Tweetluenza: Predicting flu trends from twitter data

Alkouz, Balsam; Aghbari, Zaher Al; Abawajy, Jemal

doi:10.26599/bdma.2019.9020012

Cited by 49 publications

(29 citation statements)

References 21 publications

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“…Several studies have utilized the abundance of information offered by social platforms to conduct nonclinical medical research. For example, Twitter has been a source of data for many health and medical studies, such as surveillance and monitoring of flu and cancer timelines and distribution across the United States [ 1 ], analyzing the spread of influenza in the United Arab Emirates based on geotagged tweets in Arabic [ 2 ], and the surveillance and monitoring of influenza in the United Arab Emirates based on tweets in Arabic and English [ 3 ]. In addition, Twitter data have been utilized in symptom and disease identification in Saudi Arabia [ 4 ], and most recently, to examine COVID-19 symptoms as reported on Twitter [ 5 ] and to analyze the chronological and geographical distribution of infected tweeters in the United States [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis

Alanazi¹,

Alashaikh²,

Alqurashi³

et al. 2020

J Med Internet Res

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Background A substantial amount of COVID-19–related data is generated by Twitter users every day. Self-reports of COVID-19 symptoms on Twitter can reveal a great deal about the disease and its prevalence in the community. In particular, self-reports can be used as a valuable resource to learn more about common symptoms and whether their order of appearance differs among different groups in the community. These data may be used to develop a COVID-19 risk assessment system that is tailored toward a specific group of people. Objective The aim of this study was to identify the most common symptoms reported by patients with COVID-19, as well as the order of symptom appearance, by examining tweets in Arabic. Methods We searched Twitter posts in Arabic for personal reports of COVID-19 symptoms from March 1 to May 27, 2020. We identified 463 Arabic users who had tweeted about testing positive for COVID-19 and extracted the symptoms they associated with the disease. Furthermore, we asked them directly via personal messaging to rank the appearance of the first 3 symptoms they had experienced immediately before (or after) their COVID-19 diagnosis. Finally, we tracked their Twitter timeline to identify additional symptoms that were mentioned within ±5 days from the day of the first tweet on their COVID-19 diagnosis. In total, 270 COVID-19 self-reports were collected, and symptoms were (at least partially) ranked. Results The collected self-reports contained 893 symptoms from 201 (74%) male and 69 (26%) female Twitter users. The majority (n=270, 82%) of the tracked users were living in Saudi Arabia (n=125, 46%) and Kuwait (n=98, 36%). Furthermore, 13% (n=36) of the collected reports were from asymptomatic individuals. Of the 234 users with symptoms, 66% (n=180) provided a chronological order of appearance for at least 3 symptoms. Fever (n=139, 59%), headache (n=101, 43%), and anosmia (n=91, 39%) were the top 3 symptoms mentioned in the self-reports. Additionally, 28% (n=65) reported that their COVID-19 experience started with a fever, 15% (n=34) with a headache, and 12% (n=28) with anosmia. Of the 110 symptomatic cases from Saudi Arabia, the most common 3 symptoms were fever (n=65, 59%), anosmia (n=46, 42%), and headache (n=42, 38%). Conclusions This study identified the most common symptoms of COVID-19 from tweets in Arabic. These symptoms can be further analyzed in clinical settings and may be incorporated into a real-time COVID-19 risk estimator.

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Section: Introductionmentioning

confidence: 99%

Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis

Alanazi¹,

Alashaikh²,

Alqurashi³

et al. 2020

J Med Internet Res

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“…This section is a continuation of the review work discussion in our prior work [10]. The most recent work carried out by Alkouz et al [11] have developed a model that is capable of performing forecasting of the disease on the basis of social network platforms. The study makes use of the linear regression model over the data for improving the prediction performance.…”

Section: Related Workmentioning

confidence: 95%

Modeling Method for Leveraging Data Quality in Healthcare Big Data

K.*,

Ramesh

2020

IJITEE

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An accurate diagnosis of the healthcare-based Big Data will always demand a significant level of quality in its input data itself, which is a serious level of concern in the area of healthcare analytics. Review of existing approaches shows that there has been various learning-based approaches being used for disease diagnosis which often ignores various issues viz. data aggregation, presence of error prone data, accuracy etc. Therefore, this paper presents a novel framework which offers cost effective modeling of the aggregation process of healthcare-big data followed by facilitating solution towards identifying and rectifying all the positions within a database system where there are presence of an error. The proposed system offer a mechanism where the error-prone data has been identified and substituted with data of better quality in order to offer better analytical outcomes. The study offers a strong baseline in order to leverage the data quality in healthcare big data.

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“…Social Media Marketing using Hashtag (#) approach to attract Twitter F. M. F. Wong [18] Parody, Political, Media, Others…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

Nugroho¹,

Sumpeno²,

Fujisawa³

et al. 2021

IJIES

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Campaigns on online social media are becoming intriguing because all parties can compete with each other. Various content can be created to support or defeat one another. Interaction is important to control the battlefield in the face of disinformation. The campaign team must be able to increase interaction and reporting it as a form of achievement. The reality is that the campaign team has not been able to provide a measurable report on the success rate of political campaign interaction on social media. This is because there is no standardization in measuring these interactions. This fact inspires to build a model for measuring campaign interaction on social media. This model will be able to help the campaign team. This study uses a dataset from a governor election campaign in Indonesia which is then tested on a presidential election campaign dataset in America. The model was built using machine learning with the k classification method Nearest Neighbor (kNN) and Distance Weight kNN (DWkNN). The research stages were arranged using the Nassi-Shneiderman Framework. This framework describes the stages and comparison of the use of the KNN and the DWkNN from the training to testing stages in an easy to understand manner. The kNN and DWkNN training stages showed excellent accuracy results of nearly 100%. Furthermore, at the testing stage using the crossvalidation method with a variety of fold 5 to 20 variations showed excellent results, namely 99.89% on the governor election dataset and a range of 98% on the American presidential election dataset. This classification model has been tested using several datasets from several candidate social media accounts. We also created a dataset campaign interaction. This study shows that the proposed model can outperform previous studies. So, that it provides novelty in the form of a campaign interaction guide model for the campaign team.

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Tweetluenza: Predicting flu trends from twitter data

Cited by 49 publications

References 21 publications

Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis

Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis

Modeling Method for Leveraging Data Quality in Healthcare Big Data

A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

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