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

Note: If your paper has appendices you do not have to read but
you are welcome to do so if helpful to you.

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.
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.
https://ceur-ws.org/Vol-3022/paper7.pdf

3. J. Bentahar et al.
Quantitative group trust: a two-stage verification approach;
Proceedings of AAMAS 2022.
https://dl.acm.org/doi/abs/10.5555/3535850.3535863

4. L. Zeynalvand et al.;
COBRA: Context-aware Bernoulli neural networks for reputation assessment;
Proceedings of AAAI 2020.
https://arxiv.org/abs/1912.08446

5. H. Fang et al;
A trust model stemmed from the diffusion theory for opinion evaluation;
Proceedings of AAMS 2013.
https://www.ifaamas.org/Proceedings/aamas2013/docs/p805.pdf

6. 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.
https://personal.ntu.edu.sg/zhangj/paper/aaai15-guo.pdf

7. Z. Zahedi et al;
Trust-aware planning: modeling trust evolution in longitudinal
human-robot interaction;
Proceedings of XAIP workshop at ICAPS 2021.
https://arxiv.org/abs/2105.01220

8. G. Venkatadari et al.;
Strengthening weak identities through inter-domain trust transfer;
Proceedings of IW3C2 2016.
https://www.lix.polytechnique.fr/~goga/papers/transfer_trust_WWW2016.pdf

9. D. Di Scala and P. Yolum;
PCCART: Reinforcing trust in multiuser privacy agreement systems;
Proceedings of AAMAS 2023.
https://arxiv.org/pdf/2302.13650v1.pdf

10. Z. Lu et al.;
Strategic adversarial attacks in AI-assisted decision making to
reduce human trust and reliance; Proceedings of IJCAI 2023.
https://www.ijcai.org/proceedings/2023/0337.pdf

11. P. Gutierrez et al;
Trust-based community assessment;
Pattern Recognition Letters; 2015.
https://digital.csic.es/bitstream/10261/130828/1/PRL(67)_49-58.pdf

12. 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

13. S. Jiang et al.;
An evolutionary model for constructing robust trust networks;
Proceedings of AAMAS 2013.
https://www.ifaamas.org/Proceedings/aamas2013/docs/p813.pdf

14. M. Cheng et al;
A general trust framework for multi-agent systems;
Proceedings of AAMAS 2021.
https://www.ifaamas.org/Proceedings/aamas2021/pdfs/p332.pdf

15. P. Shiwaswamy and D. Garcia-Garcia;
Adversary or friend? An adversarial approach to improving recommender systems;
Proceedings of ACM Rec Sys confefence 2022.
https://dl.acm.org/doi/pdf/10.1145/3523227.3546784

16.  Y. Che et al.;
Efficient and trustworthy social navigation via explicit and
implicit robot-human communication;
IEEE Transactions on Robotics 36 (3), 692-707; 2020.
https://arxiv.org/pdf/1810.11556.pdf

17. Z. Yang et al;
Whom to trust? Elective learning for distributed Gaussian process
regression; Proceedings of AAMAS 2024.
https://arxiv.org/pdf/2402.03014.pdf

18. M. Mechergui and S. Sreedharan;
Goal alignment: re-analyzing value alignment problems using human-aware AI;
ALA workshop at AAMAS 2023.
https://arxiv.org/pdf/2302.00813.pdf

19. Z. Yu et al;
KGTrust: evaluating trustworthiness of SIoT via knowledge
enhanced graph neural networks;
Proceedings of ACM TheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583549

20. S. Sharma et al.;
REFRESH: Responsible and efficient feature reselection guided by SHAP values;
Proceedings of AIES 2023.
https://dl.acm.org/doi/abs/10.1145/3600211.3604706

21. S. Beckers et al.;
Quantifying harm; Proceedings of IJCAI 2023.
https://www.ijcai.org/proceedings/2023/0041.pdf

22. Y. Cui et al.;
Controllable universal fair representation learning;
Proceedings of ACM TheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583260

23. F. Cordoba et al.;
Analyzing intentional behavior in autonomous agents under uncertainty;
Proceedings of IJCAI 2023.
https://www.ijcai.org/proceedings/2023/0042.pdf

24. A. Khaddaj et al.;
Rethinking backdoor attacks; Proceedings of ICML 2023.
https://openreview.net/pdf?id=mSKJS7YbwU

25. W. Cai et al.;
Adaptive sampling strategies to construct equitable training datasets;
Proceedings of FAccT 2022. 
https://dl.acm.org/doi/abs/10.1145/3531146.3533203

26. M. Dreyer et al.;
From hope to safety: unlearning biases of deep models via
gradient penalization in latent space;
Proceedings of AAAI 2024.
https://arxiv.org/pdf/2308.09437.pdf

27. R. Yu et al;
Why do decision makers (not) make use of AI?
Proceedings of AIES 2025.
https://ojs.aaai.org/index.php/AIES/article/view/36758/38896

28. S. Zhou et al;
Assessing Automated Fact-Checking for Medical LLM Responses
with Knowledge Graphs; Proceedings of AAAI 2026.
https://arxiv.org/pdf/2511.12817

29. A. Kothari et al;
When the Domain Expert Has No Time and the LLM Developer Has No Clinical
Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospital;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2508.08504

30. T. Neumann et al;
Should You Use LLMs to Simulate Opinions?
Quality Checks for Early-Stage Deliberation;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2504.08954

31. A. Jiang et al;
EduGuardBench: A Holistic Benchmark for Evaluating the Pedagogical Fidelity
and Adversarial Safety of LLMs as Simulated Teachers;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2511.06890

32. J. Xu et al;
PulseMind: A Multi-Modal Medical Model for Real-World Clinical Diagnosis;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2601.07344

33. N. Xu et al;
Bridging the Copyright Gap: Do Large Vision-Language Models Recognize and
Respect Copyrighted Content?;
Proceedings of AAAI 2026.
https://arxiv.org/abs/2512.21871

34. W. Zheng et al;
Navigating Through Paper Flood: Advancing LLM-based Paper Evaluation
through Domain-aware Retrieval and Latent Reasoning;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2508.05129

35. S. Si et al;
Teaching Large Language Models to Maintain Contextual Faithfulness via
Synthetic Tasks and Reinforcement Learning;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2505.16483

36. Y. Zhu et al;
MoHoBench: Assessing Honesty of Multimodal Large Language
Models via Unanswerable Visual Questions;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2507.21503

37. B. Pang et al.;
CABIN: Debiasing Vision-Language Models using Backdoor Adjustments;
Proceedings of IJCAI 2025.
https://www.ijcai.org/proceedings/2025/55

38. A. Das Antar et al;
Do your guardrails even guard? Method for evaluating effectiveness of
moderation guardails is aligning LLM outputs with expert user expectations;
Proceedings of AIES 2025.
https://ojs.aaai.org/index.php/AIES/article/view/36583/38721

39. C. Wang et al.;
Joint evaluation of answer and reasoning consistency for hallucination
detection in large reasoning models;
Proceedings of AAAI 2026.
https://arxiv.org/abs/2506.04832

40. C. Yang et al.;
Towards AI-45 degrees Law: A Roadmap to Trustworthy AGI;
Proceedings of EMNLP 2024.
https://arxiv.org/abs/2412.14186

41. J. Wei et al.;
Chain of thought prompting elicits reasoning in large language models;
Proceedings of Neurips 2022.
https://arxiv.org/abs/2201.11903

42. H. Han et al.;
Small language model can self-correct;
Proceedings of AAAI 2024.
https://arxiv.org/abs/2401.07301

43. G. Bansal et al.;
Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork;
Proceedings of AAAI 2021.
https://ojs.aaai.org/index.php/AAAI/article/view/17359

44. O. Kwon et al;
SLM as Guardian: Pioneering AI Safety with Small Language Models;
Proceedings of EMNLP 2024.
https://arxiv.org/abs/2405.19795

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.
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.
https://arxiv.org/pdf/2206.08705.pdf

3. M. Persiani and T. Hellstrom;
The mirror agent model: a Bayesian architecture for interpretable agent
behaviour; Proceedings of Extraamas workshop at AAMAS 2022.
https://www.diva-portal.org/smash/get/diva2:1656416/FULLTEXT02

4. R. Mothilal et al.;
Explaining machine learning classifiers through diverse counterfactual
explanations.
Proceedings of FAT 2020.
https://arxiv.org/abs/1905.07697

5. A. Jacq et al;
Lazy-MDPs: towards interpretable reinforcement learning by learning when to
act; Proceedings of AAMAS 2022.
https://arxiv.org/abs/2203.08542

6. F. Barsotti et al.;
Transparency, detection and imitation in strategic classification;
Proceedings of IJCAI 2022.
https://www.ijcai.org/proceedings/2022/0010.pdf

7. Q. Li et al;
Optimal local explainer aggregration for interpretable prediction;
Proceedings of AAAI 2022.
https://arxiv.org/abs/2003.09466
https://ojs.aaai.org/index.php/AAAI/article/view/21458

8. 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.
https://arxiv.org/abs/2202.07766

9. F. Mosca and J. Such;
ELVIRA: an explainable agent for value and utility-driven nultiuser privacy;
Proceedings of AAMAS 2021.
https://www.ifaamas.org/Proceedings/aamas2021/pdfs/p916.pdf

10. X. Wang et al;
A reinforcement learning framework for explainable recommendation;
Proceedings of IEEE Conference on Data Mining 2018.
https://www.microsoft.com/en-us/research/uploads/prod/2018/08/main.pdf

11. X. Dai et al;
Counterfactual explanations for prediction and diagnosis in XAI;
Proceedings of AIES 2022; 2022.
https://dl.acm.org/doi/pdf/10.1145/3514094.3534144

12. V. Contreras et al.;
Explanation generation via decompositional rules extraction
for head and neck cancer classification;
Proceedings of Extraamas workshop at AAMAS 2023.
https://link.springer.com/book/10.1007/978-3-031-40878-6

13. J.Zhang et al.;
Model debiasing via gradient-based explanation on representation;
Proceedings of AIES 2023.
https://arxiv.org/pdf/2305.12178.pdf

14. A. Artelt and B. Hammer;
Explain it in the same way: model-agnostic group fairness of
counterfactual explanations; Proceedings of IJCAI 2023 XAI workshop.
https://arxiv.org/pdf/2211.14858.pdf

15. W. Zhang et al.; 
Neuro-symbolic interpretable collaborative filtering for attribute-based
recommendation; Proceedings of WWW 2022.
https://dl.acm.org/doi/10.1145/3485447.3512042

16. P. Madumal et al.;
Explainable reinforcement learning through a causal lens;
Proceedings of AAAI 2020.
https://arxiv.org/pdf/1905.10958.pdf

17. C-K. Yeh et al.;
On the (in)fidelity and sensitivity of explanations;
Proceedings of Neurips 2019.
https://arxiv.org/pdf/1901.09392.pdf

18.  Y. Du et al.;
Towards explainable collaborative filtering with taste clusters learning;
Proceedings of ACM TheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583303

19. S. Zhang et al.;
PaGE-Link: path-based graph neural network explanation for
heterogeous link prediction;
Proceedings of ACM TheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583511

20. J. Shen et al.;
"Why is this misleading?": Detecting news headline hallucinations
with explanations; Proceedings of ACM TheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583375

21.  S. Chandramouli et al.;
Interactive personalization of classifiers for explainability
using multi-objective Bayesian optimization;
Proceedings of UMAP 2023.
https://dl.acm.org/doi/pdf/10.1145/3565472.3592956

22. A. Boggust et al.;
Saliency cards: a framework to characterize and compare saliency methods;
Proceedings of FaCCT Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3593013.3593997

23. Z. Lin et al.;
Contrastive explanations for reinforcement learning via embedded
self predictions;
Proceedings of ICLR 2021.
https://arxiv.org/pdf/2010.05180.pdf

24. M. Hee et al.;
Decoding the underlying meaning of multimodal hateful memes;
Proceedings of IJCAI 2023.
https://www.ijcai.org/proceedings/2023/0665.pdf

25. H. Wang et al.;
Evaluating GPT-3 generated explanations for hateful content moderation;
Proceedings of IJCAI 2023.
https://www.ijcai.org/proceedings/2023/0694.pdf

26. J. Gajcin and I. Dusparic;
RACCER: Towards reachable and certain counterfactual explanstions
for reinforcement learning; Proceedings of AAMAS 2024.
https://arxiv.org/pdf/2303.04475.pdf

27. F. Leofante and N. Potyka;
Promoting counterfactual robustness through diversity;
Proceedings of AAAI 2024.
https://arxiv.org/pdf/2312.06564.pdf

28. 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.
https://aclanthology.org/2023.emnlp-main.792.pdf

29. N. Topin and M. Veloso;
Generation of policy-level explanations for reinforcement learning;
Proceedings of AAAI 2019.
https://ojs.aaai.org/index.php/AAAI/article/view/4097

30. F. Giorgi et al.;
Generate Counterfactual Explanations for Graph Neural Networks
from Node Feature Perturbations;
Proceedings of AAAI 2026.
227250/preprint_pdf/b60eaa27af14be4554d1caa0ae5a5201.pdf

31. J. Li et al.;
From Single to Societal: Analyzing Persona-Induced Bias
in Multi-Agent Interactions;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2511.11789

32. O. Barkan et al.;
Fidelity-Aware Recommendation Explanations via Stochastic Path Integration;
Proceedings of AAAI 2026.
https://www.arxiv.org/pdf/2511.18047

33. K. Bogess et al.;
Explaining Decentralized Multi-Agent Reinforcement Learning Policies;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2511.10409

34. P. Borycki et al;
EPIC: Explanation of pretrained image classification networks via protoypes;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2505.12897

35. B. Wei et al.;
Learning to explain: prototype-based surrogate models for LLM classification;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2505.18970

36. K. Amarsinghe et al;
On the importance of Application-Grounded Experimental
Design for Evaluating Explainable ML Methods;
Proceedings of AAAI 2024.
https://ojs.aaai.org/index.php/AAAI/article/view/30082

37. V. Chen; Use-case grounded simulations for explanation evaluation; 
Proceedings of Neurips 2022.
https://arxiv.org/abs/2206.02256

38. S. Ahmed et al.;
Enhancing image comprehension: the impact of ai-generated explanations on
perception of synthetic and altered media;
Proceedings of AIES 2025.
https://ojs.aaai.org/index.php/AIES/article/view/36529/38667

39. R. Chen et al;
Making teams and influencing agents: effectively coordinating decision trees
for interpretable multi-agent reinforcement learning;
Proceedings of AIES 2025.
https://ojs.aaai.org/index.php/AIES/article/view/36571

40. M. Dhani et al.;
When Explainability meets Privacy;
Proceedings of AIES 2025.
https://ojs.aaai.org/index.php/AIES/article/view/36585/38723

41. P. Zehtabi et al;
Contrastive explanations of centralized multi-agent optimization solutions.
Proceedings of ICAPS 2024.
https://ojs.aaai.org/index.php/ICAPS/article/view/31530

42. Y. Yoshikawa et al.;
Explaining black-box model predictions via two-level nested feature
attributions with consistency property;
Proceedings of IJCAI 2025.
https://arxiv.org/abs/2405.14522

43. R. Zuo et al.;
Why the agent made that decision: contrastive explanation learning
for reinforcement learning;
Proceedings of IJCAI 2025.
https://dl.acm.org/doi/10.24963/ijcai.2025/74

44. M. Hee and R. Lee;
Demystifying hateful content: leveragng large multimodal models
for hateful meme detection with explainable decisions;
Proceedings of ICSWM 2025.
https://ojs.aaai.org/index.php/ICWSM/article/view/35845

45. G. Freeman et al.;
Argumentative Large Language Models for Explainable and Contestable
Claim Verification;
Proceedings of AAAI  2025.
https://arxiv.org/abs/2405.02079

Papers on Social Networks

1. P. Berenbrink et al.;
Asynchronous opinion dynamics in social networks;
Proceedings of AAMAS 2022.
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.
https://ifaamas.org/Proceedings/aamas2022/pdfs/p642.pdf

3. A. Abouzeid et al.;
Socially fair mitigation of misinformation on social networks via
constraint stochastic optimization;
Proceedings of AAAI 2022.
https://arxiv.org/abs/2203.12537

4. 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.
https://arxiv.org/pdf/2110.10655.pdf

5. C. Schweimer et al;
Generating simple directed social network graphs for
information spreading;
Proceedings of ACM The Web conference 2022.
https://arxiv.org/pdf/2205.02485.pdf

6. W. Xu et al;
Evidence-aware fake news detection with graph neural networks;
Proceedings of ACM the Web conference 2022.
https://arxiv.org/pdf/2201.06885.pdf

7. Z-Y Dou et al;
Harnessing social media to identify homeless youth at risk of
substance use;
Proceedings of AAAI 2021.
https://ojs.aaai.org/index.php/AAAI/article/view/17732

8. A. Tsang et al;
Group-fairness in influence maximization;
Proceedings of IJCAI 2019.
https://people.scs.carleton.ca/~alantsang/files/groupfair19.pdf
Proceedings of AAMAS 2020.

9. A. de Silva et al;
Adversarial community detection with graph convolutional neural networks:
bridging topological and attributive cohesion;
Proceedings of IJCAI 2025.
https://www.arxiv.org/abs/2505.10197

10. S. Aref and Z. Neal;
Detecting coalitions by optimally partitioning signed networks
of political collaboration;
Nature Scientific Reports 10, Article number 1506; 2020.
https://www.nature.com/articles/s41598-020-58471-z

11. R. Baten et al.;
Predicting future location categories of users in a large social platform;
Proceedings of ICSWM 2023.
https://ojs.aaai.org/index.php/ICWSM/article/view/22125/21904

12. X. Wang, O. Varol and T. Eliassi-Rad;
Information access equality on generative models of complex networks;
Applied Network Science 7, Article 54; 2022.
https://appliednetsci.springeropen.com/articles/10.1007/s41109-022-00494-8

13. 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.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583214

14. Y. Dong et al.;
A generalized deep Markov random fields framework for fake news detection;
Proceedings of IJCAI 2023.
https://www.ijcai.org/proceedings/2023/0529.pdf

15.  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.
https://dl.acm.org/doi/10.1145/3336191.3371825

16. H. Huang et al.; Multi-aspect diffusion network inference;
Proceedings of ACM TheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583228

17.  X. Wu et al;
ConsRec: learning consensus behind interactions for group recommendation;
Proceedings of ACM TheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583277

18.  C. Gao et al.;
Pairwise-interactions-based Bayesian inference of network structure
from information cascades;
Proceedings of ACM TheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583231

19. X. Lu et al.;
Continuous-time graph learning for cascade popularity prediction;
Proceedings of IJCAI 2023.
https://www.ijcai.org/proceedings/2023/0247.pdf

20. Z. Yu et al.;
Commonsense knowledge enhanced sentiment dependency grafor sarcasm detection;
Proceedings of IJCAI 2023.
https://www.ijcai.org/proceedings/2023/0269.pdf

21.  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.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583388

22.  Y. Wang et al.;
Label information enhanced fraud detection against low homophily in graphs;
Proceedings of ACMTheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583373

23. G. Zhang et al.;
An attentional multi-scale co-evolving model for dynamic link prediction;
Proceedings of ACMTheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583396

24. S. Diao et al.;
Hashtag-guided low-resource tweet classification;
Proceedings of ACM TheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583194

25.  Y. Luo et al.;
Improving (dis)agreement detection with inductive social relation
information from comment-reply interactions;
Proceedings of ACM TheWeb Conference 2023.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583314

26. K. Pelrine et al.;
Towards reliable misinformation mitigation: generalization,
uncertainty and GPT-4; Proceedings of EMNLP 2023.
https://aclanthology.org/2023.emnlp-main.395.pdf

27. H. Le et al.;
Reinforce Trustworthiness in Multimodal Emotional Support System;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2511.10011

28. S. Cu et al;
When Smiley turns hostile: interpreting how emojis trigger LLM's toxicity;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2509.11141

29. H. He et al; 
FACT2FICTION: Targeted Poisoning Attack to Agentic Fact-checking System;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2508.06059

30. X. You et al.;
Knowledge Completes the Vision: A Multimodal Entity-aware
Retrieval-Augmented Generation Framework for News Image Captioning;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2511.21002

31. X. Mao et al.;
MindVote: When AI Meets the Wild West of Social Media Opinion;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2505.14422

32. L. Tian and M-A. Rizoiu;
Estimating Online Influence Needs Causal Modeling!
Counterfactual Analysis of Social Media Engagement;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2505.19355

33. G. Fidone et al;
Evaluating Online Moderation Via LLM-Powered Counterfactual Simulations;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2511.07204

34. M. Richards et al.;
Toward Simulating Networked Societies with Formal Institutions Using AI Agents;
Proceedings of AAAI 2026.
https://students.cs.byu.edu/~crandall/papers/Richards_etal_AAAI2026.pdf

35. C. Han et al.;
Beyond Detection: Exploring Evidence-based Multi-Agent Debate for
Misinformation Intervention and Persuasion;
Proceedings of AAAI 2026.
https://arxiv.org/pdf/2511.07267

36. G. Ahnert et al.;
Extracting affect aggregates from longitudinal socal media data with
temporal adapters for large language models;
Proceedings of ICWSM 2025.
https://ojs.aaai.org/index.php/ICWSM/article/view/35801

37. A. Alsoubai et al.;
Timelilness matters: leveraging reinforcement learning on social media data
to prioritize high-risk conversations for promoting youth online safety;
Proceedings of ICWSM 2025.
https://ojs.aaai.org/index.php/ICWSM/article/view/35802

38. A. Amini et al;
News source credibility assessment: a Reddit case study;
Proceedings of ICWSM 2025.
https://ojs.aaai.org/index.php/ICWSM/article/view/35804

39. Y. Cetinkaya et al;
Cross-partisan interactions on Twitterl
Proceedings of ICWSM 2025.
https://ojs.aaai.org/index.php/ICWSM/article/view/35819

40. K. Garimella et al;
Global patterns of viral content on WhatsApp;
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