Things to Do in L.A. Tap to enable a layout that focuses on the article. California has a strict vaccine mandate. Will it survive the Trump administration? Dr. Neville Anderson, right, tries to distract Perry Roj, 4, while nurse Breanna Kirby gives her a DTaP polio vaccination. Her mom, Devin Homsey, holds her tight at Larchmont Pediatrics.

The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of social media discussions poses a continual challenge for these techniques due to the constant shifting of the focus. On the other hand, traditional unsupervised methods for extracting themes from public discourse, such as topic modeling, often reveal overarching patterns that might not capture specific nuances. Consequently, a significant portion of research into social media discourse still depends on labor-intensive manual coding techniques and a human-in-the-loop approach, which are both time-consuming and costly. In this work, we study the problem of discovering arguments associated with a specific theme. We propose a generic LLMs-in-the-Loop strategy that leverages the advanced capabilities of Large Language Models (LLMs) to extract latent arguments from social media messaging. To demonstrate our approach, we apply our framework to contentious topics. We use two publicly available datasets: (1) the climate campaigns dataset of 14k Facebook ads with 25 themes and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads with 14 themes. Additionally, we design a downstream task as stance prediction by leveraging talking points in climate debates. Furthermore, we analyze demographic targeting and the adaptation of messaging based on real-world events.

The COVID-19 pandemic has sparked numerous discussions on social media platforms, with users sharing their views on topics such as mask-wearing and vaccination. To facilitate the evaluation of neural models for stance detection and premise classification, we organized the Social Media Mining for Health (SMM4H) 2022 Shared Task 2. This competition utilized manually annotated posts on three COVID-19-related topics: school closures, stay-at-home orders, and wearing masks. In this paper, we extend the previous work and present newly collected data on vaccination from Twitter to assess the performance of models on a different topic. To enhance the accuracy and effectiveness of our evaluation, we employed various strategies to aggregate tweet texts with claims, including models with feature-level (early) fusion and dual-view architectures from SMM4H 2022 leaderboard. Our primary objective was to create a valuable dataset and perform an extensive experimental evaluation to support future research in argument mining in the health domain.

User-generated textual contents on the Internet are often noisy, erroneous, and not in correct forms in grammar. In fact, some online users choose to express their opinions online through carefully perturbed texts, especially in controversial topics (e.g., politics, vaccine mandate) or abusive contexts (e.g., cyberbullying, hate-speech). However, to the best of our knowledge, there is no framework that explores these online ``human-written" perturbations (as opposed to algorithm-generated perturbations). Therefore, we introduce an interactive system called CRYPTEXT. CRYPTEXT is a data-intensive application that provides the users with a database and several tools to extract and interact with human-written perturbations. Specifically, CRYPTEXT helps look up, perturb, and normalize (i.e., de-perturb) texts. CRYPTEXT also provides an interactive interface to monitor and analyze text perturbations online. A short demo video is available at: https://youtu.be/8WT3G8xjIoI

In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world. It provokes a surge of incomplete, biased, and incorrect information, also known as an infodemic. This global infodemic jeopardizes measures to control the pandemic by creating panic, vaccine hesitancy, and fragmented social response. Platforms like Facebook allow advertisers to adapt their messaging to target different demographics and help alleviate or exacerbate the infodemic problem depending on their content. In this paper, we propose a minimally supervised multi-task learning framework for understanding messaging on Facebook related to the COVID vaccine by identifying ad themes and moral foundations. Furthermore, we perform a more nuanced thematic analysis of messaging tactics of vaccine campaigns on social media so that policymakers can make better decisions on pandemic control.

Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the current COVID-19 pandemic, but also for future pathogen outbreaks. We examine a Twitter dataset containing 75 million English tweets discussing COVID-19 vaccination from March 2020 to March 2021. We train a stance detection algorithm using natural language processing (NLP) techniques to classify tweets as `anti-vax' or `pro-vax', and examine the main topics of discourse using topic modelling techniques. While pro-vax tweets (37 million) far outnumbered anti-vax tweets (10 million), a majority of tweets from both stances (63% anti-vax and 53% pro-vax tweets) came from dual-stance users who posted both pro- and anti-vax tweets during the observation period. Pro-vax tweets focused mostly on vaccine development, while anti-vax tweets covered a wide range of topics, some of which included genuine concerns, though there was a large dose of falsehoods. A number of topics were common to both stances, though pro- and anti-vax tweets discussed them from opposite viewpoints. Memes and jokes were amongst the most retweeted messages. Whereas concerns about polarisation and online prevalence of anti-vax discourse are unfounded, targeted countering of falsehoods is important.

The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and fine-grained entity level moral sentiment analysis. We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predictions even in the low-supervision settings.

The walkout was organized in response to a March 31 email to staff from Activision Blizzard executive Brian Bulatao announcing the company would no longer require employees to receive the vaccine before returning to in-person work. On April 1, following pushback from employees led by the ABK Workers Alliance, Bulatao clarified that despite the policy shift, leadership of the company's different major studios -- for example, Activision Publishing, Blizzard and King -- would be able to "determine the processes and policies that work best for their employees and locations based on local conditions and risk" despite the companywide policy change. Mike Ybarra, the president of Blizzard Entertainment, said that, for now, the vaccine mandate would remain in place at Blizzard offices, a decision that was echoed by several other ABK offices.

The Supreme Court has dealt a major blow to the Biden administration, ending a White House requirement that employees at large businesses get a vaccine or test regularly and wear a mask on the job. The court's conservative majority concluded the administration overstepped its authority by seeking to impose the Occupational Safety and Health Administration's vaccine-or-test rule on U.S. businesses with at least 100 employees. At the same time, the court is allowing the administration to proceed with a vaccine mandate for most health care workers in the U.S., resulting in a mixed decision for the administration. Reacting to the ruling, Psaki hailed the decision allowing mandates for health care workers as "good news," saying the administration will continue to enforce it. On the OSHA ruling, Psaki said the White House will "continue to call on businesses to immediately join those those who have already stepped up, including one third of Fortune 100 companies, to institute vaccination requirements to protect their workers, customers and communities."