Maria Schuld

The erosion of public trust in health information and government communication, particularly during crises like the Covid-19 pandemic, highlights a critical challenge in how health policies are transmitted and received. This study examines the dramatic shift in public sentiment toward Covid-19 vaccination in South Africa during 2021, a period that saw a decline from initial high acceptance to significant hesitancy. We argue that a process of social media selection allowed extreme views to proliferate as official sources retreated. Our findings suggest that sustained or increased mainstream media engagement, particularly from official sources like government and health authorities, could have mitigated the dominance of anti-vaccine narratives and have crucial implications for government communication and public health policies in the digital age. We collected and classified 482,450 original tweets about vaccination. We show that, by the end of 2021, Twitter activity was characterized by a progressive surge in anti-vaccination tweets and a decline in pro-vaccination and factual information, particularly from mainstream media. This shift mirrors the decrease in actual vaccination rates. Employing agent-based modeling, we simulated counterfactual scenarios to assess the impact of media presence on vaccine discourse. The results indicate that sustained or increased media engagement could have mitigated the dominance of anti-vaccine narratives. Conversely, a simulated media downturn led to a steeper decline in pro-vaccination content. The findings suggest that mainstream news media play a crucial role in shaping public perceptions of and support for health policies and that their disengagement creates an informational void exploited by misinformation.

Vaccine hesitancy is a public health concern in South Africa and internationally. Literature on vaccine hesitancy associates this with mistrust of the government. We present a qualitative analysis of opinions about COVID-19 vaccination expressed by South African Twitter (now X) users during the first year of the vaccine rollout in South Africa. We conducted a thematic analysis of 800 randomly selected tweets containing vaccine-related keywords, sampled from four time periods in 2021. We categorised comprehensible South African non-news tweets as pro-vaccination (24.75% of sample), anti-vaccination (20.25%) or ambivalent (4.5%), and then identified themes. Among pro-vaccination tweets, the most common themes were criticism of the government’s handling of vaccine procurement and the rollout; concerns that the vaccine was urgently needed and/or not being made available fast enough; and statements that vaccines were safe and/or effective against COVID-19. Among anti-vaccination tweets, the most common themes were claims that the vaccine was harmful or too risky; suspicion of the government’s intentions with respect to the vaccine it was offering the public; and opposition to mandatory or ‘forced’ vaccination. Criticism and mistrust of the government were present among both pro- and anti-vaccination tweets, though for different reasons. We discuss this in light of literature recommending trust-building as a response to vaccine hesitancy.

This paper explores how individuals’ language use in gender-specific groups (“mothers” and “fathers”) compares to their interactions when referred to as “parents.” Language adaptation based on the audience is well-documented, yet large-scale studies of naturally-occurring audience effects are rare. To address this, we investigate audience and gender effects in the context of parenting, where gender plays a significant role. We focus on interactions within Reddit, particularly in the parenting Subreddits r/Daddit, r/Mommit, and r/Parenting, which cater to distinct audiences. By analyzing user posts using word embeddings, we measure similarities between user-tokens and word-tokens, also considering differences among high and low self-monitors. Results reveal that in mixed-gender contexts, mothers and fathers exhibit similar behavior in discussing a wide range of topics, while fathers emphasize more on educational and family advice. Single-gender Subreddits see more focused discussions. Mothers in r/Mommit discuss medical care, sleep, potty training, and food, distinguishing themselves. In terms of individual differences, we found that, especially on r/Parenting, high self-monitors tend to conform more to the norms of the Subreddit by discussing more of the topics associated with the Subreddit.

We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant examples across quantum computing (gradient computation, circuit compilation, and error mitigation), and implement them using the transform framework of PennyLane, a software library for differentiable quantum programming. In this framework, the transforms themselves are differentiable and can be parametrized and optimized, which opens up the possibility of improved quantum resource requirements across a spectrum of tasks.

Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: Today's machine learning algorithms are notoriously powerful in practice, but remain theoretically difficult to study. Quantum computing, in contrast, does not offer practical benchmarks on realistic scales, and theory is the main tool we have to judge whether it could become relevant for a problem. In this perspective we explain why it is so difficult to say something about the practical power of quantum computers for machine learning with the tools we are currently using. We argue that these challenges call for a critical debate on whether quantum advantage and the narrative of 'beating' classical machine learning should continue to dominate the literature the way it does, and highlight examples for how other perspectives in existing research provide an important alternative to the focus on advantage.

This article presents results from a study that developed and tested a word embedding trained on a dataset of South African news articles. A word embedding is an algorithm-generated word representation that can be used to analyse the corpus of words that the embedding is trained on. The embedding on which this article is based was generated using the Word2Vec algorithm, which was trained on a dataset of 1.3 million African news articles published between January 2018 and March 2021, containing a vocabulary of approximately 124,000 unique words. The efficacy of this Word2Vec South African news embedding was then tested, and compared to the efficacy provided by the globally used GloVe algorithm. The testing of the local Word2Vec embedding showed that it performed well, with similar efficacy to that provided by GloVe. The South African news word embedding generated by this study is freely available for public use.

Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: the machine-learning algorithms of today are notoriously powerful in practice but remain theoretically difficult to study. Quantum computing, in contrast, does not offer practical benchmarks on realistic scales and theory is the main tool we have to judge whether it could become relevant for a problem. In this perspective, we explain why it is so difficult to say something about the practical power of quantum computers for machine learning with the tools we are currently using. We argue that these challenges call for a critical debate on whether quantum advantage and that the narrative of “beating” classical machine learning should continue to dominate the literature in the way it does, and highlight examples for how other perspectives in existing research provide an important alternative to the focus on advantage.

Machine learning has been used in high energy physics (HEP) for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to HEP. This paper reviews the first generation of ideas that use quantum machine learning on problems in HEP and provide an outlook on future applications.