The release of ChatGPT has uncovered a range of possibilities whereby large language
models (LLMs) can substitute human intelligence. In this paper, we seek to understand …

The mining of Tweets to develop datasets on recent issues, global challenges, pandemics,
virus outbreaks, emerging technologies, and trending matters has been of significant interest …

For the past seven years, the Social Media Mining for Health Applications (# SMM4H)
shared tasks have promoted the community-driven development and evaluation of …

Identifying users' stances regarding specific targets/topics is a significant route to learning
public opinion from social media platforms. Most existing studies of stance detection strive to …

Stance detection infers a text author's attitude towards a target. This is challenging when the
model lacks background knowledge about the target. Here, we show how background …

The development of social media user stance detection and bot detection methods rely
heavily on large-scale and high-quality benchmarks. However, in addition to low annotation …

Background In recent years, the COVID-19 pandemic has brought great changes to public
health, society, and the economy. Social media provide a platform for people to discuss …

Stance detection aims to detect the stance toward a corresponding target. Existing works
use the assumption that the target is known in advance, which is often not the case in the …

Research on automated social media rumour verification, the task of identifying the veracity
of questionable information circulating on social media, has yielded neural models …

The goal of zero-shot stance detection (ZSSD) is to identify the stance (in favor of, against, or
neutral) of a text towards an unseen target in the inference stage. In this paper, we explore …