- Text
- History
ABSTRACTWe consider social media as
ABSTRACT
We consider social media as a promising tool for public health,
focusing on the use of Twitter posts to build predictive models about the influence of childbirth on the forthcoming behavior and mood of new mothers.
Using Twitter posts, we quantify postpartum changes in 376 mothers along dimensions of social engagement, emotion, social network, and linguistic style.
We then construct statistical models from a training set of observations of these measures before and after the reported childbirth,
to forecast significant postpartum changes in mothers.
The predictive models can classify mothers who will change significantly following childbirth with an accuracy of 71%, using observations about their prenatal behavior,
and as accurately as 80-83% when additionally leveraging the initial 2-3 weeks of postnatal data.
The study is motivated by the opportunity to use social media to identify mothers at risk of postpartum depression,
an underreported health concern among large populations, and to inform the design of low-cost,
privacy-sensitive early-warning systems and intervention programs aimed at promoting wellness postpartum.
We consider social media as a promising tool for public health,
focusing on the use of Twitter posts to build predictive models about the influence of childbirth on the forthcoming behavior and mood of new mothers.
Using Twitter posts, we quantify postpartum changes in 376 mothers along dimensions of social engagement, emotion, social network, and linguistic style.
We then construct statistical models from a training set of observations of these measures before and after the reported childbirth,
to forecast significant postpartum changes in mothers.
The predictive models can classify mothers who will change significantly following childbirth with an accuracy of 71%, using observations about their prenatal behavior,
and as accurately as 80-83% when additionally leveraging the initial 2-3 weeks of postnatal data.
The study is motivated by the opportunity to use social media to identify mothers at risk of postpartum depression,
an underreported health concern among large populations, and to inform the design of low-cost,
privacy-sensitive early-warning systems and intervention programs aimed at promoting wellness postpartum.
0/5000
ABSTRACTWe consider social media as a promising tool for public health, focusing on the use of Twitter posts to build predictive models about the influence of childbirth on the forthcoming behavior and mood of new mothers. Using Twitter posts, we quantify postpartum changes in 376 mothers along dimensions of social engagement, emotion, social network, and linguistic style. We then construct statistical models from a training set of observations of these measures before and after the reported childbirth, to forecast significant postpartum changes in mothers. The predictive models can classify mothers who will change significantly following childbirth with an accuracy of 71%, using observations about their prenatal behavior, and as accurately as 80-83% when additionally leveraging the initial 2-3 weeks of postnatal data. The study is motivated by the opportunity to use social media to identify mothers at risk of postpartum depression, an underreported health concern among large populations, and to inform the design of low-cost, privacy-sensitive early-warning systems and intervention programs aimed at promoting wellness postpartum.
Being translated, please wait..
ABSTRACT
We consider Social Media As a Promising Tool for public Health,
Focusing on The Use of Twitter posts to Build predictive models About The influence of Childbirth on The forthcoming behavior and mood of New Mothers.
Using Twitter posts, we quantify postpartum Changes in 376 Mothers. Along dimensions of Social engagement, Emotion, Social Network, and linguistic style.
We then Construct statistical models from a Training Set of observations of these Measures Before and After The Reported Childbirth,
to forecast significant postpartum Changes in Mothers.
The predictive models Can CLASSIFY Mothers. Who Will Change significantly following Childbirth with an accuracy of 71%, using their observations About Prenatal behavior,
and As accurately As 80-83% When Additionally leveraging 2-3 weeks of postnatal The Initial Data.
The Study is motivated by The Opportunity to Use. Social Media to Identify Mothers at risk of postpartum depression,
an underreported Health Concern Among Large populations, and to Inform The Design of Low-Cost,
privacy-sensitive Early-Warning Systems and Intervention programs aimed at Promoting Wellness postpartum.
We consider Social Media As a Promising Tool for public Health,
Focusing on The Use of Twitter posts to Build predictive models About The influence of Childbirth on The forthcoming behavior and mood of New Mothers.
Using Twitter posts, we quantify postpartum Changes in 376 Mothers. Along dimensions of Social engagement, Emotion, Social Network, and linguistic style.
We then Construct statistical models from a Training Set of observations of these Measures Before and After The Reported Childbirth,
to forecast significant postpartum Changes in Mothers.
The predictive models Can CLASSIFY Mothers. Who Will Change significantly following Childbirth with an accuracy of 71%, using their observations About Prenatal behavior,
and As accurately As 80-83% When Additionally leveraging 2-3 weeks of postnatal The Initial Data.
The Study is motivated by The Opportunity to Use. Social Media to Identify Mothers at risk of postpartum depression,
an underreported Health Concern Among Large populations, and to Inform The Design of Low-Cost,
privacy-sensitive Early-Warning Systems and Intervention programs aimed at Promoting Wellness postpartum.
Being translated, please wait..
ABSTRACT
We consider social media as a promising tool for, public health
focusing on the use of Twitter posts to build. Predictive models about the influence of childbirth on the forthcoming behavior and mood of new mothers.
Using Twitter. Posts we quantify, postpartum changes in 376 mothers along dimensions of social engagement emotion social network and,,,, Linguistic style.
.We then construct statistical models from a training set of observations of these measures before and after the reported. Childbirth
to, forecast significant postpartum changes in mothers.
The predictive models can classify mothers who will. Change significantly following childbirth with an accuracy of 71% using observations, about their, prenatal behavior
.And as accurately as 80-83% when additionally leveraging the initial 2 - 3 weeks of postnatal data.
The study is motivated. By the opportunity to use social media to identify mothers at risk of postpartum depression
an, underreported health concern. Among large populations and to, inform the design, of low-cost
.Privacy-sensitive early - warning systems and intervention programs aimed at promoting wellness postpartum.
.
We consider social media as a promising tool for, public health
focusing on the use of Twitter posts to build. Predictive models about the influence of childbirth on the forthcoming behavior and mood of new mothers.
Using Twitter. Posts we quantify, postpartum changes in 376 mothers along dimensions of social engagement emotion social network and,,,, Linguistic style.
.We then construct statistical models from a training set of observations of these measures before and after the reported. Childbirth
to, forecast significant postpartum changes in mothers.
The predictive models can classify mothers who will. Change significantly following childbirth with an accuracy of 71% using observations, about their, prenatal behavior
.And as accurately as 80-83% when additionally leveraging the initial 2 - 3 weeks of postnatal data.
The study is motivated. By the opportunity to use social media to identify mothers at risk of postpartum depression
an, underreported health concern. Among large populations and to, inform the design, of low-cost
.Privacy-sensitive early - warning systems and intervention programs aimed at promoting wellness postpartum.
.
Being translated, please wait..
Other languages
The translation tool support: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bosnian, Bulgarian, Catalan, Cebuano, Chichewa, Chinese, Chinese Traditional, Corsican, Croatian, Czech, Danish, Detect language, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Frisian, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Kinyarwanda, Klingon, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Myanmar (Burmese), Nepali, Norwegian, Odia (Oriya), Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scots Gaelic, Serbian, Sesotho, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Tatar, Telugu, Thai, Turkish, Turkmen, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Welsh, Xhosa, Yiddish, Yoruba, Zulu, Language translation.
- My friend Pick up me
- Jaryakat a zem. Daryeet acza
- ابعث لي موقع العمل عل ماجلان
- Te ago videollamada
- คุณอยุ่คนเดียวเหงาไหม
- security clearence for badge to go into
- Allotment alert level
- ฉันต้องนอน
- Por wester union
- Oligomers prepared under optimized condi
- I have to shooting today
- พ่อและแม่ปลูกต้นไม้แต่ลูกและหลานดูโทรทัศ
- Ya que saque el dinero
- มารับฉันเร็ว
- enzyme concentration
- แล้วอีกอย่าง ฉันขอแนะนำให้รู้จักกับคริส
- And you
- Pick me fast
- Jaryakat a zem. Daryeet acza
- Pick me early
- ทำงานเสร็จหรือยัง
- My intentions aren't really pure though
- ขอโทษที่ไม่ได้ตอบคุณพอดีฉันเหนื่อยจากการ
- Fast Pick me
