Identifying personal health experience tweets with deep neural networks

Jiang, Keyuan; Gupta, Ravish; Gupta, Matrika; Calix, Ricardo A.; Bernard, Gordon R.

doi:10.1109/embc.2017.8037039

Cited by 7 publications

(8 citation statements)

References 24 publications

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“…Results showed that the highest F-measure value reached 72.9%. Compared with results of similar studies (F-measure: 37–83%) [15,16,18,28,29,30], the classification performance is satisfying in this current study. Moreover, with the help of established classification models, the workload of human coders can also be reduced considerably (screening efficacy: 31.7%–47.1%).…”

Section: Discussionsupporting

confidence: 59%

A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media

Jiao²,

Liu

et al. 2019

IJERPH

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Live-stream suicide has become an emerging public health problem in many countries. Regular users are often the first to witness and respond to such suicides, emphasizing their impact on the success of crisis intervention. In order to reduce the likelihood of suicide deaths, this paper aims to use psycholinguistic analysis methods to facilitate automatic detection of negative expressions in responses to live-stream suicides on social media. In this paper, a total of 7212 comments posted on suicide-related messages were collected and analyzed. First, a content analysis was performed to investigate the nature of each comment (negative or not). Second, the simplified Chinese version of the LIWC software was used to extract 75 psycholinguistic features from each comment. Third, based on 19 selected key features, four classification models were established to differentiate between comments with and without negative expressions. Results showed that 19.55% of 7212 comments were recognized as “making negative responses”. Among the four classification models, the highest values of Precision, Recall, F-Measure, and Screening Efficacy reached 69.8%, 85.9%, 72.9%, and 47.1%, respectively. This paper confirms the need for campaigns to reduce negative responses to live-stream suicides and support the use of psycholinguistic analysis methods to improve suicide prevention efforts.

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Section: Discussionsupporting

confidence: 59%

A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media

Jiao²,

Liu

et al. 2019

IJERPH

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“…J-48-Decision Trees classifier performed well in predicting positive and negative tweets related to personal health experience [33]. Similarly, R. A. Calix and A.…”

Section: Decision Treementioning

confidence: 76%

“…This classifier was observed as having a good precision for text classification than other classifiers such as k-means, but not the best [30]. Other research papers that have used the k-NN are: [23,33].…”

Section: K-nearest Neighbormentioning

confidence: 94%

“…They also showed that time consumption by SVM for classification task did not get much affected when the micro-blogs needed to be arranged increased by 100 times, though there is a huge increase in the time consumption by KNN to complete the classification task. SVM turned out to be the best tweet classification method when compared with other machine learning techniques and has been utilized to classify social media data on a range of physical health issues: [20,21,23,24,25,31,32,33,34,35,36,37,38,39].…”

Section: Support Vector Machinementioning

confidence: 99%

“…Logistic regression showed excellent precision for the analysis of personal and non-personal tweets [23]. Among the research papers, we have reviewed [33,44] also utilized logistic regression classifier. Maximum Entropy classifier, sometimes called Multinomial Logistic Regression [45], is also used for the text classification task.…”

Section: Logistic Regressionmentioning

confidence: 99%

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Social media based surveillance systems for healthcare using machine learning: A systematic review

Gupta

Katarya

2020

Journal of Biomedical Informatics

144

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Background: Real-time surveillance in the field of health informatics has emerged as a growing domain of interest among worldwide researchers. Evolution in this field has helped in the introduction of various initiatives related to public health informatics. Surveillance systems in the area of health informatics utilizing social media information have been developed for early prediction of disease outbreaks and to monitor diseases. In the past few years, the availability of social media data, particularly Twitter data, enabled real-time syndromic surveillance that provides immediate analysis and instant feedback to those who are charged with follow-ups and investigation of potential outbreaks. In this paper, we review the recent work, trends, and machine learning(ML) text classification approaches used by surveillance systems seeking social media data in the healthcare domain. We also highlight the limitations and challenges followed by possible future directions that can be taken further in this domain. Methods: To study the landscape of research in health informatics performing surveillance of the various healthrelated data posted on social media or web-based platforms, we present a bibliometric analysis of the 1240 publications indexed in multiple scientific databases (IEEE, ACM Digital Library, ScienceDirect, PubMed) from the year 2010-2018. The papers were further reviewed based on the various machine learning algorithms used for analyzing health-related text posted on social media platforms. Findings:: Based on the corpus of 148 selected articles, the study finds the types of social media or web-based platforms used for surveillance in the healthcare domain, along with the health topic(s) studied by them. In the corpus of selected articles, we found 26 articles were using machine learning technique. These articles were studied to find commonly used ML techniques. The majority of studies (24%) focused on the surveillance of flu or influenza-like illness (ILI). Twitter (64%) is the most popular data source to perform surveillance research using social media text data, and Support Vector Machine (SVM) (33%) being the most used ML algorithm for text classification. Conclusions: The inclusion of online data in surveillance systems has improved the disease prediction ability over traditional syndromic surveillance systems. However, social media based surveillance systems have many limitations and challenges, including noise, demographic bias, privacy issues, etc. Our paper mentions future directions, which can be useful for researchers working in the area. Researchers can use this paper as a library for social media based surveillance systems in the healthcare domain and can expand such systems by incorporating the future works discussed in our paper. period before the confirmation of a particular disease by any clinical unit or laboratory and to mobilize the rapid response. Surveillance systems are usually concerned with the systematic collection, analysis, and interpretation of the collected data along with the...

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Detecting depression stigma on social media: A linguistic analysis

Jiao²,

Zhu

2018

Journal of Affective Disorders

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Identifying personal health experience tweets with deep neural networks

Cited by 7 publications

References 24 publications

A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media

A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media

Social media based surveillance systems for healthcare using machine learning: A systematic review

Detecting depression stigma on social media: A linguistic analysis

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