Applying Survival Analysis and Count Models to Twitter Data

Congjian Liu; Jingjing Yin; Isaac Chun-Hai Fung; Lindsay A. Mullican

Applying Survival Analysis and Count Models to Twitter Data

Congjian Liu, Jingjing Yin, Isaac Chun-Hai Fung, Lindsay A. Mullican

Biostatistics, Epidemiology & Environmental Health Sciences

Georgia Southern University

Research output: Contribution to conference › Presentation

Abstract

Twitter has a variety of information on it, health topic is one of the popular categories. We used a collection of almost 40,000 tweets extracted from Twitter with #blood pressure from January, 2014 to April, 2015 to investigate the potentially associated factors for popularity (measured by the number of retweet) as well as the survival of tweets (measured by the time frame from the first post to its last retweet). We have found the appearance of a few hashtags significantly decreased the survival of tweets. Furthermore, these hashtags increase( but some decrease) the odds of being retweeted. And other factors significantly associated with the odds include actor's friends count, actor's follower's count, actor's listed count and so on. We explored our results using R, the results do not highlight the potential of hashtag in the application of twitter.

Original language	American English
State	Published - Mar 26 2018
Event	Eastern North American Region International Biometric Society (ENAR) - Duration: Mar 25 2018 → …

Conference

Conference	Eastern North American Region International Biometric Society (ENAR)
Period	03/25/18 → …

Disciplines

Biostatistics
Public Health

Keywords

Count Models
Survival Analysis
Twitter

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

https://www.enar.org/meetings/spring2018/program/Abstracts.pdf

Cite this

@conference{3160ffc250a14fbaafb06d34cd94cb40,

title = "Applying Survival Analysis and Count Models to Twitter Data",

abstract = " Twitter has a variety of information on it, health topic is one of the popular categories. We used a collection of almost 40,000 tweets extracted from Twitter with #blood pressure from January, 2014 to April, 2015 to investigate the potentially associated factors for popularity (measured by the number of retweet) as well as the survival of tweets (measured by the time frame from the first post to its last retweet). We have found the appearance of a few hashtags significantly decreased the survival of tweets. Furthermore, these hashtags increase( but some decrease) the odds of being retweeted. And other factors significantly associated with the odds include actor's friends count, actor's follower's count, actor's listed count and so on. We explored our results using R, the results do not highlight the potential of hashtag in the application of twitter.",

keywords = "Count Models, Survival Analysis, Twitter",

author = "Congjian Liu and Jingjing Yin and Fung, {Isaac Chun-Hai} and Mullican, {Lindsay A.}",

year = "2018",

month = mar,

day = "26",

language = "American English",

note = "Eastern North American Region International Biometric Society (ENAR) ; Conference date: 25-03-2018",

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TY - CONF

T1 - Applying Survival Analysis and Count Models to Twitter Data

AU - Liu, Congjian

AU - Yin, Jingjing

AU - Fung, Isaac Chun-Hai

AU - Mullican, Lindsay A.

PY - 2018/3/26

Y1 - 2018/3/26

N2 - Twitter has a variety of information on it, health topic is one of the popular categories. We used a collection of almost 40,000 tweets extracted from Twitter with #blood pressure from January, 2014 to April, 2015 to investigate the potentially associated factors for popularity (measured by the number of retweet) as well as the survival of tweets (measured by the time frame from the first post to its last retweet). We have found the appearance of a few hashtags significantly decreased the survival of tweets. Furthermore, these hashtags increase( but some decrease) the odds of being retweeted. And other factors significantly associated with the odds include actor's friends count, actor's follower's count, actor's listed count and so on. We explored our results using R, the results do not highlight the potential of hashtag in the application of twitter.

AB - Twitter has a variety of information on it, health topic is one of the popular categories. We used a collection of almost 40,000 tweets extracted from Twitter with #blood pressure from January, 2014 to April, 2015 to investigate the potentially associated factors for popularity (measured by the number of retweet) as well as the survival of tweets (measured by the time frame from the first post to its last retweet). We have found the appearance of a few hashtags significantly decreased the survival of tweets. Furthermore, these hashtags increase( but some decrease) the odds of being retweeted. And other factors significantly associated with the odds include actor's friends count, actor's follower's count, actor's listed count and so on. We explored our results using R, the results do not highlight the potential of hashtag in the application of twitter.

KW - Count Models

KW - Survival Analysis

KW - Twitter

UR - https://digitalcommons.georgiasouthern.edu/biostat-facpres/114

UR - https://www.enar.org/meetings/spring2018/program/Abstracts.pdf

M3 - Presentation

T2 - Eastern North American Region International Biometric Society (ENAR)

Y2 - 25 March 2018

ER -

Applying Survival Analysis and Count Models to Twitter Data

Abstract

Conference

Disciplines

Keywords

UN SDGs

Access to Document

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Cite this