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This study examines the impact of word count in Facebook posts on user engagement and emotional responses, focusing on sports shoe brands. It addresses a research gap concerning the effects of post length on various emotional aspects and engagement metrics within social media marketing. Data were collected from January 1, 2024, to January 30, 2024, covering posts from January 2013 to December 2023 on official Facebook pages of several sports shoe brands. After cleaning an initial 723 posts, 636 were analyzed using statistical methods and Python libraries. Eight emotional aspects-fear, anger, sadness, trust, anticipation, joy, disgust, and surprise-were examined in relation to post length. Mean reaction values and emotional aspects were compared before and during the COVID-19 period. A structured equation model (SEM) in AMOS assessed these relationships. The analysis revealed that longer posts positively influenced trust (β = 0.208, ρ < 0.001), anticipation (β = 0.095, ρ < 0.05), joy (β = 0.259, ρ < 0.001), and surprise (β = 0.072, ρ < 0.05), but did not significantly affect fear (β = 0.065, ρ = 0.089), sadness (β = 0.054, ρ = 0.155), or disgust (β = 0.072, ρ = 0.116). During COVID-19, increased trust and anticipation were noted, while sadness and anger decreased. This study highlights that longer Facebook posts can enhance engagement through increased trust, anticipation, joy, and surprise, offering valuable insights for optimizing social media content strategies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Rising global oil prices are a major challenge for an emerging oil-importing nation such as Bangladesh. The majority of prior research on the economic effects of an oil price shock has concentrated on developed countries, with emerging economies receiving comparatively less attention. Bangladesh is vulnerable to price shocks due to its rising oil consumption over the past decade. This study aims to investigate how changes in oil prices would affect Bangladesh's total export earnings and to forecast the overall export volume. This study utilized a nonlinear autoregressive distributed lag (NARDL) approach to account for the asymmetric behavior of oil prices from 1991 to 2021. To assess the accuracy of predictions, the study employed the Prophet forecasting model and the Long Short-Term Memory (LSTM) method. Additionally, the symmetry test revealed a nonlinear relationship between export volume and oil price but a linear relationship between inflation and export volume. According to the NARDL assessment, both positive and negative oil shocks increase export earnings over the long run. The short run summary clarifies that both positive and negative changes in oil prices exert a significant negative effect on exports. Also, Inflation influences export earnings negatively in the short run but positively over the long term. Moreover, using machine learning methods, it was found that the LSTM method outperforms the prophet model in prediction performance with a low root mean square error (RMSE) of 1.88. Also, the analysis revealed policymakers that the export sector requires diversification to reduce its exposure to oil price shocks., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)