A Data-Driven Score Model to Assess Online News Articles in Event-Based Surveillance System
| Autor: | Alam, Syed Mehtab, Arsevska, Elena, Roche, Mathieu, Teisseire, Maguelonne |
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| Rok vydání: | 2022 |
| Zdroj: | Information Management and Big Data ISBN: 9783031044465 Computer and Information Science Information management and big data. Communications in Computer and Information Science book series (CCIS, volume 1577), Springer Communications in Computer and Information Science Communications in Computer and Information Science-Information Management and Big Data |
| ISSN: | 1865-0929 1865-0937 |
| DOI: | 10.1007/978-3-031-04447-2_18 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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