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Discovering health-related knowledge in social media using ensembles of heterogeneous features

Suppawong Tuarob, Conrad S. Tucker, Marcel Salathe, Nilam Ram
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
aspects of semantics for identification of health-related messages in social media. • Combine feature types using ensemble methods where each base classifier learns a different aspect of the data.  ...  • Contributions• Develop a public health surveillance system using the dynamic large scale availability of social media data. • Propose to use 5 heterogeneous feature types representing different  ... 
doi:10.1145/2505515.2505629 dblp:conf/cikm/TuarobTSR13 fatcat:t7cbqr7lgzgz5jrcwwwixq2ptq

A Survey and Analysis of Various Health-Related Knowledge Mining Techniques in Social Media

D. Krithika, B. Rosiline
2017 International Journal of Computer Applications  
Smart extraction of knowledge from social media has received the recent interest of the Biomedical and Health Informatics community for the simultaneous improvement of healthcare outcomes and lessen the  ...  Nonetheless, for conventional public health surveillance systems, it is difficult to detect and then monitor the concerns related to health and the changes seen in attitudes of the public towards health-related  ...  Trajectory model are used for discover the health-related knowledge from social media.  ... 
doi:10.5120/ijca2017912718 fatcat:ghl7nejyencsbgbfq7p4pf52hy

Modeling Health Care Q&A Questions with Ensemble Classification Approaches

Yi-Ling Lin, Cheng-Yu Chung, Che-Wei Kuo, Te-Ming Chang
2016 Americas Conference on Information Systems  
Although researchers have long examined ways to augment questions and answering systems in medical domain, most of them focused on clinicians' use of the systems rather than general users.  ...  This study examines (1) What are the features and classification techniques useful to predict whether a CQA question can obtain answers from experts?  ...  health safety information or health-related expression of consumers in social media (Konovalov, Scotch, Post, & Brandt, 2010; Qiu et al., 2011) .  ... 
dblp:conf/amcis/LinCKC16 fatcat:nlu6e3szq5h35ltg6uheypi5vy

Beyond the Words

Honghao Wei, Fuzheng Zhang, Nicholas Jing Yuan, Chuan Cao, Hao Fu, Xing Xie, Yong Rui, Wei-Ying Ma
2017 Proceedings of the Tenth ACM International Conference on Web Search and Data Mining - WSDM '17  
However, except for single language features, a less researched direction is how to leverage the heterogeneous information on social media to have a better understanding of user personality.  ...  In our framework, to improve the performance of personality prediction, we have designed different strategies extracting semantic representations to fully leverage heterogeneous information on social media  ...  To the best of our knowledge, little prior work considers user behavior patterns from the angle of diverse features in heterogeneous information on social media.  ... 
doi:10.1145/3018661.3018717 dblp:conf/wsdm/WeiZYCFXRM17 fatcat:q7cyxl6nwvexvk4n3fp7xgjtpe

Harnessing social media for health information management

Lina Zhou, Dongsong Zhang, Christopher C. Yang, Yu Wang
2018 Electronic Commerce Research and Applications  
Social media are reshaping health information management in a variety of ways, ranging from providing cost-effective ways to improve clinician-patient communication and exchange health-related information  ...  The remarkable upsurge of social media has dramatic impacts on health care research and practice in the past decade.  ...  Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the above funding agencies.  ... 
doi:10.1016/j.elerap.2017.12.003 pmid:30147636 pmcid:PMC6105292 fatcat:p6uquchmavctdh33jmqpbv4iga

Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being [article]

Pablo Robles-Granda, Suwen Lin, Xian Wu, Sidney D'Mello, Gonzalo J. Martinez, Koustuv Saha, Kari Nies, Gloria Mark, Andrew T. Campbell, Munmun De Choudhury, Anind D. Dey, Julie Gregg (+6 others)
2020 arXiv   pre-print
and social media sources.  ...  Assessment of job performance, personalized health and psychometric measures are domains where data-driven and ubiquitous computing exhibits the potential of a profound impact in the future.  ...  Model Selection Using the elements described so far, we build the components of the ensemble learning model by combining the HON features (heart and stress), heart rate, social media, beacons, phone agent  ... 
arXiv:2006.08364v1 fatcat:ibk7rfug2bhipmkcae7jpzbgfy

DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

Suppawong Tuarob, Poom Wettayakorn, Ponpat Phetchai, Siripong Traivijitkhun, Sunghoon Lim, Thanapon Noraset, Tipajin Thaipisutikul
2021 Financial Innovation  
articles and social media data.  ...  news articles, social media, and company technical information.  ...  RSA6280105, funded by Thailand Science Research and Innovation (TSRI), (formerly known as the Thailand Research Fund (TRF)), and the National Research Council of Thailand (NRCT).  ... 
doi:10.1186/s40854-021-00269-7 fatcat:6ur53ktpezg4teriawmqlpk4zu

2018 Index IEEE Transactions on Knowledge and Data Engineering Vol. 30

2019 IEEE Transactions on Knowledge and Data Engineering  
., þ, TKDE April 2018 703-716 H Health care Health Monitoring on Social Media over Time.  ...  ., þ, TKDE July 2018 1352-1365 Heterogeneous networks Ensemble Learning for Multi-Type Classification in Heterogeneous Networks.  ... 
doi:10.1109/tkde.2018.2882359 fatcat:asiids266jagrkx5eac6higrlq

An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

Sultan Noman Qasem, Mohammed Al-Sarem, Faisal Saeed
2022 Computers Materials & Continua  
During COVID 19, people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media.  ...  A new detection model was used which combined two models: The Genetic Algorithm Based Support Vector Machine (that works on users' and tweets' features) and the stacking ensemble method (that works on  ...  Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/cmc.2022.018972 fatcat:bvpg3h6td5g5ndnpgbdzqbann4

#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs [article]

Yichao Zhou, Jyun-yu Jiang, Xiusi Chen, Wei Wang
2021 arXiv   pre-print
To take advantage of the social media data, we propose a novel framework, Social Media enhAnced pandemic suRveillance Technique (SMART), which is composed of two modules: (i) information extraction module  ...  COVID-19 has caused lasting damage to almost every domain in public health, society, and economy.  ...  This work was partially supported by the National Science Foundation [NSF-DGE-1829071, NSF-IIS-2031187] and the National Institutes of Health [NIH-R35-HL135772, NIH/NIBIB-R01-EB027650].  ... 
arXiv:2108.03670v1 fatcat:fa5ifyhn5bdzdo4igujrsxik6e

A Survey on Food Computing [article]

Weiqing Min and Shuqiang Jiang and Linhu Liu and Yong Rui and Ramesh Jain
2019 arXiv   pre-print
working in different food-related fields.  ...  Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing.  ...  Recent studies have shown that we can use social media to get aggregating statistics about the health of people for public health monitoring.  ... 
arXiv:1808.07202v5 fatcat:qjitfexaffd3fohfb7iy3lwfyi

Modern Views of Machine Learning for Precision Psychiatry [article]

Zhe Sage Chen, Prathamesh Kulkarni, Isaac R. Galatzer-Levy, Benedetta Bigio, Carla Nasca, Yu Zhang
2022 arXiv   pre-print
Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health.  ...  Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry.  ...  Acknowledgments The research was partially supported from the US National Science Foundation (CBET-1835000 to Z.S.C.), the National Institutes of Health (R01-NS121776 and R01-MH118928 to Z.S.C.).  ... 
arXiv:2204.01607v2 fatcat:coo557v2jzh6debycy3mhccfze

Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion [article]

Chuanbo Hu, Minglei Yin, Bin Liu, Xin Li, Yanfang Ye
2021 arXiv   pre-print
Illicit drug trafficking via social media sites such as Instagram has become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies.  ...  Moreover, we have developed a hashtag-based community detection technique for discovering evolving patterns, especially those related to geography and drug types.  ...  Social media and suicide: a public health perspective.  ... 
arXiv:2108.08301v2 fatcat:r5omsmxaenfslcy6zdkt427ggq

Proceedings of Symposium on Data Mining Applications 2014 [article]

Basit Qureshi, Yasir Javed
2020 arXiv   pre-print
SDMA is organized by MEGDAM to advance the state of the art in data mining research field and its various real world applications.  ...  The Symposium on Data Mining and Applications (SDMA 2014) is aimed to gather researchers and application developers from a wide range of data mining related areas such as statistics, computational intelligence  ...  content in the world of social media.  ... 
arXiv:2001.11324v1 fatcat:ezplxohltvbvrggurw3hdkdhz4

Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media [article]

Hamad Zogan, Imran Razzak, Xianzhi Wang, Shoaib Jameel, Guandong Xu
2021 arXiv   pre-print
We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media.  ...  In this work, we propose interpretive Multi-Modal Depression Detection with Hierarchical Attention Network MDHAN, for detection depressed users on social media and explain the model prediction.  ...  ., [48] study the problem of early detection of depression from social media using deep learning where the leverage different word embeddings in an ensemble-based learning setup.  ... 
arXiv:2007.02847v2 fatcat:4y4xl7rysfdqtfj3pojqxk6zo4
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