Positive sentiments as coping mechanisms and path to resilience: the case of Qatar blockade

• Published in: Information, communication & society Vol. 24; no. 13; pp. 1835 - 1853
• Main Authors: El-Masri, Mazen, Ramsay, Allan, Ahmed, Hanady Mansour, Ahmad, Tariq
• Format: Journal Article
• Language: English
• Published: Abingdon Routledge 03.10.2021; Taylor & Francis Ltd
• Subjects: Algorithms; Anger; Conditional probability; Coping; Embargoes & blockades; Emotions; Long term; Machine learning; Negative emotions; Neural networks; opinion mining; Optimism; Positive emotions; Resilience; sentiment analysis; Support vector machines; Qatar
• ISSN: 1369-118X; 1468-4462; 1468-4462
• DOI: 10.1080/1369118X.2020.1748086

Existing research on coping accentuates the role of positive emotions as defensive mechanisms to cope with stressful situations and the ensuing negative emotions. The same literature justifies the long-term effects of positive emotions that help build lasting resilience. Grounded in theories of coping and resilience, this paper (1) identifies the emotions that people actuate to cope with adversaries and (2) evaluates the resulting long-lasting adaptation and resilience. To do this, we examined the emotions felt by Qatar residents due to a land, sea, and air blockade enforced by neighbouring counties. Accordingly, we analysed 160,000 Arabic tweets originating from Qatar between June-2017 and March-2018 using a novel machine-learning algorithm termed Weighted Conditional Probability. Our algorithm achieved state-of-the-art performance when compared with the often-used Support Vector Machine, Naïve Bayes and Deep Neural Nets algorithms. Results show that, while Qatar residents experienced an emotional roller coaster during the blockade, they used positive emotions like love and optimism to cope with adversities and accompanying emotions of fear and anger. Moreover, our analysis reveals that their adaptive resilient capacities gradually strengthened during the nine months of blockade. The study supports the renowned theory of positive emotions using an advanced methodology and a large-scale dataset.