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Construction of a Personal Experience Tweet Corpus for Health Surveillance

Keyuan Jiang, Ricardo Calix, Matrika Gupta
2016 Proceedings of the 15th Workshop on Biomedical Natural Language Processing  
Studies have shown that Twitter can be used for health surveillance, and personal experience tweets (PETs) are an important source of information for health surveillance.  ...  To mine Twitter data requires a relatively balanced corpus and it is challenging to construct such a corpus due to the labor-intensive annotation tasks of large data sets.  ...  This work was supported in part by the National Institutes of Health grant 1R15LM011999-01.  ... 
doi:10.18653/v1/w16-2917 dblp:conf/bionlp/JiangCG16 fatcat:k7njlnekkfgu3cobdy3p4qafxm

Identifying personal health experience tweets with deep neural networks

Keyuan Jiang, Ravish Gupta, Matrika Gupta, Ricardo A. Calix, Gordon R. Bernard
2017 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
Twitter, as a social media platform, has become an increasingly useful data source for health surveillance studies, and personal health experiences shared on Twitter provide valuable information to the  ...  In this study, we designed deep neural networks with 3 different architectural configurations, and after training them with a corpus of 8,770 annotated tweets, we used them to predict personal experience  ...  Acknowledgments Authors wish to thank Yongbing Tang for collecting the Twitter data, and Cecelia Lai for annotating the tweets.  ... 
doi:10.1109/embc.2017.8037039 pmid:29060084 pmcid:PMC5702551 dblp:conf/embc/JiangGGCB17 fatcat:vukcztehtjhjjlrlroefcbzsca

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

Keyuan Jiang, Shichao Feng, Qunhao Song, Ricardo A. Calix, Matrika Gupta, Gordon R. Bernard
2018 BMC Bioinformatics  
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  ...  Conclusion: We presented an efficient and effective method of identifying health-related personal experience tweets by combining word embedding and an LSTM neural network.  ...  Aryal for assistance on statistical analysis of the experiment results.  ... 
doi:10.1186/s12859-018-2198-y pmid:29897323 pmcid:PMC5998756 fatcat:ehqfugt3effavn2boitlqx45tu

Syndromic classification of Twitter messages [article]

Nigel Collier, Son Doan
2011 arXiv   pre-print
-fold cross validation tests were used to compare Naive Bayes (NB) and Support Vector Machine (SVM) models on a corpus of 7431 Twitter messages.  ...  We expanded upon this success to develop an automated text mining system that classifies Twitter messages in real time into six syndromic categories based on key terms from a public health ontology. 10  ...  Acknowledgements This work was in part supported by grant in aid support from the National Institute of Informatics' Grand Challenge Project (PI:NC). We are grateful  ... 
arXiv:1110.3094v1 fatcat:suamtl3bvzfsrc5g4qevg6zoka

Social Media based Surveillance Systems for Healthcare using Machine Learning: A Systematic Review

Aakansha Gupta, Rahul Katarya
2020 Journal of Biomedical Informatics  
Real-time surveillance in the field of health informatics has emerged as a growing domain of interest among worldwide researchers.  ...  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  ...  The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  ... 
doi:10.1016/j.jbi.2020.103500 pmid:32622833 pmcid:PMC7331523 fatcat:jfj34tsxkvhlhkwnna6fmgnvni

SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text [article]

Kartikey Pant, Venkata Himakar Yanamandra, Alok Debnath, Radhika Mamidi
2019 arXiv   pre-print
In this paper, we create a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet.  ...  Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services.  ...  Our schema for categorization targets posts on public health as much as tobacco related products, therefore allowing us to know the number and type of tweets used in public health surveillance for the  ... 
arXiv:1910.05598v1 fatcat:vqb72ttn7rgppf7k7frgsm57za

Automated monitoring of tweets for early detection of the 2014 Ebola epidemic

Aditya Joshi, Ross Sparks, Sarvnaz Karimi, Sheng-Lun Jason Yan, Abrar Ahmad Chughtai, Cecile Paris, C. Raina MacIntyre, Eric Forgoston
2020 PLoS ONE  
We experiment with two variations of an existing surveillance architecture: the first aggregates tweets related to different symptoms together, while the second considers tweets about each symptom separately  ...  We demonstrate the value of social media for automated surveillance of infectious diseases such as the West Africa Ebola epidemic.  ...  Personal health mention classification: This step is necessary because a tweet containing a symptom word may not be the report of a symptom.  ... 
doi:10.1371/journal.pone.0230322 pmid:32182277 fatcat:yqldzlzqlndo7fggtptoykolaq

Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance

Oduwa Edo-Osagie, Gillian Smith, Iain Lake, Obaghe Edeghere, Beatriz De La Iglesia, Olalekan Uthman
2019 PLoS ONE  
We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of  ...  In light of this, we set out to identify relevant tweets to collect a strong and reliable signal.  ...  Acknowledgments We acknowledge support from NHS 111 and NHS Digital for their assistance with the NHS 111 system; Out-of-Hours providers submitting data to the GPOOH syndromic surveillance and Advanced  ... 
doi:10.1371/journal.pone.0210689 pmid:31318885 pmcid:PMC6638773 fatcat:2kqtjnjrjvcdxcfqaur6765imu

Using Twitter To Generate Signals For The Enhancement Of Syndromic Surveillance Systems: Semi-Supervised Classification For Relevance Filtering in Syndromic Surveillance [article]

Oduwa Edo-Osagie, Gillian Smith, Iain Lake, Obaghe Edeghere, Beatriz De La Iglesia
2019 bioRxiv   pre-print
We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of  ...  In light of this, we set out to identify relevant tweets to collect a strong and reliable signal.  ...  Acknowledgments 768 We acknowledge support from NHS 111 and NHS Digital for their assistance and  ... 
doi:10.1101/511071 fatcat:sqxvehr6s5emznodd4orc7ol4e

An Analysis of Twitter Messages in the 2011 Tohoku Earthquake [chapter]

Son Doan, Bao-Khanh Ho Vo, Nigel Collier
2012 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
The results suggest that Twitter data can be used as a useful resource for tracking the public mood of populations affected by natural disasters as well as an early warning system.  ...  In this paper, we investigate over 1.5 million Twitter messages (tweets) for the period 9th March 2011 to 31st May 2011 in order to track awareness and anxiety levels in the Tokyo metropolitan district  ...  The resulting corpus had a total of 48,870 tweets in English and 1,611,753 tweets in Japanese.  ... 
doi:10.1007/978-3-642-29262-0_8 fatcat:ef3cmsgzzvcmhdd5yvsi3wx5xi

Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective [article]

Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris, C Raina MacIntyre
2019 arXiv   pre-print
We view past work in terms of two broad categories: health mention classification (selecting relevant text from a large volume) and health event detection (predicting epidemic events from a collection  ...  The survey also provides details of the state-of-the-art in annotation techniques, resources and evaluation strategies for epidemic intelligence.  ...  Construction of a personal experience tweet corpus for health surveillance. In Proceedings of the 15th Workshop on Biomedical Natural Language Processing, pages 128-135.  ... 
arXiv:1903.05801v1 fatcat:ga75672fcfdtzggj6gqhvc6ule

Mining Social Media Streams to Improve Public Health Allergy Surveillance

Kathy Lee, Ankit Agrawal, Alok Choudhary
2015 Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 - ASONAM '15  
With the prevalence of social media, people sharing experiences and opinions on personal health symptoms and concerns on social media are increasing.  ...  In this paper, we propose a real-time allergy surveillance system that first classifies tweets to identify those that mention actual allergy incidents using bag-of-words model and NaiveBayesMultinomial  ...  From our allergyrelated tweet corpus, we extracted most frequently occurring 2-grams where the second word is 'allergy'. N-gram is a contiguous sequence of n words in a sequence of text.  ... 
doi:10.1145/2808797.2808896 dblp:conf/asunam/LeeAC15 fatcat:6uwjjdalzvghlnbub4lmr5w7he

Building a semantically annotated corpus for chronic disease complications using two document types

Noha Alnazzawi, Nicolas Fiorini
2021 PLoS ONE  
as 0.60 and 0.75 for EHRs and tweets, respectively.  ...  In addition, people use Twitter to express their experiences regarding personal health issues, such as medical complaints, symptoms, treatments, lifestyle, and other factors.  ...  Methods A. Corpus construction The PrevComp corpus consists of two document types: EHRs and tweets. The EHRs are a subset of the i2b2 heart risk factor EHR challenge [14] .  ... 
doi:10.1371/journal.pone.0247319 pmid:33735207 fatcat:2ymazoqfofcslgjhcr7hjalk6u

Identifying Methods for Monitoring Foodborne Illness: Review of Existing Public Health Surveillance Techniques

Rachel A Oldroyd, Michelle A Morris, Mark Birkin
2018 JMIR Public Health and Surveillance  
Methods: Structured scoping methods were used to identify and analyze primary research papers using consumer-generated data for disease or public health surveillance.  ...  Objective: The aim of the study was to identify and formally analyze research papers using consumer-generated data, such as social media data or restaurant reviews, to quantify a disease or public health  ...  Each tweet was assumed to represent a report of first-person illness, and an ILI rate was calculated based on the number of reports.  ... 
doi:10.2196/publichealth.8218 pmid:29875090 pmcid:PMC6010836 fatcat:ltrf6lh7gzbmbp2qhxj2dwfxke

Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus (Preprint)

Ashlynn R Daughton, Michael J Paul
2018 Journal of Medical Internet Research  
An estimated 3.9 billion individuals live in a location endemic for common mosquito-borne diseases.  ...  The emergence of Zika virus in South America in 2015 marked the largest known Zika outbreak and caused hundreds of thousands of infections.  ...  Further, this is a common method used in other health surveillance work (e.g., [9, 49] ). 20% of the initial dataset was reserved for testing.  ... 
doi:10.2196/13090 pmid:31094347 pmcid:PMC6535980 fatcat:w4mza6kdtrfyhheht3uduzx3gi
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