Avinash Tulasi


An Exploratory Study on Temporally Evolving Discussion around Covid-19 using Diachronic Word Embeddings
Avinash Tulasi | Asanobu Kitamoto | Ponnurangam Kumaraguru | Arun Balaji Buduru
Proceedings of the Workshop on Natural Language Processing for Digital Humanities

Covid 19 has seen the world go into a lock down and unconventional social situations throughout. During this time, the world saw a surge in information sharing around the pandemic and the topics shared in the time were diverse. People’s sentiments have changed during this period. Given the wide spread usage of Online Social Networks (OSN) and support groups, the user sentiment is well reflected in online discussions. In this work, we aim to show the topics under discussion, evolution of discussions, change in user sentiment during the pandemic. Alongside which, we also demonstrate the possibility of exploratory analysis to find pressing topics, change in perception towards the topics and ways to use the knowledge extracted from online discussions. For our work we employ Diachronic Word embeddings which capture the change in word usage over time. With the help of analysis from temporal word usages, we show the change in people’s option on covid-19 from being a conspiracy, to the post-covid topics that surround vaccination.