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BACKGROUND: Researchers have used traditional databases to study public health for decades. Less is known about the use of social media data sources, such as Twitter, for this purpose. OBJECTIVES: To systematically review the use of Twitter in health research, define a taxonomy to describe Twitter use, and characterize the current state of Twitter in health research. SEARCH METHODS: We performed a literature search in PubMed, Embase, Web of Science, Google Scholar, and CINAHL through September 2015. SELECTION CRITERIA: We searched for peer-reviewed original research studies that primarily used Twitter for health research. DATA COLLECTION AND ANALYSIS: Two authors independently screened studies and abstracted data related to the approach to analysis of Twitter data, methodology used to study Twitter, and current state of Twitter research by evaluating time of publication, research topic, discussion of ethical concerns, and study funding source. MAIN RESULTS: Of 1110 unique health-related articles mentioning Twitter, 137 met eligibility criteria. The primary approaches for using Twitter in health research that constitute a new taxonomy were content analysis (56%; n = 77), surveillance (26%; n = 36), engagement (14%; n = 19), recruitment (7%; n = 9), intervention (7%; n = 9), and network analysis (4%; n = 5). These studies collectively analyzed more than 5 billion tweets primarily by using the Twitter application program interface. Of 38 potential data features describing tweets and Twitter users, 23 were reported in fewer than 4% of the articles. The Twitter-based studies in this review focused on a small subset of data elements including content analysis, geotags, and language. Most studies were published recently (33% in 2015). Public health (23%; n = 31) and infectious disease (20%; n = 28) were the research fields most commonly represented in the included studies. Approximately one third of the studies mentioned ethical board approval in their articles. Primary funding sources included federal (63%), university (13%), and foundation (6%). CONCLUSIONS: We identified a new taxonomy to describe Twitter use in health research with 6 categories. Many data elements discernible from a user's Twitter profile, especially demographics, have been underreported in the literature and can provide new opportunities to characterize the users whose data are analyzed in these studies. Twitter-based health research is a growing field funded by a diversity of organizations. Public health implications. Future work should develop standardized reporting guidelines for health researchers who use Twitter and policies that address privacy and ethical concerns in social media research.
BACKGROUND: Researchers have used traditional databases to study public health for decades. Less is known about the use of social media data sources, such as Twitter, for this purpose. OBJECTIVES: To systematically review the use of Twitter in health research, define a taxonomy to describe Twitter use, and characterize the current state of Twitter in health research. SEARCH METHODS: We performed a literature search in PubMed, Embase, Web of Science, Google Scholar, and CINAHL through September 2015. SELECTION CRITERIA: We searched for peer-reviewed original research studies that primarily used Twitter for health research. DATA COLLECTION AND ANALYSIS: Two authors independently screened studies and abstracted data related to the approach to analysis of Twitter data, methodology used to study Twitter, and current state of Twitter research by evaluating time of publication, research topic, discussion of ethical concerns, and study funding source. MAIN RESULTS: Of 1110 unique health-related articles mentioning Twitter, 137 met eligibility criteria. The primary approaches for using Twitter in health research that constitute a new taxonomy were content analysis (56%; n = 77), surveillance (26%; n = 36), engagement (14%; n = 19), recruitment (7%; n = 9), intervention (7%; n = 9), and network analysis (4%; n = 5). These studies collectively analyzed more than 5 billion tweets primarily by using the Twitter application program interface. Of 38 potential data features describing tweets and Twitter users, 23 were reported in fewer than 4% of the articles. The Twitter-based studies in this review focused on a small subset of data elements including content analysis, geotags, and language. Most studies were published recently (33% in 2015). Public health (23%; n = 31) and infectious disease (20%; n = 28) were the research fields most commonly represented in the included studies. Approximately one third of the studies mentioned ethical board approval in their articles. Primary funding sources included federal (63%), university (13%), and foundation (6%). CONCLUSIONS: We identified a new taxonomy to describe Twitter use in health research with 6 categories. Many data elements discernible from a user's Twitter profile, especially demographics, have been underreported in the literature and can provide new opportunities to characterize the users whose data are analyzed in these studies. Twitter-based health research is a growing field funded by a diversity of organizations. Public health implications. Future work should develop standardized reporting guidelines for health researchers who use Twitter and policies that address privacy and ethical concerns in social media research.
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Detect language
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Translate from
Detect language
All languages
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Albanian
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Bambara
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Catalan
Cebuano
Chichewa
Chinese (Simplified)
Chinese (Traditional)
Corsican
Croatian
Czech
Danish
Dhivehi
Dogri
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Estonian
Ewe
Filipino
Finnish
French
Frisian
Galician
Georgian
German
Greek
Guarani
Gujarati
Haitian Creole
Hausa
Hawaiian
Hebrew
Hindi
Hmong
Hungarian
Icelandic
Igbo
Ilocano
Indonesian
Irish
Italian
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Javanese
Kannada
Kazakh
Khmer
Kinyarwanda
Konkani
Korean
Krio
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Kurdish (Sorani)
Kyrgyz
Lao
Latin
Latvian
Lingala
Lithuanian
Luganda
Luxembourgish
Macedonian
Maithili
Malagasy
Malay
Malayalam
Maltese
Maori
Marathi
Meiteilon (Manipuri)
Mizo
Mongolian
Myanmar (Burmese)
Nepali
Norwegian
Odia (Oriya)
Oromo
Pashto
Persian
Polish
Portuguese
Punjabi
Quechua
Romanian
Russian
Samoan
Sanskrit
Scots Gaelic
Sepedi
Serbian
Sesotho
Shona
Sindhi
Sinhala
Slovak
Slovenian
Somali
Spanish
Sundanese
Swahili
Swedish
Tajik
Tamil
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Telugu
Thai
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Tsonga
Turkish
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Translate to
All languages
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Amharic
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Armenian
Assamese
Aymara
Azerbaijani
Bambara
Basque
Belarusian
Bengali
Bhojpuri
Bosnian
Bulgarian
Catalan
Cebuano
Chichewa
Chinese (Simplified)
Chinese (Traditional)
Corsican
Croatian
Czech
Danish
Dhivehi
Dogri
Dutch
English
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Estonian
Ewe
Filipino
Finnish
French
Frisian
Galician
Georgian
German
Greek
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Gujarati
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Hawaiian
Hebrew
Hindi
Hmong
Hungarian
Icelandic
Igbo
Ilocano
Indonesian
Irish
Italian
Japanese
Javanese
Kannada
Kazakh
Khmer
Kinyarwanda
Konkani
Korean
Krio
Kurdish (Kurmanji)
Kurdish (Sorani)
Kyrgyz
Lao
Latin
Latvian
Lingala
Lithuanian
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Macedonian
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Malagasy
Malay
Malayalam
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Maori
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Meiteilon (Manipuri)
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Mongolian
Myanmar (Burmese)
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Quechua
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Russian
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Scots Gaelic
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Serbian
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Sindhi
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Slovenian
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Sundanese
Swahili
Swedish
Tajik
Tamil
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Telugu
Thai
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Hungarian
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