This is the main website for all the information related to the SocialMediaIE project. This project was started as part of the PhD thesis of Shubhanshu Mishra and many parts were done in collaboration with his advisor Jana Diesner at the School of Information Sciences, University of Illinois at Urbana-Champaign
- SocialMediaIE tool - uses neural multi-task learning to perform multiple sequence tagging and classification tasks for social media data. Currently focused on English Twitter data
- TwitterNER - a publicly available wikipedia based tool for named entity recognition for Twitter data
- Hate Speech Identification in Indo-European Languages
- Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
- Tutorials on the SocialMediaIE tool - https://socialmediaie.github.io/tutorials/
- List of datasets used in the project
- MetaCorpus - A meta corpus of social media corpus
- Label Embedding Visualization Classification code, Tagging code
- Mishra, S., Prasad, S., & Mishra, S. (2020, May). Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020. In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (pp. 120-125). https://www.aclweb.org/anthology/2020.trac-1.19/
- Mishra, S. S., & Mishra, S. S. (2019). 3Idiots at HASOC 2019: Fine-tuning Transformer Neural Networks for Hate Speech Identification in Indo-European Languages. In Proceedings of the 11th annual meeting of the Forum for Information Retrieval Evaluation (pp. 208–213). Kolkata, India. Retrieved from http://ceur-ws.org/Vol-2517/T3-4.pdf
- Mishra, S. (2019). Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media - HT ’19 (pp. 283–284). New York, New York, USA: ACM Press. https://doi.org/10.1145/3342220.3344929
- Mishra, S. (2019). Information extraction from digital social trace data with applications to social media and scholarly communication data. PhD Dissertation, University of Illinois at Urbana-Champaign. https://shubhanshu.com/phd_thesis/
- Mishra, S., & Diesner, J. (2019). Capturing Signals of Enthusiasm and Support Towards Social Issues from Twitter. In Proceedings of the 5th International Workshop on Social Media World Sensors - SIdEWayS’19 (pp. 19–24). New York, New York, USA: ACM Press. https://doi.org/10.1145/3345645.3351104
- Mishra, S., Diesner, J., Byrne, J., & Surbeck, E. (2015). Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization. In Proceedings of the 26th ACM Conference on Hypertext & Social Media - HT ’15 (pp. 323–325). New York, New York, USA: ACM Press. https://doi.org/10.1145/2700171.2791022
- Mishra, S., & Diesner, J. (2016). Semi-supervised Named Entity Recognition in noisy-text. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT) (pp. 203–212). Osaka, Japan: The COLING 2016 Organizing Committee. Retrieved from https://aclweb.org/anthology/papers/W/W16/W16-3927/
- Mishra, S., & Diesner, J. (2018). Detecting the Correlation between Sentiment and User-level as well as Text-Level Meta-data from Benchmark Corpora. In Proceedings of the 29th on Hypertext and Social Media - HT ’18 (pp. 2–10). New York, New York, USA: ACM Press. https://doi.org/10.1145/3209542.3209562
- Mishra, S., Agarwal, S., Guo, J., Phelps, K., Picco, J., & Diesner, J. (2014). Enthusiasm and support: alternative sentiment classification for social movements on social media. In Proceedings of the 2014 ACM conference on Web science - WebSci ’14 (pp. 261–262). Bloomington, Indiana, USA: ACM Press. https://doi.org/10.1145/2615569.2615667
- Mishra, S., Agarwal, S., Guo, J., Phelps, K., & Picco, J. (2014). SENTINETS: User Classification Based on Sentiment for Social Causes within a Twitter Network. IDEALS UIUC. https://doi.org/http://hdl.handle.net/2142/49961
For details please contact @TheShubhanshu