Effective communication during health crises is critical, with social media serving as a key platform for public health experts (PHEs) to engage with the public. However, it also amplifies pseudo-experts promoting contrarian views. Despite its importance, the role of emotional and moral language in PHEs' communication during COVID-19 remains under explored. This study examines how PHEs and pseudo-experts communicated on Twitter during the pandemic, focusing on emotional and moral language and their engagement with political elites. Analyzing tweets from 489 PHEs and 356 pseudo-experts from January 2020 to January 2021, alongside public responses, we identified key priorities and differences in messaging strategy. PHEs prioritize masking, healthcare, education, and vaccines, using positive emotional language like optimism. In contrast, pseudo-experts discuss therapeutics and lockdowns more frequently, employing negative emotions like pessimism and disgust. Negative emotional and moral language tends to drive engagement, but positive language from PHEs fosters positivity in public responses. PHEs exhibit liberal partisanship, expressing more positivity towards liberals and negativity towards conservative elites, while pseudo-experts show conservative partisanship. These findings shed light on the polarization of COVID-19 discourse and underscore the importance of strategic use of emotional and moral language by experts to mitigate polarization and enhance public trust.

This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of fairness queries for a predictive model on a target population. The framework employs background knowledge of the data collection process and biased data, working with or without limited statistics about the target population, to compute a range of answers for fairness queries. Using CRA, the framework builds predictive models that are certifiably fair on the target population, regardless of the availability of external data during training. The framework's efficacy is demonstrated through evaluations on real data, showing substantial improvement over existing state-of-the-art methods.

Emotion recognition is one of the machine learning applications which can be done using text, speech, or image data gathered from social media spaces. Detecting emotion can help us in different fields, including opinion mining. With the spread of social media, different platforms like Twitter have become data sources, and the language used in these platforms is informal, making the emotion detection task difficult. EmoPars and ArmanEmo are two new human-labeled emotion datasets for the Persian language. These datasets, especially EmoPars, are suffering from inequality between several samples between two classes. In this paper, we evaluate EmoPars and compare them with ArmanEmo. Throughout this analysis, we use data augmentation techniques, data re-sampling, and class-weights with Transformer-based Pretrained Language Models(PLMs) to handle the imbalance problem of these datasets. Moreover, feature selection is used to enhance the models' performance by emphasizing the text's specific features. In addition, we provide a new policy for selecting data from EmoPars, which selects the high-confidence samples; as a result, the model does not see samples that do not have specific emotion during training. Our model reaches a Macro-averaged F1-score of 0.81 and 0.76 on ArmanEmo and EmoPars, respectively, which are new state-of-the-art results in these benchmarks.