2 research outputs found
How COVID-19 has Impacted the Anti-Vaccine Discourse: A Large-Scale Twitter Study Spanning Pre-COVID and Post-COVID Era
The debate around vaccines has been going on for decades, but the COVID-19
pandemic showed how crucial it is to understand and mitigate anti-vaccine
sentiments. While the pandemic may be over, it is still important to understand
how the pandemic affected the anti-vaccine discourse, and whether the arguments
against non-COVID vaccines (e.g., Flu, MMR, IPV, HPV vaccines) have also
changed due to the pandemic. This study attempts to answer these questions
through a large-scale study of anti-vaccine posts on Twitter. Almost all prior
works that utilized social media to understand anti-vaccine opinions considered
only the three broad stances of Anti-Vax, Pro-Vax, and Neutral. There has not
been any effort to identify the specific reasons/concerns behind the anti-vax
sentiments (e.g., side-effects, conspiracy theories, political reasons) on
social media at scale. In this work, we propose two novel methods for
classifying tweets into 11 different anti-vax concerns -- a discriminative
approach (entailment-based) and a generative approach (based on instruction
tuning of LLMs) -- which outperform several strong baselines. We then apply
this classifier on anti-vaccine tweets posted over a 5-year period (Jan 2018 -
Jan 2023) to understand how the COVID-19 pandemic has impacted the anti-vaccine
concerns among the masses. We find that the pandemic has made the anti-vaccine
discourse far more complex than in the pre-COVID times, and increased the
variety of concerns being voiced. Alarmingly, we find that concerns about COVID
vaccines are now being projected onto the non-COVID vaccines, thus making more
people hesitant in taking vaccines in the post-COVID era.Comment: This work has been accepted to appear at the 18th International AAAI
Conference on Web and Social Media (ICWSM), 202
Financial Numeric Extreme Labelling: A Dataset and Benchmarking for XBRL Tagging
The U.S. Securities and Exchange Commission (SEC) mandates all public
companies to file periodic financial statements that should contain numerals
annotated with a particular label from a taxonomy. In this paper, we formulate
the task of automating the assignment of a label to a particular numeral span
in a sentence from an extremely large label set. Towards this task, we release
a dataset, Financial Numeric Extreme Labelling (FNXL), annotated with 2,794
labels. We benchmark the performance of the FNXL dataset by formulating the
task as (a) a sequence labelling problem and (b) a pipeline with span
extraction followed by Extreme Classification. Although the two approaches
perform comparably, the pipeline solution provides a slight edge for the least
frequent labels.Comment: Accepted to ACL'23 Findings Pape