation; Proceedings of IJCAI 2023; 2023; 9 pages. https://www.ijcai.org/proceedings/2023/0694.pdf 35. J. Gajcin and I. Dusparic; RACCER: Towards reachable and certain counterfactual explanstions for reinforcement learning; Proceedings of AAMAS 2024; 2024; 10 pages. https://arxiv.org/pdf/2303.04475.pdf 36. F. Leofante and N. Potyka; Promoting counterfactual robustness through diversity; Proceedings of AAAI 2024; 11 pages. https://arxiv.org/pdf/2312.06564.pdf 37. Y. Amitai et al.; Explaining reinforcement learning agents through counterfactual action outcomes; Proceedings of AAAI 2024; 12 pages. https://arxiv.org/pdf/2312.11118.pdf 38. S. Sreedharan et al; Bridging the gap: Providing post-hoc symbolic explanations for sequential decision-making problems with inscrutable representations; Proceedings of ICLR 2022; 12 pages plus appendices. https://arxiv.org/pdf/2002.01080.pdf 39. M. Millecamp et al.; To explain or not to explain: the effects of personal characteristics when explaining music recommendations; Proceedings of IUI 2019; 2019; 11 pages. https://www.cs.ubc.ca/~conati/522/532b-2019/papers/MillecampIUI2019.pdf 40. C. Burger et al.; "Are your Explanations Reliable?" Investigating the stability of LIME in explaining text classfiers by marrying XAIand adversarial attack; Proceedings of EMNLP 2023; 2023; 14 pages. https://aclanthology.org/2023.emnlp-main.792.pdf 41. N. Topin and M. Veloso; Generation of policy-level explanations for reinforcement learning; Proceedings of AAAI 2019; 2019; 8 pages. https://ojs.aaai.org/index.php/AAAI/article/view/4097 42. V. Petsiuk et al.; Black-box explanation of object detectors via saliency maps; Proceedings of CVPR 2021; 2021; 15 pages. https://arxiv.org/pdf/2006.03204.pdf Papers on Social Networks 1. P. Berenbrink et al.; Asynchronous opinion dynamics in social networks; Proceedings of AAMAS 2022; 9 pages. https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p109.pdf 2. M. Irfan et al; Cascades and overexposure in social networks: the budgeted case; Proceedings of AAMAS 2022; 9 pages. https://ifaamas.org/Proceedings/aamas2022/pdfs/p642.pdf 3. D. Zhang and A. Carver; Segregation in social networks of heterogeneous agents acting under incomplete information; Proceedings of AAMAS 2022; 9 pages. https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1455.pdf 4. A. Abouzeid et al.; Socially fair mitigation of misinformation on social networks via constraint stochastic optimization; Proceedings of AAAI 2022; 9 pages. https://arxiv.org/abs/2203.12537 5. T. Le et al; Socialbots on fire: modeling adversarial behaviors of socialbots via multi-agent hierarchical reinforcement learning; Proceedings of ACM The Web conference 2022; 10 pages; 2022. https://arxiv.org/pdf/2110.10655.pdf 6. C. Schweimer et al; Generating simple directed social network graphs for information spreading; Proceedings of ACM The Web conference 2022; 11 pages; 2022. https://arxiv.org/pdf/2205.02485.pdf 7. W. Xu et al; Evidence-aware fake news detection with graph neural networks; Proceedings of ACM the Web conference 2022; 10 pages; 2022. https://arxiv.org/pdf/2201.06885.pdf 8. M. Sun et al; DDGCN: Dual dynamic graph convolutional networks for rumour detection on social media; Proceedings of AAAI 2022; 9 pages; 2022. https://ojs.aaai.org/index.php/AAAI/article/view/20385 9. Z-Y Dou et al; Harnessing social media to identify homeless youth at risk of substance use; Proceedings of AAAI 2021; 2021. https://ojs.aaai.org/index.php/AAAI/article/view/17732 10. Q. Kong et al; Slipping to the extreme: a mixed method to explain how extreme opinions infiltrate online discussions; Proceedings of ICSWM 2022 conference; 12 pages; 2022. https://ojs.aaai.org/index.php/ICWSM/article/view/19312/19084 11. A. Tsang et al; Group-fairness in influence maximization; Proceedings of IJCAI 2019; 9 pages; 2019. https://people.scs.carleton.ca/~alantsang/files/groupfair19.pdf 12. H. Kamarthi et al; Influence maximization in unknown social networks: learning policies for effective graph sampling; Proceedings of AAMAS 2020; 10 pages; 2020. https://arxiv.org/pdf/1907.11625.pdf 13. A. Mashhadi et al; No walk in the park: the viability and fairness of social media analysis for parks and recreation; Proceedings of ICWSM 2021; 12 pages; 2021. https://ojs.aaai.org/index.php/ICWSM/article/view/18071/17874 14. K. Solovev and N. Prollochs; Moral emotions shape the virality of Covid-19 misinformation on social media; Proceedings of ACM the Web 2022; 12 pages; 2022. https://dl.acm.org/doi/pdf/10.1145/3485447.3512266 15. L. Tian et al.; DUCK: Rumour detection on social media by modelling user and comment propagation networks; Proceedings of NAACL 2022; 11pages; 2022. https://aclanthology.org/2022.naacl-main.364.pdf 16. P. Sobhani et al.; Exploring deep neural networks for multitarget stance detection; Computational Intelligence 2019; 16 pages. https://onlinelibrary-wiley-com.proxy.lib.uwaterloo.ca/doi/pdf/10.1111/coin.12189 17. S. Aref and Z. Neal; Detecting coalitions by optimally partitioning signed networks of political collaboration; Nature Scientific Reports 10, Article number 1506; 2020; 10 pages. https://www.nature.com/articles/s41598-020-58471-z 18. R. Baten et al.; Predicting future location categories of users in a large social platform; Proceedings of ICSWM 2023; 12 pages. https://ojs.aaai.org/index.php/ICWSM/article/view/22125/21904 19. X. Wang, O. Varol and T. Eliassi-Rad; Information access equality on generative models of complex networks; Applied Network Science 7, Article 54; 2022; 20 pages. https://appliednetsci.springeropen.com/articles/10.1007/s41109-022-00494-8 20. C. Hays et al.; Simplistic collection and labelling practices limit the utility of benchmark datasets for Twitter bot detection; Proceedings of ACM TheWeb Conference 2023; 10 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583214 21. Y. Dong et al.; A generalized deep Markov random fields framework for fake news detection; Proceedings of IJCAI 2023; 2023; 8 pages. https://www.ijcai.org/proceedings/2023/0529.pdf 22. U. Chitra and C. Musco; Analyzing the impact of filter bubbles on social network polarization; Proceedings of ACM Web Search and Data Mining Conference 2020; 9 pages. https://www.researchgate.net/profile/Uthsav-Chitra/publication/338758106_An alyzing_the_Impact_of_Filter_Bubbles_on_Social_Network_Polarization/links/5 eda6c4692851c9c5e81af45/Analyzing-the-Impact-of-Filter-Bubbles-on-Social-Ne twork-Polarization.pdf 23. H. Huang et al.; Multi-aspect diffusion network inference; Proceedings of ACM TheWeb Conference 2023; 9 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583228 24. X. Wu et al; ConsRec: learning consensus behind interactions for group recommendation; Proceedings of ACM TheWeb Conference 2023; 11 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583277 25. C. Gao et al.; Pairwise-interactions-based Bayesian inference of network structure from information cascades; Proceedings of ACM TheWeb Conference 2023; 11 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583231 26. Z. Wang et al.; Lightweight source localization for large-scale social networks; Proceedings of ACM TheWeb Conference 2023; 9 pages; https://dl.acm.org/doi/pdf/10.1145/3543507.3583299 27. X. Song et al.; xGCN: an extreme graph convolutional network for large-scale social link prediction; Proceedings of ACM TheWeb Conference 2023; 11 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583340 28. A. Zehmakan et al.; Why rumors spread fast in social networks and how to stop it; Proceedings of IJCAI 2023; 2023; 9 pages. https://www.ijcai.org/proceedings/2023/0027.pdf 29. X. Lu et al.; Continuous-time graph learning for cascade popularity prediction; Proceedings of IJCAI 2023; 2023; 9 pages. https://www.ijcai.org/proceedings/2023/0247.pdf 30. Z. Yu et al.; Commonsense knowledge enhanced sentiment dependency graph for sarcasm detection; Proceedings of IJCAI 2023; 2023; 9 pages. https://www.ijcai.org/proceedings/2023/0269.pdf 31. Y. Yuan et al.; Mental health coping stories on social media: a causal-inference study of Papageno effect; Proceedings of ACM TheWeb Conference 2023; 9 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583350 32. B. He et al.; Reinforcement learning-based counter-misinformation response generation: a case study of Covid-19 vaccine misinformation; Proceedings of ACM TheWeb Conference 2023; 12 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583388 33. Y. Wang et al.; Label information enhanced fraud detection against low homophily in graphs; Proceedings of ACMTheWeb Conference 2023; 11 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583373 34. G. Zhang et al.; An attentional multi-scale co-evolving model for dynamic link prediction; Proceedings of ACMTheWeb Conference 2023; 9 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583396 35. S. Diao et al.; Hashtag-guided low-resource tweet classification; Proceedings of ACM TheWeb Conference 2023; 12 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583194 36. Y. Luo et al.; Improving (dis)agreement detection with inductive social relation information from comment-reply interactions; Proceedings of ACM TheWeb Conference 2023; 10 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583314 37. L. Tian et al.; MetaTroll: few-shot detection of state-sponsored trolls with transformer adapters; Proceedings of ACM TheWeb Conference 2023; 11 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583417 38. J. Wu et al.; Exploring social media for early detection of depression in Covid-19 patients; Proceedings of ACM TheWeb Conference 2023; 10 pages. https://dl.acm.org/doi/pdf/10.1145/3543507.3583867 39. J. Kurrek et al.; Enriching abusive language detection with community context; Proceedings of Sixth Workshop on Online Abuse and Harms 2022; 12 pages. https://aclanthology.org/2022.woah-1.13.pdf 40. K. Pelrine et al.; Towards reliable misinformation mitigation: generalization, uncertainty and GPT-4; Proceedings of EMNLP 2023; 12 pages plus appendices. https://aclanthology.org/2023.emnlp-main.395.pdf 41. S. Tzeng et al; Norm enforcement with a soft touch: faster emergence, happier agents; Proceedings of AAMAS 2024; 12 pages. https://arxiv.org/pdf/2401.16461.pdf 42. S. Cen and D. Shah; Regulating algorithmic filtering on social media; Proceedings of Neurips 2021; 14 pages plus appendix. https://arxiv.org/abs/2006.09647 43. T. Nguyen and K. Rudra; Rationale aware contrastive learning based approaches to classify and summarize crisis-related microblogs; Proceedings of CIKM 2022; 11 pages. https://dl.acm.org/doi/abs/10.1145/3511808.3557426