Using SNSs for early detection of disease outbreak in developing countries: evidence from COVID-19 pandemic in Nigeria

Abstract

Developing countries continually find it challenging to proactively and actively carry out early-stage surveillance for disease outbreaks due to the lack of quality workforce, a dearth of public health data, and the absence of automated surveillance systems in the country. This study presents the potential and ability of Twitter in tracking early detection of COVID-19, monitoring the dissemination of information, and exploration of public awareness and attitudes among Nigerians. Tweets mentioning COVID-19 and related keywords were collected in 11 batches via the NCapture™ plugin available on Google Chrome from February 20 - May 6, 2020. The analysis includes a time series analysis to track the distribution of data and content analysis to analyze the knowledge and attitudes of Nigerians. A total of 67,989 tweets (1,484 unique and 66,505 retweets) citing COVID-19 and related keywords were returned. The Tweets started to emerge earlier to the first confirmed case in Nigeria while maintaining a dangling-upward movement up to the 11th week under study. Matters arising from the tweets include a dearth of information on COVID-19 and optimism among others. The results provide insight into the intersection of SNSs and public health surveillance. Results show how helpful Twitter is to educate education in public health. Health organizations and the government may benefit from paying attention to both amusing and emotional contents from the Twitter community to formulate a viable policy for treatment and control.

Circular business strategy challenges and opportunities for Industry 4.0: A social media data‐driven analysis

“…Twitter information has appeared as one of the most extensively exploited data sources for academic research and practical applications (Zhan et al, 2021). Data collection was executed through NCapture, which facilitates the immediate and appropriate capture of webpage content and social media tweets (Adebisi et al, 2021). The occurrence frequency of indicators is estimated by searching the information posted on Twitter with appropriate hashtags.…”

Building a data‐driven circular supply chain hierarchical structure: Resource recovery implementation drives circular business strategy

“…Twitter data have formerly been utilized to monitor and analyze challenging issues in various fields (Zhan et al, 2021). The data collection was carried out using NCapture, an internet browser extension for NVivo software, and hand-operated codification, which allows the instant and convenient capture of webpage content and social media posts (Adebisi et al, 2021). The criteria frequency is counted by searching Twitter data using the appropriate search terms.…”

“…To fill the identified research gap, this study follows three objectives. Designate a set of attributes from big data Construct a hierarchical structure for CSC attributes and analyze the causal interrelationships among them Reveal the decisive criteria that express the opportunities and challenges of CSC practices This study contributes to the research by (Abd Aziz et al, 2021) identifying CSC attributes by exploiting the literature and social media sources; (Adebisi et al, 2021) defining the cause‐and‐effect structure of CSC aspects, thereby providing directions for future approaches; and (Ahirwar & Tripathi, 2021) presenting opportunities and challenges in the adoption of CSC practices by identifying decisive criteria.…”

“…This study contributes to the research by (Abd Aziz et al, 2021) identifying CSC attributes by exploiting the literature and social media sources; (Adebisi et al, 2021) defining the cause-and-effect structure of CSC aspects, thereby providing directions for future approaches;…”

Monkey pox: Rethinking COVID‐19 to project future strategies against emerging and evolving pathogens

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