Open Vocabulary Extreme Classification Using Generative Models

Daniel, Simig,; Petroni, Filippo; Yanki, Pouya; Popat, Kashyap; Du, Christina; Riedel, Sebastian; Yazdani, Majid

doi:10.48550/arxiv.2205.05812

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A few prior works tried to understand these fine-grained reasons for vaccine hesitancy, mostly by manual analysis (Bonnevie et al 2020) Multi-label Classification: Multi-label classification is a long-studied problem, and several approaches have been applied in various sub-domains of social media analysis, such as emotion detection from tweets (Mukherjee et al 2021;Ameer et al 2023), disaster mitigation (Chowdhury et al 2020) and symptom detection (Jarynowski et al 2021). Looking out to general domains, entailment-based methods (Wang et al 2021) and generative models (Simig et al 2022) (which we apply in this work) have been applied to classification tasks. However, the key novelty in our proposed methods -incorporating label semantics into multilabel classification, has not been explored earlier in the domain of social media analysis.…”

Section: Related Workmentioning

confidence: 99%

Winds of Change: Impact of COVID-19 on Vaccine-Related Opinions of Twitter Users

Poddar

Mondal

Misra³

et al. 2022

ICWSM

View full text Add to dashboard Cite

Today, administering COVID-19 vaccines at a societal scale has been deemed as the most appropriate way to defend against the COVID-19 pandemic. This global vaccination drive naturally fueled a possibility of pro-vax and anti-vax users strongly expressing their supports and concerns regarding the vaccines in online social media platforms. Understanding this online discourse is crucial for policy makers. This understanding is likely to impact the success of vaccination drives and might even impact the final outcome of our fight against the pandemic. The goal of this work is to improve this understanding using the lens of Twitter-discourse data. We first develop a classifier to categorize users according to their vaccine-related stance with high precision (97%). Using this method we detect and investigate specific user-groups who posted about vaccines in pre-COVID and COVID times. We specifically identify distinct topics that these users talk about, and investigate how vaccine-related discourse has changed between pre-COVID times and COVID times. Finally, for the first time, we investigate the change of vaccine-related stances in Twitter users and shed light on potential reasons for such changes in stance.

show abstract

Section: Related Workmentioning

confidence: 99%

Winds of Change: Impact of COVID-19 on Vaccine-Related Opinions of Twitter Users

Poddar

Mondal

Misra³

et al. 2022

ICWSM

View full text Add to dashboard Cite

show abstract

“…Multi-label Classification: Multi-label classification is a long-studied problem, and several approaches have been applied in various sub-domains of social media analysis, such as emotion detection from tweets (Mukherjee et al 2021;Ameer et al 2023), disaster mitigation (Chowdhury et al 2020) and symptom detection (Jarynowski et al 2021). Looking out to general domains, entailment-based methods (Wang et al 2021) and generative models (Simig et al 2022) (which we apply in this work) have been applied to classification tasks. However, the key novelty in our proposed methods -incorporating label semantics into multilabel classification, has not been explored earlier in the domain of social media analysis.…”

Section: Related Workmentioning

confidence: 99%

How COVID-19 Has Impacted the Anti-vaccine Discourse: A Large-Scale Twitter Study Spanning Pre-COVID and Post-COVID Era

Poddar,

Mukherjee,

Khatuya

et al. 2024

ICWSM

View full text Add to dashboard Cite

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 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 classifing 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 them in post-COVID times.

show abstract

Open Vocabulary Extreme Classification Using Generative Models

Cited by 2 publications

References 0 publications

Winds of Change: Impact of COVID-19 on Vaccine-Related Opinions of Twitter Users

Winds of Change: Impact of COVID-19 on Vaccine-Related Opinions of Twitter Users

How COVID-19 Has Impacted the Anti-vaccine Discourse: A Large-Scale Twitter Study Spanning Pre-COVID and Post-COVID Era

Contact Info

Product

Resources

About