A Stance Detection Approach Based on Generalized Autoregressive pretrained Language Model in Chinese Microblogs

Su, Zhizhong; Xi, Yaoyi; Cao, Rong; Tang, Huili; Pan, Hangyu

doi:10.1145/3457682.3457717

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both are large language models based on the Transformer architecture (Vaswani et al 2017), and both models regularly achieve very high accuracy on many prediction tasks. While there are studies employing XLNet (SU et al 2021;Yang et al 2019), previous research suggests that RoBERTa can achieve state-of-the-art results outperforming BERT and XLNet (Slovikovskaya 2019;Dulhanty et al 2019;Liu et al 2022;Barbieri et al 2020). Despite its strong performance, RoBERTa has a maximum input length when processing texts.…”

Section: Stance Detectionmentioning

confidence: 99%

Analyzing the Stance of Facebook Posts on Abortion Considering State-Level Health and Social Compositions

Aleksandric,

Anderson,

Dangal

et al. 2024

ICWSM

View full text Add to dashboard Cite

Abortion remains one of the most controversial topics, especially after overturning Roe v. Wade ruling in the United States. Previous literature showed that the illegality of abortion could have serious consequences, as women might seek unsafe pregnancy terminations leading to increased maternal mortality rates and negative effects on their reproductive health. Therefore, the stances of the abortion-related Facebook posts were analyzed at the state level in the United States from May 4 until June 30, 2022, right after the Supreme Court’s decision was disclosed. In more detail, a pre-trained Transformer architecture-based model was fine-tuned on a manually labeled training set to obtain a stance detection model suitable for the collected dataset. Afterward, we employed appropriate statistical tests to examine the relationships between public opinion regarding abortion, abortion legality, political leaning, and factors measuring the overall population’s health, health knowledge, and vulnerability per state. We found that infant mortality rate, political affiliation, abortion rates, and abortion legality are associated with stances toward abortion at the state level in the US. While aligned with existing literature, these findings indicate how public opinion, laws, and women’s and infants’ health are related, as well as how these relationships can be demonstrated by using social media data.

show abstract

Section: Stance Detectionmentioning

confidence: 99%

Analyzing the Stance of Facebook Posts on Abortion Considering State-Level Health and Social Compositions

Aleksandric,

Anderson,

Dangal

et al. 2024

ICWSM

View full text Add to dashboard Cite

show abstract

Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training

Hardalov

Arora

Nakov

et al. 2022

AAAI

View full text Add to dashboard Cite

The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts are often expressed in many different languages depending on the user and the platform, which can be a local news outlet, a social media platform, a news forum, etc. Most research on stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection. Moreover, non-English sources of labelled data are often scarce and present additional challenges. Recently, large multilingual language models have substantially improved the performance on many non-English tasks, especially such with a limited number of examples. This highlights the importance of model pre-training and its ability to learn from few examples. In this paper, we present the most comprehensive study of cross-lingual stance detection to date: we experiment with 15 diverse datasets in 12 languages from 6 language families, and with 6 low-resource evaluation settings each. For our experiments, we build on pattern-exploiting training (PET), proposing the addition of a novel label encoder to simplify the verbalisation procedure. We further propose sentiment-based generation of stance data for pre-training, which shows sizeable improvement of more than 6% F1 absolute in few-shot learning settings compared to several strong baselines.

show abstract

A Stance Detection Approach Based on Generalized Autoregressive pretrained Language Model in Chinese Microblogs

Cited by 2 publications

References 4 publications

Analyzing the Stance of Facebook Posts on Abortion Considering State-Level Health and Social Compositions

Analyzing the Stance of Facebook Posts on Abortion Considering State-Level Health and Social Compositions

Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training

Contact Info

Product

Resources

About