RESUMEN
BACKGROUND: Philip Morris International (PMI) claims to be transforming and has committed to a 'smoke-free' future. In 2020, it announced an 'aspirational' target for reduced cigarette shipments by 2025. METHODS: PMI cigarette shipment data are taken from PMI quarterly financial reports 2008-2023. Trends in these data before and after the 2020 announcement are analysed using linear regression, and auto regressive integrated moving average and error, trend, seasonal time-series models to assess if PMI's 2025 target would be met on pre-existing trends, and if the trend changed after the announcement. These trends are also compared with the global retail market for cigarettes, using sales data from Euromonitor. RESULTS: Findings were consistent across all three models. PMI's shipment target of 550 billion cigarette sticks by 2025 would readily have been met given pre-existing shipment trends. Following the 2020 announcement, the decline in PMI cigarette shipments stalled markedly with a statistically significant change in trend (p<0.001). The current and projected trend to 2025 is consistent with no further decline in cigarette volumes, meaning PMI is unlikely to hit its target. This mirrors a global pattern in which declines in cigarette sales have stalled since 2020. CONCLUSIONS: PMI's 2025 target was not 'aspirational' but highly conservative-it would have been met based on pre-existing trends in declining cigarette shipments. Yet PMI will nonetheless fail to meet that target providing evidence it is not transforming. Stalling of the decline of PMI and global cigarette sales raises significant concerns about progress in global tobacco control.
RESUMEN
Introduction: Sentiment analysis has become a crucial area of research in natural language processing in recent years. The study aims to compare the performance of various sentiment analysis techniques, including lexicon-based, machine learning, Bi-LSTM, BERT, and GPT-3 approaches, using two commonly used datasets, IMDB reviews and Sentiment140. The objective is to identify the best-performing technique for an exemplar dataset, tweets associated with the WHO Framework Convention on Tobacco Control Ninth Conference of the Parties in 2021 (COP9). Methods: A two-stage evaluation was conducted. In the first stage, various techniques were compared on standard sentiment analysis datasets using standard evaluation metrics such as accuracy, F1-score, and precision. In the second stage, the best-performing techniques from the first stage were applied to partially annotated COP9 conference-related tweets. Results: In the first stage, BERT achieved the highest F1-scores (0.9380 for IMDB and 0.8114 for Sentiment 140), followed by GPT-3 (0.9119 and 0.7913) and Bi-LSTM (0.8971 and 0.7778). In the second stage, GPT-3 performed the best for sentiment analysis on partially annotated COP9 conference-related tweets, with an F1-score of 0.8812. Discussion: The study demonstrates the effectiveness of pre-trained models like BERT and GPT-3 for sentiment analysis tasks, outperforming traditional techniques on standard datasets. Moreover, the better performance of GPT-3 on the partially annotated COP9 tweets highlights its ability to generalize well to domain-specific data with limited annotations. This provides researchers and practitioners with a viable option of using pre-trained models for sentiment analysis in scenarios with limited or no annotated data across different domains.
RESUMEN
The prediction of tweets associated with specific topics offers the potential to automatically focus on and understand online discussions surrounding these issues. This paper introduces a comprehensive approach that centers on the topic of "harm reduction" within the broader context of tobacco control. The study leveraged tweets from the period surrounding the ninth Conference of the Parties to review the Framework Convention on Tobacco Control (COP9) as a case study to pilot this approach. By using Latent Dirichlet Allocation (LDA)-based topic modeling, the study successfully categorized tweets related to harm reduction. Subsequently, various machine learning techniques were employed to predict these topics, achieving a prediction accuracy of 91.87% using the Random Forest algorithm. Additionally, the study explored correlations between retweets and sentiment scores. It also conducted a toxicity analysis to understand the extent to which online conversations lacked neutrality. Understanding the topics, sentiment, and toxicity of Twitter data is crucial for identifying public opinion and its formation. By specifically focusing on the topic of "harm reduction" in tweets related to COP9, the findings offer valuable insights into online discussions surrounding tobacco control. This understanding can aid policymakers in effectively informing the public and garnering public support, ultimately contributing to the successful implementation of tobacco control policies.