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A Scalable Framework to Detect Personal Health Mentions on Twitter.

Yin Z, Fabbri D, Rosenbloom ST, Malin B - J. Med. Internet Res. (2015)

Bottom Line: Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001).Fourth, the likelihood that people disclose their own versus other people's health status was dependent on health issue in a statistically significant manner as well (P<.001).Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept. of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States.

ABSTRACT

Background: Biomedical research has traditionally been conducted via surveys and the analysis of medical records. However, these resources are limited in their content, such that non-traditional domains (eg, online forums and social media) have an opportunity to supplement the view of an individual's health.

Objective: The objective of this study was to develop a scalable framework to detect personal health status mentions on Twitter and assess the extent to which such information is disclosed.

Methods: We collected more than 250 million tweets via the Twitter streaming API over a 2-month period in 2014. The corpus was filtered down to approximately 250,000 tweets, stratified across 34 high-impact health issues, based on guidance from the Medical Expenditure Panel Survey. We created a labeled corpus of several thousand tweets via a survey, administered over Amazon Mechanical Turk, that documents when terms correspond to mentions of personal health issues or an alternative (eg, a metaphor). We engineered a scalable classifier for personal health mentions via feature selection and assessed its potential over the health issues. We further investigated the utility of the tweets by determining the extent to which Twitter users disclose personal health status.

Results: Our investigation yielded several notable findings. First, we find that tweets from a small subset of the health issues can train a scalable classifier to detect health mentions. Specifically, training on 2000 tweets from four health issues (cancer, depression, hypertension, and leukemia) yielded a classifier with precision of 0.77 on all 34 health issues. Second, Twitter users disclosed personal health status for all health issues. Notably, personal health status was disclosed over 50% of the time for 11 out of 34 (33%) investigated health issues. Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001). For instance, more than 80% of the tweets about migraines (83/100) and allergies (85/100) communicated personal health status, while only around 10% of the tweets about obesity (13/100) and heart attack (12/100) did so. Fourth, the likelihood that people disclose their own versus other people's health status was dependent on health issue in a statistically significant manner as well (P<.001). For example, 69% (69/100) of the insomnia tweets disclosed the author's status, while only 1% (1/100) disclosed another person's status. By contrast, 1% (1/100) of the Down syndrome tweets disclosed the author's status, while 21% (21/100) disclosed another person's status.

Conclusions: It is possible to automatically detect personal health status mentions on Twitter in a scalable manner. These mentions correspond to the health issues of the Twitter users themselves, but also other individuals. Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes.

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Related in: MedlinePlus

PR (precision recall) curves for testing on the gold, CAN (conflict as negative), and CAP (conflict as positive) datasets.
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figure6: PR (precision recall) curves for testing on the gold, CAN (conflict as negative), and CAP (conflict as positive) datasets.

Mentions: We trained the SYND classifier with the gold standard datasets for cancer, depression, hypertension, and leukemia, and tested it on the other three types of datasets. Figure 6 depicts the PR curves for each dataset and shows the average and standard deviation of AUPRC. The upper line corresponds to testing on the CAP dataset (AUPRC 0.753, SD 0.005), the middle line corresponds to testing on the TieBreak dataset (AUPRC 0.685, SD 0.005) and the lower line corresponds to testing on the CAP dataset (AUPRC 0.594, SD 0.007). When fixing the recall to 0.4, it was observed that the CAP, TieBreak, and CAN scenarios yield a precision of 0.8, 0.77, and 0.61, respectively. These results demonstrate the scalability of the system classifiers to obtain a high precision with a reasonable recall when testing many other health issues in the Twitter environment.


A Scalable Framework to Detect Personal Health Mentions on Twitter.

Yin Z, Fabbri D, Rosenbloom ST, Malin B - J. Med. Internet Res. (2015)

PR (precision recall) curves for testing on the gold, CAN (conflict as negative), and CAP (conflict as positive) datasets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526910&req=5

figure6: PR (precision recall) curves for testing on the gold, CAN (conflict as negative), and CAP (conflict as positive) datasets.
Mentions: We trained the SYND classifier with the gold standard datasets for cancer, depression, hypertension, and leukemia, and tested it on the other three types of datasets. Figure 6 depicts the PR curves for each dataset and shows the average and standard deviation of AUPRC. The upper line corresponds to testing on the CAP dataset (AUPRC 0.753, SD 0.005), the middle line corresponds to testing on the TieBreak dataset (AUPRC 0.685, SD 0.005) and the lower line corresponds to testing on the CAP dataset (AUPRC 0.594, SD 0.007). When fixing the recall to 0.4, it was observed that the CAP, TieBreak, and CAN scenarios yield a precision of 0.8, 0.77, and 0.61, respectively. These results demonstrate the scalability of the system classifiers to obtain a high precision with a reasonable recall when testing many other health issues in the Twitter environment.

Bottom Line: Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001).Fourth, the likelihood that people disclose their own versus other people's health status was dependent on health issue in a statistically significant manner as well (P<.001).Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept. of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States.

ABSTRACT

Background: Biomedical research has traditionally been conducted via surveys and the analysis of medical records. However, these resources are limited in their content, such that non-traditional domains (eg, online forums and social media) have an opportunity to supplement the view of an individual's health.

Objective: The objective of this study was to develop a scalable framework to detect personal health status mentions on Twitter and assess the extent to which such information is disclosed.

Methods: We collected more than 250 million tweets via the Twitter streaming API over a 2-month period in 2014. The corpus was filtered down to approximately 250,000 tweets, stratified across 34 high-impact health issues, based on guidance from the Medical Expenditure Panel Survey. We created a labeled corpus of several thousand tweets via a survey, administered over Amazon Mechanical Turk, that documents when terms correspond to mentions of personal health issues or an alternative (eg, a metaphor). We engineered a scalable classifier for personal health mentions via feature selection and assessed its potential over the health issues. We further investigated the utility of the tweets by determining the extent to which Twitter users disclose personal health status.

Results: Our investigation yielded several notable findings. First, we find that tweets from a small subset of the health issues can train a scalable classifier to detect health mentions. Specifically, training on 2000 tweets from four health issues (cancer, depression, hypertension, and leukemia) yielded a classifier with precision of 0.77 on all 34 health issues. Second, Twitter users disclosed personal health status for all health issues. Notably, personal health status was disclosed over 50% of the time for 11 out of 34 (33%) investigated health issues. Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001). For instance, more than 80% of the tweets about migraines (83/100) and allergies (85/100) communicated personal health status, while only around 10% of the tweets about obesity (13/100) and heart attack (12/100) did so. Fourth, the likelihood that people disclose their own versus other people's health status was dependent on health issue in a statistically significant manner as well (P<.001). For example, 69% (69/100) of the insomnia tweets disclosed the author's status, while only 1% (1/100) disclosed another person's status. By contrast, 1% (1/100) of the Down syndrome tweets disclosed the author's status, while 21% (21/100) disclosed another person's status.

Conclusions: It is possible to automatically detect personal health status mentions on Twitter in a scalable manner. These mentions correspond to the health issues of the Twitter users themselves, but also other individuals. Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes.

Show MeSH
Related in: MedlinePlus