Shubhanshu Mishra

Doctoral Student

Integrated Master and Bachelor of Science, Mathematics and Computing, Indian Institute of Technology, Kharagpur, 2012

Research focus

I conduct research on extracting and analyzing information from temporally changing social data. I have applied human-in-the-loop as well as semi-supervised machine learning techniques to extract information from social media datasets. I have also worked on quantifying conceptual novelty and expertise in biomedical articles. I have also worked on studying the effects of gender, ethnicity, and author age on self-citation rates in biomedical articles. Most of my research is available in form of open source tools, and open datasets.

Honors and Awards

Best Student Paper Award (Workshop on Informetric and Scienometric Research), 2018
Fellow of Kishor Vaigyanik Protsahan Yojana, 2007-2012

Publications & Papers

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.

Mishra, S., Fegley, B. D., Diesner, J., & Torvik, V. I. (2018). Self-citation is the hallmark of productive authors, of any gender. PLOS ONE, 13(9), e0195773.

Collier, D. A., Mishra, S., Houston, D. A., Hensley, B. O., & Hartlep, N. D. (2017). Americans ‘support’ the idea of tuition-free college: an exploration of sentiment and political identity signals otherwise. Journal of Further and Higher Education, 1–16.

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

Mishra, S., & Torvik, V. I. (2016). Quantifying Conceptual Novelty in the Biomedical Literature. D-Lib Magazine, 22(9/10).

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 (pp. 323–325).

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.


2018 - Expertise as an aspect of author contributions, Workshop on Informetric and Scientometric Research, Vancouver, Canada