Social Media Analytics for Dengue Monitoring in Malaysia

Kannan, Rathimala; Govindasamy, Menagaeswary Ap; Soon, Lay-Ki; Ramakrishnan, Kannan

doi:10.1109/iccsce.2018.8685028

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…In addition, the authors stated that the geolocation of tweets through geotagging remained a major challenge. Several other studies have described the use of Twitter for outbreak investigation [8][9][10] or for understanding public perception of an epidemic [11,12], but these did not provide insights into the possible use of social media for automated event detection and real-time monitoring.…”

Section: Introductionmentioning

confidence: 99%

Epitweetr: Early warning of public health threats using Twitter data

Espinosa

Wijermans

Orchard³

et al. 2022

Eurosurveillance

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Background The European Centre for Disease Prevention and Control (ECDC) systematically collates information from sources to rapidly detect early public health threats. The lack of a freely available, customisable and automated early warning tool using data from Twitter prompted the ECDC to develop epitweetr, which collects, geolocates and aggregates tweets generating signals and email alerts. Aim This study aims to compare the performance of epitweetr to manually monitoring tweets for the purpose of early detecting public health threats. Methods We calculated the general and specific positive predictive value (PPV) of signals generated by epitweetr between 19 October and 30 November 2020. Sensitivity, specificity, timeliness and accuracy and performance of tweet geolocation and signal detection algorithms obtained from epitweetr and the manual monitoring of 1,200 tweets were compared. Results The epitweetr geolocation algorithm had an accuracy of 30.1% at national, and 25.9% at subnational levels. The signal detection algorithm had 3.0% general PPV and 74.6% specific PPV. Compared to manual monitoring, epitweetr had greater sensitivity (47.9% and 78.6%, respectively), and reduced PPV (97.9% and 74.6%, respectively). Median validation time difference between 16 common events detected by epitweetr and manual monitoring was -48.6 hours (IQR: −102.8 to −23.7). Conclusion Epitweetr has shown sufficient performance as an early warning tool for public health threats using Twitter data. Since epitweetr is a free, open-source tool with configurable settings and a strong automated component, it is expected to increase in usability and usefulness to public health experts.

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Section: Introductionmentioning

confidence: 99%

Epitweetr: Early warning of public health threats using Twitter data

Espinosa

Wijermans

Orchard³

et al. 2022

Eurosurveillance

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“…The results from the reviewed papers and articles show different SMA methods and tools. Most of the reviewed articles demonstrate the usage of sentiment analysis in different areas 54.54% of the articles reviewed uses sentiment analysis Su and Chen (2016), Vorvoreanu et al (2013), Chang et al (2017), Xiang et al (2016), Park et al (2016, He et al (2017), Stieglitz (2012, Anyanwu (2019), Shang et al (2018), Dong et al (2013,), Xu et al (2019), Dahal et al (2019), Kannan et al (2018), Martinez et al(2019), Alamsyah (2017), Barrelet et al (2016), Chen (2016), Chumwatana and Wongkolkitsilp (2019) Kannan et al (2018), Hu et al (2011), and Sachdeva and Mc Caffrey (2018, clustering technique 6.81% Jansen et al (2018), Myaeng et al (2016), and Ghosh et al (2017 , natural language processing 6.81% Barrelet et al (2016), Al Kubaizi et al (2015), and Saravan and Perepu ( 2019), text analysis 4.54% Dias et al (2018), andSingh et al (2018), event detection tool 2.27% Weiler (2013) and social network analysis 6.81% Alamsyah (2017), Udanor et al (2016), and Rahmani et al (2013).…”

Section: Sma Methods and Tools Usedmentioning

confidence: 99%

“…Social media analytics applications have potential in several health services, according to literature there are some studies explaining this well, Kannan et al (2018) introduced the study using topic discovery and contents analysis in twitter platform to determine the information about Dengue fever shared in twitter. Culotta (2010) introduces the study which uses predictive analysis to predict the rate of influenza in a population using twitter messages.…”

Section: Summary Of Reviewed Articlesmentioning

confidence: 99%

“…The most usable platform for social media analytics was twitter. 77.27% of the reviewed articles use twitter as the social media platform and the study found that most of the analytics performed in twitter platform was sentiment analysis; Su and Chen (2016), Vorvoreanu et al (2013), Bukhari et al (2012), Xiang et al (2016), Park et al (2016), Stieglitz (2012), Myaeng (2016, Weiler (2013), Anyanwu (2019), Shang et al (2018), Dong et al (2019), Xu et al (2019), Dahal et al (2019), Singh et al (2018), Kannan et al (2018), Culotta (2010), Martinez et al (2019), Al Kubaizi et al (2015), Saravan and Perepu (2019), Alamsyah (2017), Barrelet et al (2016), Chen (2016), Chumwatana and Wongkolkitsilp (2019), Tian et al (2019), He et al (2019), Hu et al (2011), Sachdeva andMc Caffrey (2018), Udanor et al (2016), Wu et al (2018), Santander et al (2020, Ghosh et al (2017), Araya et al (2017, Rahmani et al (2013), andVon Hoffen et al (2019). 18.18% of the reviewed articles used Facebook; Vorvoreanu et al (2013), Bukhari et al (2012), Dias et al (2018), Jain et al (2015, Saravan and Perepu (2019), Chumwatana and Wongkolkitsilp (2019), Cvijikj andMi...…”

Section: Types Of Sm Platforms Used For Smamentioning

confidence: 99%

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A Review of Usage and Applications of Social Media Analytics

Madila¹,

Dida²,

Kaijage³

2021

J INFORM SYSTEMS ENG

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This paper presents the report of a social media analytics (SMA) review. The review conducted to find out the methods and tools used in social media analytics, types of social media platforms which the SMA are performed and the field which SMA has been performed. Social media contains a lot of user uploaded data in different formats like text, images, photos, video etc. These large volumes of data are converted in meaningful information which can be understood using different methods and tools which are called social media analytics. A literature review of articles published between 2010-2020 has been conducted using articles obtained from reputable databases IEEE Xplore, ACM digital, Emerald insight, Springer Link and Science direct. A number of 44 articles have been selected for review from 110 retrieved papers. The paper has been reviewed according to the study objectives. The study found that SMA tools and techniques which have been used are sentiment analysis, youtube analytics, visible intelligence, IBM Watson tool and predictive models. The social media platforms which were mostly used are twitter, facebook, youtube, trip advisor and blogs. SMA has been observed in different fields like agriculture, politics, health, social and business sector.

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“…In addition, the authors found out that the geolocation of tweets through geotagging remained a major challenge. Several other published studies have described the use of Twitter for outbreak investigation [10][11][12] or for understanding the public perception of an epidemic 13,14 , but these did not provide insights on the possible use of social media for automated event detection and monitoring in real time.…”

Section: Introductionmentioning

confidence: 99%

Epitweetr: Early warning of public health threats using Twitter data

Espinosa

Wijermans

Orchard³

et al. 2021

Preprint

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Background: ECDC performs epidemic intelligence activities to systematically collate information from a variety of sources, including Twitter, to rapidly detect public health events. The lack of a freely available, customisable and automated early warning tool using Twitter data, prompted ECDC to develop epitweetr. The specific objectives are to assess the performance of the geolocation and signal detection algorithms used by epitweetr and to assess the performance of epitweetr in comparison with the manual monitoring of Twitter for early detection of public health threats. Methods: Epitweetr collects, geolocates and aggregates tweets to generate signals and email alerts. Firstly, we evaluated manually the tweet geolocation characteristics of 1,200 tweets, and assessed its accuracy in extracting the correct location and its performance in detecting tweets with available information on the tweet geolocation. Secondly, we evaluated signals generated by epitweetr between 19 October and 30 November 2020 and we calculated the positive predictive value (PPV). Then, we evaluated the sensitivity, specificity and timeliness of epitweetr in comparison with Twitter manual monitoring. Findings: The epitweetr geolocation algorithm had an accuracy of 30.1% and 25.9% at national and subnational levels, respectively. General and specific PPV of the signal detection algorithm was 3.0% and 74.6%, respectively. Epitweetr and/or manual monitoring detected 570 signals and 454 events. Epitweetr had a sensitivity of 78.6% [75.2% - 82.0%] and PPV of 74.6% [70.5% - 78.6%]; and the manual monitoring had a sensitivity of 47.9% [43.8% - 52.0%] and PPV of 97.9% [95.8% - 99.9%]. The median validation time difference between sixteen common events detected by epitweetr and manual monitoring was -48.6 hours [(-102.8) - (-23.7) hours]. Interpretation: Epitweetr has shown to have sufficient performance as an early warning tool for public health threats using Twitter data. Having developed epitweetr as a free, open-source tool with several configurable settings and a strong automated component, it is expected to increase its usability and usefulness to public health experts.

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Social Media Analytics for Dengue Monitoring in Malaysia

Cited by 7 publications

References 12 publications

Epitweetr: Early warning of public health threats using Twitter data

Epitweetr: Early warning of public health threats using Twitter data

A Review of Usage and Applications of Social Media Analytics

Epitweetr: Early warning of public health threats using Twitter data

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