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

ABSTRACT

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.

KEYWORDS:

• Resilience
• coping
• sentiment analysis
• opinion mining
• machine learning

Acknowledgement

The publication of this article is funded by the Qatar National Library.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author, M.E. The data are not publicly available due to the enormity of the data. Specifically, we used data that we collected from Twitter over 9 months of tweets originated from Qatar which amounted to around 450,000 tweets out of which approximately 160,000 were original and 290,000 were either retweets.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 www.internetlivestats.com/twitter-statistics/

2 Macro F1 score of 0.475 compared to the winner’s score of 0.461, i.e. better than the winner on this score, micro F1 score of 0.608 compared to the winner’s 0.618: micro F is often preferred because it provides a better reflection of a classifiers ability to deal with minority classes.

Additional information

Funding

This publication was made possible by the NPRP award [NPRP 7-1334-6-039 PR3] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the author[s].

Notes on contributors

Mazen El-Masri

Mazen El-Masri is an Associate Professor of Information Systems at the department of Accounting and Information Systems of the college of Business and Economics at Qatar University. His current research focuses on bigdata analytics, machine learning, social media, blockchain, fintech, e-banking, and design. He has published in journals like IT and People, Communications of the Association for Information Systems, Journal of Enterprise Information Management, Educational Technology Research and Development among others.

Allan Ramsay

Allan Ramsay is Emeritus Professor of Formal Linguistics at the University of Manchester, having previously held academic posts as Professor of Artificial Intelligence at University College Dublin and as a lecturer in computer science at the University of Sussex and the University of Essex. He holds a PhD in Artificial Intelligence from the University of Sussex and is the author of over 140 books and papers.

Hanady Mansour Ahmed

Hanady Mansour Ahmed is a Lecturer at faculty of Arts Linguistics and phonetics department Alexandria university Egypt. Her research focuses on Arabic NLP, Arabic morphology and phonology. She has publications in journals like Speech language and computer, and Artificial Intelligence.

Tariq Ahmad

Tariq Ahmad obtained his PhD in Computer Science from Manchester University, England, and currently works as a Senior Developer. His research interests centred around emotion analysis of tweets in an interpretable way and without relying on black boxes, large datasets and external resources. The approach was evaluated in a worldwide competition and performed exceptionally well. His current focus is on exploring the use of combining Computer Vision and Machine Learning in commercial settings.