Current - Issue

1. Kleinberg, J. Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373–397.
2. Blei, D. M., Ng, A. Y., & Jordan, M. I. Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
3. Lundberg, S. M., & Lee, S. I. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems,
30, 4765–4774.
4. Ribeiro, M. T., Singh, S., & Guestrin, C. 'Why should I trust you?': Explaining the predictions of any classifier. Proceedings of the 22nd
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
5. Chen, T., & Guestrin, C. XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, 785–794.
6. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. LightGBM: A highly efficient gradient boosting decision
tree. Advances in Neural Information Processing Systems, 30, 3146–3154.
7. Hochreiter, S., & Schmidhuber, J. Long short-term memory. Neural Computation, 9(8), 1735–1780.
8. Taylor, S. J., & Letham, B. Forecasting at scale. The American Statistician, 72(1), 37–45.
9. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. Attention is all you need. Advances
in Neural Information Processing Systems, 30, 5998–6008.
10. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding.
Proceedings of NAACL-HLT, 4171–4186.
11. Reimers, N., & Gurevych, I. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of the 2019 Conference
on Empirical Methods in Natural Language Processing, 3982–3992.
12. Campello, R. J. G. B., Moulavi, D., & Sander, J. Density-based clustering based on hierarchical density estimates. Pacific-Asia Conference
on Knowledge Discovery and Data Mining, 160–172.
13. Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E. P., Yan, H., & Li, X. Comparing Twitter and traditional media using topic models. Advances
in Information Retrieval, 338–349.
14. Vosoughi, S., Roy, D., & Aral, S. The spread of true and false news online. Science, 359(6380), 1146–1151.
15. Kwak, H., Lee, C., Park, H., & Moon, S. What is Twitter, a social network or a news media? Proceedings of the 19th International
Conference on World Wide Web, 591–600.
16. Zhou, X., & Zafarani, R. A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys,
53(5), 1–40.
17. Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. Fake news detection on social media: A data mining perspective. ACM SIGKDD
Explorations Newsletter, 19(1), 22–36.
18. Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. Detection and resolution of rumours in social media: A survey. ACM
Computing Surveys, 51(2), 1–36.
19. Castillo, C., Mendoza, M., & Poblete, B. Information credibility on Twitter. Proceedings of the 20th International Conference on World
Wide Web, 675–684.
20. Myers, S. A., Zhu, C., & Leskovec, J. Information diffusion and external influence in networks. Proceedings of the 18th ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining, 33–41.
21. Cheng, J., Adamic, L., Dow, P. A., Kleinberg, J. M., & Leskovec, J. Can cascades be predicted? Proceedings of the 23rd International
Conference on World Wide Web, 925–936.
22. Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. Everyone's an influencer: Quantifying influence on Twitter. Proceedings of the
4th ACM International Conference on Web Search and Data Mining, 65–74.
23. Goel, S., Watts, D. J., & Goldstein, D. G. The structure of online diffusion networks. Proceedings of the 13th ACM Conference on Electronic
Commerce, 623–638.
24. Yang, J., & Leskovec, J. Patterns of temporal variation in online media. Proceedings of the 4th ACM International Conference on WebSearch and Data Mining, 177–186.
25. Mathioudakis, M., & Koudas, N. TwitterMonitor: Trend detection over the Twitter stream. Proceedings of the 2010 ACM SIGMOD
International Conference on Management of Data, 1155–1158.
26. Petrović, S., Osborne, M., & Lavrenko, V. Streaming first story detection with application to Twitter. Proceedings of NAACL-HLT, 181–
189.
27. Naaman, M., Becker, H., & Gravano, L. Hip and trendy: Characterizing emerging trends on Twitter. Journal of the American Society for
Information Science and Technology, 62(5), 902–918.
28. Doshi-Velez, F., & Kim, B. Towards a rigorous science of interpretable machine learning. arXiv preprint, Harvard University Working
Paper.
29. Molnar, C. Interpretable machine learning: A guide for making black box models explainable. Leanpub, 2nd edition
30. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature
Machine Intelligence, 1(5), 206–215.