Some interesting conferences you may explore for papers to present:

• ACL/HLT (Association of Computational Linguistics/Human Language Technologies
• AMIA (American Medical Informatics Association) and JAMIA
• ACM SIGIR (Special Interest Group in Information Retrieval)
• AIRS - Asian Information Retrieval
• ICML (International Conference on Machine Learning)
• ICWSM (AAAI Conference on Web and Social Media)
• ACM KDD (Knowledge Discovery and Data Mining)
• WWW (World Wide Web Conference)
• WSDM (ACM International Conference on Web Search and Data Mining)
• TREC Proceedings
• arXiV

Goal: To study current papers from journals and conference proceedings in text retrieval and text mining. Examples of problems include topic models, web retrieval and web mining, ranking strategies, ambiguity resolution, knowledge discovery, web phenomenon including social networks, information extraction and text classification. The reading group is led by Professor Padmini Srinivasan. Interested students (from beginning to advanced students) and faculty are invited to participate in the reading group. Participation format is informal with individuals taking turns to present an overview of the selected paper and lead the discussion. This forum has resulted in collaborative projects and published papers.

1. January 25: Padmini Srinivasan
• Del Corro, L., & Gemulla, R. (2013, May). Clausie: clause-based open information extraction. In Proceedings of the 22nd international conference on World Wide Web (pp. 355-366). ACM.
2. February 1: Shehroze Farooqi
• Yao, Y., Viswanath, B., Cryan, J., Zheng, H., & Zhao, B. Y. (2017, October). Automated crowdturfing attacks and defenses in online review systems. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1143-1158). ACM.
3. February 8: Jonathan Rusert
• An, J., & Weber, I. (2016). # greysanatomy vs.# yankees: Demographics and Hashtag Use on Twitter. arXiv preprint arXiv:1603.01973.
• Bergsma, S., Dredze, M., Van Durme, B., Wilson, T., & Yarowsky, D. (2013). Broadly improving user classification via communication-based name and location clustering on twitter. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1010-1019).
4. February 15: Huyen Le
• Thomas, K., Grier, C., Song, D., & Paxson, V. (2011, November). Suspended accounts in retrospect: an analysis of twitter spam. In Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference (pp. 243-258). ACM.
5. February 22: Momina Tabish
• Luo, Y., Cheng, Y., Uzuner, Ö., Szolovits, P., & Starren, J. (2017). Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes. Journal of the American Medical Informatics Association, 25(1), 93-98.
6. March 1: Dat Hong
• Cheng, Z., Caverlee, J., & Lee, K. (2010, October). You are where you tweet: a content-based approach to geo-locating twitter users. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 759-768). ACM.
7. March 8: Umar Iqbal
• Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., ... & Zhong, Y. (2018). VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. arXiv preprint arXiv:1801.01681.
8. March 15: SPRING BREAK
9. March 22: Osama Khalid
• Dumoulin, V., & Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285.
• Zhang, Ye, and Byron Wallace. "A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification." arXiv preprint arXiv:1510.03820(2015).
• Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
10. Mach 29: Momina Tabish
• Liu, J., Chang, W. C., Wu, Y., & Yang, Y. (2017, August). Deep learning for extreme multi-label text classification. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 115-124). ACM.
11. April 5: Huyen Le
• Kudugunta, S., & Ferrara, E. (2018). Deep Neural Networks for Bot Detection. arXiv preprint arXiv:1802.04289.
12. April 12: Shehroze Farooqi
• Nilizadeh, S., Labrèche, F., Sedighian, A., Zand, A., Fernandez, J., Kruegel, C., ... & Vigna, G. (2017, October). Poised: Spotting twitter spam off the beaten paths. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1159-1174). ACM.
13. April 19: Jonathan Rusert
• Segalin, C., Cheng, D. S., & Cristani, M. (2017). Social profiling through image understanding: Personality inference using convolutional neural networks. Computer Vision and Image Understanding, 156, 34-50.
14. April 26: John Cook
• Arras, L., Horn, F., Montavon, G., Müller, K. R., & Samek, W. (2017). " What is relevant in a text document?": An interpretable machine learning approach. PloS one, 12(8), e0181142.
15. May 3: Huyen Le
• Rizoiu, M. A., Graham, T., Zhang, R., Zhang, Y., Ackland, R., & Xie, L. (2018). # debatenight: The role and influence of socialbots on twitter during the 1st us presidential debate. arXiv preprint arXiv:1802.09808.