Utilizing Twitter data for analysis of chemotherapy

Research output: Contribution to journalArticle

Abstract

Objective: Twitter has become one of the most popular social media platforms that offers real-world insights to healthy behaviors. The purpose of this study was to assess and compare perceptions about chemotherapy of patients and health-care providers through analysis of chemo-related tweets. Materials and methods: Cancer-related Twitter accounts and their tweets were obtained through using Tweepy (Python library). Multiple text classification algorithms were tested to identify the models with best performance in classifying the accounts into individual and organization. Chemotherapy-specific tweets were extracted from historical tweetset, and the content of these tweets was analyzed using topic model, sentiment analysis and word co-occurrence network. Results: Using the description in Twitter users’ profiles, the accounts related with cancer were collected and coded as individual or organization. We employed Long Short Term Memory (LSTM) network with GloVe word embeddings to identify the user into individuals and organizations with accuracy of 85.2%. 13, 273 and 14,051 publicly available chemotherapy-related tweets were retrieved from individuals and organizations, respectively. The content of the chemo-related tweets was analyzed by text mining approaches. The tweets from individual accounts pertained to personal chemotherapy experience and emotions. In contrast with the personal users, professional accounts had a higher proportion of neutral tweets about side effects. The information about the assessment of response to chemotherapy was deficient from organizations on Twitter. Discussion: Examining chemotherapy discussions on Twitter provide new lens into content and behavioral patterns associated with treatments for cancer patients. The methodology described herein allowed us to collect relatively large number of health-related tweets over a greater time period and exploit the potential power of social media, which provide comprehensive view on patients’ perceptions of chemotherapy. Conclusion: This study sheds light on using Twitter data as a valuable healthcare data source for helping oncologists (organizations) in understanding patients’ experiences while undergoing chemotherapy, in developing personalize therapy plans, and a supplement to the clinical electronic medical records (EMRs).

Original languageEnglish (US)
Pages (from-to)92-100
Number of pages9
JournalInternational Journal of Medical Informatics
Volume120
DOIs
StatePublished - Dec 2018

Fingerprint

Drug Therapy
Social Media
Boidae
Data Mining
Second Primary Neoplasms
Long-Term Memory
Electronic Health Records
Information Storage and Retrieval
Short-Term Memory
Health Personnel
Lenses
Libraries
Neoplasms
Patient Care
Emotions
Delivery of Health Care
Health
Therapeutics

Keywords

  • Cancer
  • Chemotherapy
  • Deep learning
  • Side effect
  • Social media
  • Twitter

ASJC Scopus subject areas

  • Health Informatics

Cite this

Utilizing Twitter data for analysis of chemotherapy. / Zhang, Ling; Hall, Magie; Bastola, Dhundy.

In: International Journal of Medical Informatics, Vol. 120, 12.2018, p. 92-100.

Research output: Contribution to journalArticle

@article{c3b0aef811a643c3a69a97ba2a708a84,
title = "Utilizing Twitter data for analysis of chemotherapy",
abstract = "Objective: Twitter has become one of the most popular social media platforms that offers real-world insights to healthy behaviors. The purpose of this study was to assess and compare perceptions about chemotherapy of patients and health-care providers through analysis of chemo-related tweets. Materials and methods: Cancer-related Twitter accounts and their tweets were obtained through using Tweepy (Python library). Multiple text classification algorithms were tested to identify the models with best performance in classifying the accounts into individual and organization. Chemotherapy-specific tweets were extracted from historical tweetset, and the content of these tweets was analyzed using topic model, sentiment analysis and word co-occurrence network. Results: Using the description in Twitter users’ profiles, the accounts related with cancer were collected and coded as individual or organization. We employed Long Short Term Memory (LSTM) network with GloVe word embeddings to identify the user into individuals and organizations with accuracy of 85.2{\%}. 13, 273 and 14,051 publicly available chemotherapy-related tweets were retrieved from individuals and organizations, respectively. The content of the chemo-related tweets was analyzed by text mining approaches. The tweets from individual accounts pertained to personal chemotherapy experience and emotions. In contrast with the personal users, professional accounts had a higher proportion of neutral tweets about side effects. The information about the assessment of response to chemotherapy was deficient from organizations on Twitter. Discussion: Examining chemotherapy discussions on Twitter provide new lens into content and behavioral patterns associated with treatments for cancer patients. The methodology described herein allowed us to collect relatively large number of health-related tweets over a greater time period and exploit the potential power of social media, which provide comprehensive view on patients’ perceptions of chemotherapy. Conclusion: This study sheds light on using Twitter data as a valuable healthcare data source for helping oncologists (organizations) in understanding patients’ experiences while undergoing chemotherapy, in developing personalize therapy plans, and a supplement to the clinical electronic medical records (EMRs).",
keywords = "Cancer, Chemotherapy, Deep learning, Side effect, Social media, Twitter",
author = "Ling Zhang and Magie Hall and Dhundy Bastola",
year = "2018",
month = "12",
doi = "10.1016/j.ijmedinf.2018.10.002",
language = "English (US)",
volume = "120",
pages = "92--100",
journal = "International Journal of Medical Informatics",
issn = "1386-5056",
publisher = "Elsevier Ireland Ltd",

}

TY - JOUR

T1 - Utilizing Twitter data for analysis of chemotherapy

AU - Zhang, Ling

AU - Hall, Magie

AU - Bastola, Dhundy

PY - 2018/12

Y1 - 2018/12

N2 - Objective: Twitter has become one of the most popular social media platforms that offers real-world insights to healthy behaviors. The purpose of this study was to assess and compare perceptions about chemotherapy of patients and health-care providers through analysis of chemo-related tweets. Materials and methods: Cancer-related Twitter accounts and their tweets were obtained through using Tweepy (Python library). Multiple text classification algorithms were tested to identify the models with best performance in classifying the accounts into individual and organization. Chemotherapy-specific tweets were extracted from historical tweetset, and the content of these tweets was analyzed using topic model, sentiment analysis and word co-occurrence network. Results: Using the description in Twitter users’ profiles, the accounts related with cancer were collected and coded as individual or organization. We employed Long Short Term Memory (LSTM) network with GloVe word embeddings to identify the user into individuals and organizations with accuracy of 85.2%. 13, 273 and 14,051 publicly available chemotherapy-related tweets were retrieved from individuals and organizations, respectively. The content of the chemo-related tweets was analyzed by text mining approaches. The tweets from individual accounts pertained to personal chemotherapy experience and emotions. In contrast with the personal users, professional accounts had a higher proportion of neutral tweets about side effects. The information about the assessment of response to chemotherapy was deficient from organizations on Twitter. Discussion: Examining chemotherapy discussions on Twitter provide new lens into content and behavioral patterns associated with treatments for cancer patients. The methodology described herein allowed us to collect relatively large number of health-related tweets over a greater time period and exploit the potential power of social media, which provide comprehensive view on patients’ perceptions of chemotherapy. Conclusion: This study sheds light on using Twitter data as a valuable healthcare data source for helping oncologists (organizations) in understanding patients’ experiences while undergoing chemotherapy, in developing personalize therapy plans, and a supplement to the clinical electronic medical records (EMRs).

AB - Objective: Twitter has become one of the most popular social media platforms that offers real-world insights to healthy behaviors. The purpose of this study was to assess and compare perceptions about chemotherapy of patients and health-care providers through analysis of chemo-related tweets. Materials and methods: Cancer-related Twitter accounts and their tweets were obtained through using Tweepy (Python library). Multiple text classification algorithms were tested to identify the models with best performance in classifying the accounts into individual and organization. Chemotherapy-specific tweets were extracted from historical tweetset, and the content of these tweets was analyzed using topic model, sentiment analysis and word co-occurrence network. Results: Using the description in Twitter users’ profiles, the accounts related with cancer were collected and coded as individual or organization. We employed Long Short Term Memory (LSTM) network with GloVe word embeddings to identify the user into individuals and organizations with accuracy of 85.2%. 13, 273 and 14,051 publicly available chemotherapy-related tweets were retrieved from individuals and organizations, respectively. The content of the chemo-related tweets was analyzed by text mining approaches. The tweets from individual accounts pertained to personal chemotherapy experience and emotions. In contrast with the personal users, professional accounts had a higher proportion of neutral tweets about side effects. The information about the assessment of response to chemotherapy was deficient from organizations on Twitter. Discussion: Examining chemotherapy discussions on Twitter provide new lens into content and behavioral patterns associated with treatments for cancer patients. The methodology described herein allowed us to collect relatively large number of health-related tweets over a greater time period and exploit the potential power of social media, which provide comprehensive view on patients’ perceptions of chemotherapy. Conclusion: This study sheds light on using Twitter data as a valuable healthcare data source for helping oncologists (organizations) in understanding patients’ experiences while undergoing chemotherapy, in developing personalize therapy plans, and a supplement to the clinical electronic medical records (EMRs).

KW - Cancer

KW - Chemotherapy

KW - Deep learning

KW - Side effect

KW - Social media

KW - Twitter

UR - http://www.scopus.com/inward/record.url?scp=85054782777&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054782777&partnerID=8YFLogxK

U2 - 10.1016/j.ijmedinf.2018.10.002

DO - 10.1016/j.ijmedinf.2018.10.002

M3 - Article

C2 - 30409350

AN - SCOPUS:85054782777

VL - 120

SP - 92

EP - 100

JO - International Journal of Medical Informatics

JF - International Journal of Medical Informatics

SN - 1386-5056

ER -