2016
DOI: 10.1371/journal.pone.0166051
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Leveraging Big Data for Exploring Occupational Diseases-Related Interest at the Level of Scientific Community, Media Coverage and Novel Data Streams: The Example of Silicosis as a Pilot Study

Abstract: ObjectiveSilicosis is an untreatable but preventable occupational disease, caused by exposure to silica. It can progressively evolve to lung impairment, respiratory failure and death, even after exposure has ceased. However, little is known about occupational diseases-related interest at the level of scientific community, media coverage and web behavior. This article aims at filling in this gap of knowledge, taking the silicosis as a case study.MethodsWe investigated silicosis-related web-activities using Goog… Show more

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Cited by 45 publications
(36 citation statements)
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“…The Web tools used for conducting infodemiology studies have some limitations. The first limitation of these Web tools, such as Google Trends or Twitter, is that they track only the segment of population that uses and surfs the Web and monitors their health information behaviour (Alicino et al., ; Bragazzi, Dini, Toletone, Brigo & Durando, ; Bragazzi, Barberis, et al., ; Bragazzi, Watad, et al., ; Nuti et al., ). However, as mentioned in other studies, the major limitation of these Web tools, especially Google Trends, is the lack of detailed information on the method used for the search and analysis of new big data streams ( Alicino et al., ; Bragazzi, Barberis, et al., ; Bragazzi et al., ; Nuti et al., ).…”
Section: Discussionmentioning
confidence: 99%
“…The Web tools used for conducting infodemiology studies have some limitations. The first limitation of these Web tools, such as Google Trends or Twitter, is that they track only the segment of population that uses and surfs the Web and monitors their health information behaviour (Alicino et al., ; Bragazzi, Dini, Toletone, Brigo & Durando, ; Bragazzi, Barberis, et al., ; Bragazzi, Watad, et al., ; Nuti et al., ). However, as mentioned in other studies, the major limitation of these Web tools, especially Google Trends, is the lack of detailed information on the method used for the search and analysis of new big data streams ( Alicino et al., ; Bragazzi, Barberis, et al., ; Bragazzi et al., ; Nuti et al., ).…”
Section: Discussionmentioning
confidence: 99%
“…There are various types of Big Data, based on their sources: (i) molecular Big Data (obtained by means of wet-lab techniques and OMICS-based approaches, such as genomics, and post-genomics specialties, including proteomics, and interactomics); (ii) imaging-based Big Data (like radiomics or the massive data-mining approach to extract clinically meaningful, high-dimensional information from images); (iii) sensor-based Big Data (wearable sensors); and (iv) digital and computational Big Data (with an incredible wealth of data produced by the internet, smart phones, and other mobile devices) [10][11][12][13].…”
Section: The Currently Ongoing Covid-19 Outbreakmentioning
confidence: 99%
“…The structured queries entered in Google Search can be systematically analyzed by Google Trends, a freely accessible tool that provides geospatial and temporal patterns in search volumes for particular terms, aiming to derive meaningful insights about population health behaviors . Previous studies showed a great potential of Internet data mining to correlate the activity of health information consumers with epidemiological data of communicable morbidities, to elucidate public attitudes of distinct people in dealing with their own oral health problems, and to detect the influence of disease‐based publications on the interests of Web health seekers . This approach contributes to traditional epidemiological methods with near real‐time supplementary data.…”
Section: Introductionmentioning
confidence: 99%
“…11 Previous studies showed a great potential of Internet data mining to correlate the activity of health information consumers with epidemiological data of communicable morbidities, 12 to elucidate public attitudes of distinct people in dealing with their own oral health problems, [13][14][15] and to detect the influence of disease-based publications on the interests of Web health seekers. 16 This approach contributes to traditional epidemiological methods with near real-time supplementary data.…”
Section: Introductionmentioning
confidence: 99%