A Data-Driven Score Model to Assess Online News Articles in Event-Based Surveillance System - Archive ouverte HAL Access content directly
Book Sections Year : 2022

A Data-Driven Score Model to Assess Online News Articles in Event-Based Surveillance System

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Abstract

Online news sources are popular resources for learning about current health situations and developing event-based surveillance (EBS) systems. However, having access to diverse information originating from multiple sources can misinform stakeholders, eventually leading to false health risks. The existing literature contains several techniques for performing data quality evaluation to minimize the effects of misleading information. However, these methods only rely on the extraction of spatiotemporal information for representing health events. To address this research gap, a score-based technique is proposed to quantify the data quality of online news articles through three assessment measures: 1) news article metadata, 2) content analysis, and 3) epidemiological entity extraction with NLP to weight the contextual information. The results are calculated using classification metrics with two evaluation approaches: 1) a strict approach and 2) a flexible approach. The obtained results show significant enhancement in the data quality by filtering irrelevant news, which can potentially reduce false alert generation in EBS systems.
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Dates and versions

hal-03745869 , version 1 (04-08-2022)

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Syed Mehtab Alam, Elena Arsevska, Mathieu Roche, Maguelonne Teisseire. A Data-Driven Score Model to Assess Online News Articles in Event-Based Surveillance System. Information Management and Big Data, 1577, Springer International Publishing, pp.264-280, 2022, Communications in Computer and Information Science, ⟨10.1007/978-3-031-04447-2_18⟩. ⟨hal-03745869⟩
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