@misc{https://doi.org/10.48550/arxiv.2011.03983,<br/><span class="tab"></span>
  doi = {10.48550/ARXIV.2011.03983},<br/><span class="tab"></span>
  url = {https://arxiv.org/abs/2011.03983},<br/><span class="tab"></span>
  author = {Santosh,<br/><span class="tab"></span> Roshan and Schwartz,<br/><span class="tab"></span> H. Andrew and Eichstaedt,<br/><span class="tab"></span> Johannes C. and Ungar,<br/><span class="tab"></span> Lyle H. and Guntuku,<br/><span class="tab"></span> Sharath C.},<br/><span class="tab"></span>
  keywords = {Computation and Language (cs.CL),<br/><span class="tab"></span> Human-Computer Interaction (cs.HC),<br/><span class="tab"></span> Social and Information Networks (cs.SI),<br/><span class="tab"></span> FOS: Computer and information sciences,<br/><span class="tab"></span> FOS: Computer and information sciences},<br/><span class="tab"></span>
  title = {Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings},<br/><span class="tab"></span>
  publisher = {arXiv},<br/><span class="tab"></span>
  year = {2020},<br/><span class="tab"></span>
  copyright = {Creative Commons Attribution 4.0 International}
}