APPLYING DEEP LEARNING METHODS FOR SHORT TEXT ANALYSIS
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APPLYING DEEP LEARNING METHODS FOR SHORT TEXT ANALYSIS
Chapter One: Background to the Study
1.1 Introduction.
The current surge of data has ushered in a new era for human society. Social media platforms such as Facebook, LinkedIn, and Twitter allow people to share information in real time; in 2016, Africa had 120 million Facebook users per month, and statistics for other social media platforms show similar growth (Fuseware & World Wide Worx, 2014; Parke, n.d.).
The growing use of social media in Africa cannot be overstated, as can its potential in meaningful projects such as event monitoring, perception evaluation, information extraction and retrieval, and thus its recommendation in disease prevention measures.
Despite the success of social media approaches in politics and business, as well as their potential in epidemiology as timely, collaborative, and population-centric, extensive analysis is required, as information is prone to misinterpretation when machines process natural language. As a result, social media analytics need deep learning approaches.
1.2 Background of the Study
Prior to the 15th century, Africa was plagued by pandemic illness outbreaks, which slowed both human population growth and development aspirations (Spinage & House, 2012).
The study of these cases reveals a pattern of illness epidemics recurring in previously affected nations and migration to nearby countries, which may be attributable to regional ecological shifts (Kebede, Duales, Yokouide, & Alemu, 2010; Spinage & House, 2012).
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The recurring frequency and the related mortality rate raises questions about the level of readiness, surveillance efficacy and control efforts in place; hence, over the years, numerous ways have been studied and fused with existing methods to minimise disease spread.
1.2.i A historical overview of infectious disease outbreaks in Africa
According to records, cholera, meningitis, influenza, yellow fever, rickettsia, smallpox, HIV/AIDS, Lassa fever, and Ebola are all common viral infections in Africa.
The total mortality rate is in the millions, with Ebola and HIV/AIDS accounting for more than 3 million documented deaths (Spinage and House, 2012).
Table 1 displays a sample number of disease outbreak cases in Africa, together with the predicted casualty scores.
Table 1: History of disease outbreaks in Africa (13).
1.2.ii Disease control.
Walter R. Dowdle defined disease control as ‘the decrease of disease incidence, prevalence, morbidity, or mortality to a locally acceptable level as a result of deliberate efforts; continuous interventions are required to maintain the reduction’ (Dowdle, 1998).
Disease control measures aim to minimise the contact rate of transmission, maintain the infectious population low, shorten the infection period of the common disease, and achieve a disease-free equilibrium (DFE) in the population (Brauer and Castillo-Chavez, 2014).
Disease control includes prevention activities for disease surveillance, readiness, and fast reaction.
• Eradication activities involving the isolation, treatment, and rehabilitation of contagious individuals.
A solid disease control strategy includes both prevention and eradication, albeit they often overlap and can be carried out in different sequences within the intervention cycle.
A number of organisations collaborate with the World Health Organisation (WHO), the Centre for Disease Control (CDC), and other countries’ health ministries to combat disease.
These organisations have been involved in epidemic preparedness and response (EPR) and integrated disease surveillance response (IDSR) efforts.
Adapting established methods for data gathering, illness identification, outbreak occurrences and projections, casualty estimation, and other disease outbreak metrics.
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