Monitoring of Epidemic Outbreaks Using Social Media Data

Shinde, Tejas; Thatte, Parikshit; Sachdev, Sachin; Pujari, Vidya

doi:10.1109/incet51464.2021.9456132

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…URL addresses were changed to string "URLs", everything starting with an "at" sign was changed to "USER_MENTION" and emoticons were changed according to their emotion to "EMO_POS" and "EMO_NEG" [33]. It is in such a way that tokens in each tweet were identified (tokenisation process [35]).…”

Section: Data Processingmentioning

confidence: 99%

Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period—Temporal and Spatial Differences in Activity Topics

et al. 2022

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Public transport requires constant feedback to improve and satisfy daily users. Twitter offers monitoring of user messages, discussion and emoticons addressed to official transport provider accounts. This information can be particularly useful in delicate situations such as management of transit operations during the COVID-19 pandemic. The behaviour of Twitter users in Madrid, London and Prague is analysed with the goal of recognising similar patterns and detecting differences in traffic related topics and temporal cycles. Topics in transit tweets were identified using the bag of words approach and pre-processing in R. COVID-19 is a dominant topic for both London and Madrid but a minor one for Prague, where Twitter serves mainly to deliver messages from politicians and stakeholders. COVID-19 interferes with the meaning of other topics, such as overcrowding or staff. Additionally, specific topics were discovered, such as air quality in Victoria Station, London, or racism in Madrid. For all cities, transit-related tweeting activity declines over weekends. However, London shows much less decline than Prague or Madrid. Weekday daily rhythms show major tweeting activity during the morning in all cities but with different start times. The spatial distribution of tweets for the busiest stations shows that the best-balanced tweeting activity is found in Madrid metro stations.

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Section: Data Processingmentioning

confidence: 99%

Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period—Temporal and Spatial Differences in Activity Topics

et al. 2022

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An Analysis of Methods for Forecasting Epidemic Disease Outbreaks using Information from Social Media

Wankhede¹,

Sadawarte²,

Mulla³

et al. 2022

IJRTE

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Predicting the rise or fall of an epidemic or pandemic is an essential part of establishing control over it. Post-World War 1, when there was an outbreak of the “Black Plague” there weren’t any means to analyze and predict. Although today we are equipped with tools like Machine Learning and Artificial Intelligence which have certainly enabled us to prevent unnecessary loss of life. It helps prepare the health officials to build the infrastructure and interpret the intensity of preparedness regulation of resources. The aim of this survey is to analyze and shed some light on the various algorithms and methods such as - regression models, neural networks, ARIMA, etc. Before building any model, gathering and processing the data is also essential. Hence our paper also focuses on which social media platforms proved beneficial in comparison to all we found and then made fit to be incorporated into the models. While researching for this paper, we observed that every disease has a different transmission type that leads to an outbreak and is a key factor in constructing a model. The literature evaluation in this work is centered on various prediction algorithms and their strategies for extracting online data from social media sites like Facebook and Twitter, all of which have drawn a lot of interest in early disease diagnosis for public health.

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Monitoring of Epidemic Outbreaks Using Social Media Data

Cited by 2 publications

References 4 publications

Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period—Temporal and Spatial Differences in Activity Topics

Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period—Temporal and Spatial Differences in Activity Topics

An Analysis of Methods for Forecasting Epidemic Disease Outbreaks using Information from Social Media

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