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Identifying tweets of personal health experience through word embedding and LSTM neural network

Overview of attention for article published in BMC Bioinformatics, June 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

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1 tweeter
patent
1 patent

Citations

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26 Dimensions

Readers on

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61 Mendeley
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Title
Identifying tweets of personal health experience through word embedding and LSTM neural network
Published in
BMC Bioinformatics, June 2018
DOI 10.1186/s12859-018-2198-y
Pubmed ID
Authors

Keyuan Jiang, Shichao Feng, Qunhao Song, Ricardo A. Calix, Matrika Gupta, Gordon R. Bernard

Abstract

As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorithms rely on features engineered by human domain experts, and engineering such features is a challenging task and requires much human intelligence. The resultant features may not be optimal for the classification problem, and can make it challenging for conventional classifiers to correctly predict personal experience tweets (PETs) due to the various ways to express and/or describe personal experience in tweets. In this study, we developed a method that combines word embedding and long short-term memory (LSTM) model without the need to engineer any specific features. Through word embedding, tweet texts were represented as dense vectors which in turn were fed to the LSTM neural network as sequences. Statistical analyses of the results of 10-fold cross-validations of our method and conventional methods indicate that there exist significant differences (p < 0.01) in performance measures of accuracy, precision, recall, F1-score, and ROC/AUC, demonstrating that our approach outperforms the conventional methods in identifying PETs. We presented an efficient and effective method of identifying health-related personal experience tweets by combining word embedding and an LSTM neural network. It is conceivable that our method can help accelerate and scale up analyzing textual data of social media for health surveillance purposes, because of no need for the laborious and costly process of engineering features.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 21%
Student > Ph. D. Student 7 11%
Other 6 10%
Student > Bachelor 5 8%
Student > Doctoral Student 3 5%
Other 6 10%
Unknown 21 34%
Readers by discipline Count As %
Computer Science 18 30%
Medicine and Dentistry 5 8%
Nursing and Health Professions 3 5%
Engineering 2 3%
Biochemistry, Genetics and Molecular Biology 1 2%
Other 4 7%
Unknown 28 46%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 July 2021.
All research outputs
#6,769,207
of 21,568,231 outputs
Outputs from BMC Bioinformatics
#2,740
of 6,975 outputs
Outputs of similar age
#114,324
of 298,673 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 22 outputs
Altmetric has tracked 21,568,231 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 6,975 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 298,673 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.