Creating a metamodel based on machine learning to identify the sentiment of vaccine and disease-related messages in Twitter: the MAVIS study

被引：2

作者：

Rodriguez-Gonzalez, Alejandro ^{[1
,2
]}

Manuel Tunas, Juan ^{[2
]}

Fernandez Peces-Barba, Diego ^{[2
]}

Menasalvas-Ruiz, Ernestina ^{[1
,2
]}

Jaramillo, Almudena ^{[3
]}

Cotarelo, Manuel ^{[3
]}

Conejo, Antonio ^{[4
]}

Arce, Amalia ^{[5
]}

Gil, Angel ^{[5
]}

机构：

[1] Univ Politecn Madrid, Ctr Tecnol Biomed, Madrid, Spain

[2] Univ Politecn Madrid, ETS Ingenieros Informat, Madrid, Spain

[3] MSD Espana, Madrid, Spain

[4] Hosp Vithas Xanit Int, Malaga, Spain

[5] Fundacio Hosp Nens, Barcelona, Spain

来源：

2020 IEEE 33RD INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS(CBMS 2020) | 2020年

关键词：

twitter; vaccine; machine learning; sentiment analysis; metamodel; FACEBOOK; INFORMATION; INTERNET;

D O I：

10.1109/CBMS49503.2020.00053

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

MAVIS was a project that aimed to study the interactions in social networks (Twitter and Instagram) between users regarding the sentiment expressed in their messages when they talked about specific vaccines or diseases. The study was performed during the period 2015-2018 and was initially technically done by using a set of commercial tools to identify the polarity of the messages. With the aim of improving the results provided by such tools, we performed a deep analysis of the results from such tools and provide a machine learning method as a metamodel over the results of the commercial tools. In this paper we explain both the technical process performed together with the main results that were obtained.

引用

页码：245 / 250

页数：6

共 33 条

[1] Publicly Available Online Tool Facilitates Real-Time Monitoring Of Vaccine Conversations And Sentiments
Bahk, Chi Y.
Cumming, Melissa
Paushter, Louisa
Madoff, Lawrence C.
Thomson, Angus
Brownstein, John S.
[J]. HEALTH AFFAIRS, 2016, 35 (02) : 341 - 347
[2] Evaluation of a multinational, multilingual vaccine debate on Twitter
Becker, Benedikt F. H.
Larson, Heidi J.
Bonhoeffer, Jan
van Mulligen, Erik M.
Kors, Jan A.
Sturkenboom, Miriam C. J. M.
[J]. VACCINE, 2016, 34 (50) : 6166 - 6171
[3] Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
[4] The Digital Architectures of Social Media: Comparing Political Campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 US Election
Bossetta, Michael
[J]. JOURNALISM & MASS COMMUNICATION QUARTERLY, 2018, 95 (02) : 471 - 496
[5] Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate
Broniatowski, David A.
Jamison, Amelia M.
Qi, SiHua
AlKulaib, Lulwah
Chen, Tao
Benton, Adrian
Quinn, Sandra C.
Dredze, Mark
[J]. AMERICAN JOURNAL OF PUBLIC HEALTH, 2018, 108 (10) : 1378 - 1384
[6] Chamlertwat W, 2012, J UNIVERS COMPUT SCI, V18, P973
[7] SMOTE: Synthetic minority over-sampling technique
Chawla, Nitesh V.
Bowyer, Kevin W.
Hall, Lawrence O.
Kegelmeyer, W. Philip
[J]. 2002, American Association for Artificial Intelligence (16)
[8] Das TK, 2014, INT CONF COMP COMMUN
[9] Patients' use of the Internet for medical information
Diaz, JA
Griffith, RA
Ng, JJ
Reinert, SE
Friedmann, PD
Moulton, AW
[J]. JOURNAL OF GENERAL INTERNAL MEDICINE, 2002, 17 (03) : 180 - 185
[10] Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data
Du, Jingcheng
Xu, Jun
Song, Hsing-Yi
Tao, Cui
[J]. BMC MEDICAL INFORMATICS AND DECISION MAKING, 2017, 17

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