Evaluating the Implementation of a Twitter-Based Foodborne Illness Reporting Tool in the City of St. Louis Department of Health

Harris, Jenine K.; Hinyard, Leslie; Beatty, Kate E.; Hawkins, Joseph E.; Nsoesie, Elaine O.; Mansour, Raed; Brownstein, John S.

doi:10.3390/ijerph15050833

Cited by 27 publications

(64 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Metadata of social media include geo-location information, user profiles, hashtags, creation date and time, etc. Harris et al propose a Twitter-based food-borne illness reporting tool for the city of St. Louis [12]. Since they are only interested in the events in St. Louis, they set a boundary for tweet collection at a 50-mile radius around St. Louis using geographic coordinates as parameters.…”

Section: Related Workmentioning

confidence: 99%

“…Several previous studies attempt to filter out noises and to understand the large data set extracted from social media. Harris et al use geo-location information to remove irrelevant tweets [12]. Musaev et al design text classification to filter out noises and compute the relevance ranking of users to achieve better accuracy on landslide detection [13,7,14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Understanding Social Media Beyond Text: A Reliable Practice on Twitter

Hou

Han

et al. 2020

Preprint

View full text Add to dashboard Cite

Social media provides high-volume and real-time data, which has been broadly used in diverse applications in sales, marketing, disaster management, health surveillance, etc. However, distinguishing between noises and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. The rich information is not only included in the short textual content but also embedded in the images and videos. In this paper, we introduce an effective and efficient framework for event detection with social media data. The framework integrates both textual and imagery content in the hope to fully utilize the information. The approach has been demonstrated to be more accurate than the text-only approach by removing 58 (66.7%) false-positive events. The precision of event detection is improved by 6.5%. Besides, based on our analysis, we also look into the content of these images to further explore the space of social media studies. Finally, the closely related text and image from social media offer us valuable text-image mapping, which can enable knowledge transfer between two media types.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding Social Media Beyond Text: A Reliable Practice on Twitter

Hou

Han

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…These systems are open to the public, but also have restricted access to serve the needs of health agencies such as private discussion forums, increased functionality and data processing of commercial sources (35,36). Fully automated systems are faster at processing data and less expensive to operate than moderated systems.…”

Section: Event-based Surveillance Systemsmentioning

confidence: 99%

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change

Rees

Gachon

et al. 2019

CCDR

View full text Add to dashboard Cite

A new generation of surveillance strategies is being developed to help detect emerging infections and to identify the increased risks of infectious disease outbreaks that are expected to occur with climate change. These surveillance strategies include event-based surveillance (EBS) systems and risk modelling. The EBS systems use open-source internet data, such as media reports, official reports, and social media (such as Twitter) to detect evidence of an emerging threat, and can be used in conjunction with conventional surveillance systems to enhance early warning of public health threats. More recently, EBS systems include artificial intelligence applications such machine learning and natural language processing to increase the speed, capacity and accuracy of filtering, classifying and analysing health-related internet data. Risk modelling uses statistical and mathematical methods to assess the severity of disease emergence and spread given factors about the host (e.g. number of reported cases), pathogen (e.g. pathogenicity) and environment (e.g. climate suitability for reservoir populations). The types of data in these models are expanding to include health-related information from open-source internet data and information on mobility patterns of humans and goods. This information is helping to identify susceptible populations and predict the pathways from which infections might spread into new areas and new countries. As a powerful addition to traditional surveillance strategies that identify what has already happened, it is anticipated that EBS systems and risk modelling will increasingly be used to inform public health actions to prevent, detect and mitigate the climate change increases in infectious diseases. Affiliations

show abstract

“…Parmi les systèmes entièrement automatisés, on compte le système d'information médicale de la Commission européenne (MedISys), le Pattern-based Understanding and Learning System (PULS) et HealthMap. Bien que ces systèmes soient accessibles au public, ils comportent aussi des zones réservées aux organismes sanitaires : forums de discussion privés, fonctionnalité accrue et traitement des données de sources commerciales (35,36). Les systèmes entièrement automatisés traitent les données plus rapidement et leur utilisation est moins coûteuse que les systèmes modérés.…”

Section: Systèmes De Surveillance éVénementielleunclassified