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Since the COVID-19 pandemic was declared, the advertising industry has struggled to adapt to a new reality. The current study aims to explore the impact of emotional advertising during this pandemic on The Lebanese consumers' behavior. We are particularly interested in fast-food chains due to the contradiction between their unhealthy offerings and their empathetic marketing. A convenience sample of 270 respondents was analyzed. Findings show that positive attitudes towards emotional marketing create positive actions towards the brand, which are mediated by more positive emotions towards the brand – even in times of crises. However, there was no effect of perceived lack of authenticity on attitudes or actions. [ABSTRACT FROM AUTHOR]

University students are experiencing a mental health crisis across the world. COVID-19 has exacerbated this situation. We have conducted a survey among university students in two universities in Lebanon to gauge mental health challenges experienced by students. We constructed a machine learning approach to predict anxiety symptoms among the sample of 329 respondents based on student survey items including demographics and self-rated health. Five algorithms including logistic regression, multi-layer perceptron (MLP) neural network, support vector machine (SVM), random forest (RF) and XGBoost were used to predict anxiety. Multi-Layer Perceptron (MLP) provided the highest performing model AUC score (AUC=80.70%) and self-rated health was found to be the top ranked feature to predict anxiety. Future work will focus on using data augmentation approaches and extending to multi-class anxiety predictions. Multidisciplinary research is crucial in this emerging field. [ABSTRACT FROM AUTHOR]

Partisan media are often accused of reinforcing partisan views and contributing to political polarization. Drawing on the literature on selective exposure and media effects, we survey a representative sample of 784 Lebanese citizens who consume partisan media in a highly polarized context. In the absence of centrist nonpartisan channels in Lebanon, we differentiate between extreme and moderate partisan channels and test the prevalence of partisan selective exposure among the viewers. Results show that partisan viewers exhibit partisan selective exposure when selecting channels to watch the news but not in case of entertainment. Neutral viewers avoided extreme partisan channels and were indifferent to the political leaning of the moderate partisan channels. In this study, we present profiling models that predict the likelihood of viewers to seek/avoid extreme and moderate partisan channels based on their political affiliation, religion, age, socioeconomic status, and gender. [ABSTRACT FROM AUTHOR]

Media studies have investigated the ability of audience's sociodemographic variables to predict media preference and selective exposure. Yet the roles that religion and politics play in the Arab media scene has largely been overlooked in the literature, despite the importance of this market. Using stratified random sampling, we surveyed 784 Lebanese viewers who shared their level of interest in watching 8 different TV genres and subgenres. Through logistic regression models, we analyzed eight sociocultural variables and their ability to predict the audience's genre preference. The findings clearly showed that religion and political affiliation are strong predictors of genre preference in a diverse partisan context. Our exploratory empirical work provides critical contribution to media preference research and genre studies and provides insights to help media producers schedule the best mix of programs anchored in audience analysis. [ABSTRACT FROM AUTHOR]