A Social Media Platform for Infectious Disease Analytics

Yang, Hong; Sinnott, Richard

doi:10.1007/978-3-319-95162-1_36

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…The method achieved an accuracy of 87.1% for the classification of tweets being related or unrelated to a topic. Another study [ 17 ] concluded that there is a high correlation between flu tweets and Google Trends data.…”

Section: Introductionmentioning

confidence: 99%

Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study

Alsudias¹,

Rayson²

2021

JMIR Med Inform

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Background Twitter is a real-time messaging platform widely used by people and organizations to share information on many topics. Systematic monitoring of social media posts (infodemiology or infoveillance) could be useful to detect misinformation outbreaks as well as to reduce reporting lag time and to provide an independent complementary source of data compared with traditional surveillance approaches. However, such an analysis is currently not possible in the Arabic-speaking world owing to a lack of basic building blocks for research and dialectal variation. Objective We collected around 4000 Arabic tweets related to COVID-19 and influenza. We cleaned and labeled the tweets relative to the Arabic Infectious Diseases Ontology, which includes nonstandard terminology, as well as 11 core concepts and 21 relations. The aim of this study was to analyze Arabic tweets to estimate their usefulness for health surveillance, understand the impact of the informal terms in the analysis, show the effect of deep learning methods in the classification process, and identify the locations where the infection is spreading. Methods We applied the following multilabel classification techniques: binary relevance, classifier chains, label power set, adapted algorithm (multilabel adapted k-nearest neighbors [MLKNN]), support vector machine with naive Bayes features (NBSVM), bidirectional encoder representations from transformers (BERT), and AraBERT (transformer-based model for Arabic language understanding) to identify tweets appearing to be from infected individuals. We also used named entity recognition to predict the place names mentioned in the tweets. Results We achieved an F1 score of up to 88% in the influenza case study and 94% in the COVID-19 one. Adapting for nonstandard terminology and informal language helped to improve accuracy by as much as 15%, with an average improvement of 8%. Deep learning methods achieved an F1 score of up to 94% during the classifying process. Our geolocation detection algorithm had an average accuracy of 54% for predicting the location of users according to tweet content. Conclusions This study identified two Arabic social media data sets for monitoring tweets related to influenza and COVID-19. It demonstrated the importance of including informal terms, which are regularly used by social media users, in the analysis. It also proved that BERT achieves good results when used with new terms in COVID-19 tweets. Finally, the tweet content may contain useful information to determine the location of disease spread.

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Section: Introductionmentioning

confidence: 99%

Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study

Alsudias¹,

Rayson²

2021

JMIR Med Inform

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“…In [14] is presented a real time system for the prediction and detection of the proliferation of an epidemic by identifying disease tweets by graphical location. A seminal paper published by [15] proposes combining Twitter data with Goolgle Trends data to track the spread of infectious diseases. A study conducted by [16] analyzed Twitter data collected during some infectious disease outbreaks.…”

Section: Related Workmentioning

confidence: 99%

Text Mining Approach to Analyze Coronavirus Impact: Mexico City as Case of Study

Saire

Pineda-Briseño

2020

Preprint

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The epidemiological outbreak of a novel coronavirus (2019-nCoV or Covid-19) in China, and its rapid spread, gave rise to the first pandemic in the digital age. Derived from this fact that has surprised humanity, many countries started with different strategies in order to stop the infection. In this context, one of the greatest challenges for the scientific community is monitoring (real time) the global population to get immediate feedback of what is happening with the people during this public health contingency. An alternative interesting and affordable for the materialization of the aforementioned are the social networks. In a social network, the persons can act as sensors/information not only of personal data but also data derived from their behavior. This paper aims to analyze the publications of people in Mexico using a Text Mining approach. Specifically, Mexico City is presented as a case study to help understand the impact on society of the spread of Covid-19.

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“…Some studies composed their lexicons from emoticons/emojis that were extracted from a dataset [474,48,423,343,345,312,391,444,430,407], combined publicly available emoticon lexicons/lists [495] or mapped emoticons to their corresponding polarity [481], and others [424,499,389,390,414,444,430,503] used seed/feeling/emotional words to establish a microblog typical emotional dictionary. Additionally, some authors constructed or used sentiment lexicons [195,123,417,316,215,124,320,322,328,496,361,363,439,492,397,398,91,401,403] some of which are domain or language specific [478,317,516,347,206,100,388], others that extend state-of-the-art l...…”

Section: Hybrid (Hy)mentioning

confidence: 99%

“…Further to the quoted algorithms, 22 studies [317,452,505,319,124,512,461,488,489,344,448,352,353,356,47,449,381,386,387,275,475,117] used ensemble learning methods in their work, where they combined the output of several base machine learning and/or deep learning methods. In particular, [117] compared eight popular lexicon and machine learning based sentiment analysis algorithms, and then developed an ensemble that combines them, which in turn provided the best coverage results and competitive agreement.…”

Section: Hybrid (Hy)mentioning

confidence: 99%

“…Thirteen studies applied social opinion mining on the Film industry for recommendations [425,135], box office predictions [176,403] or from a general perspective [241,467,484,428,177,117,213,429,112]. Similarly, 13 studies focused on Healthcare, namely on epidemics/infectious diseases [317,366,76,117], drugs [196,202,359], hospitals [129], vaccines [159], public health, such as epidemics, clinical science and mental health [385,40], and in general, such as health-related tweets [412] and health applications [558].…”

Section: Application Areas Of Social Opinion Miningmentioning

confidence: 99%

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Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

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Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing and other

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A Social Media Platform for Infectious Disease Analytics

Cited by 10 publications

References 19 publications

Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study

Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study

Text Mining Approach to Analyze Coronavirus Impact: Mexico City as Case of Study

Over a Decade of Social Opinion Mining: A Systematic Review

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