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The Impact of Negative Twitter Feeds...

Scott, Kim Venitta.

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The Impact of Negative Twitter Feeds on Washington Metropolitan Area Transit Authority Ridership.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Impact of Negative Twitter Feeds on Washington Metropolitan Area Transit Authority Ridership./
Author:	Scott, Kim Venitta.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	155 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: A.
Contained By:	Dissertations Abstracts International80-12A.
Subject:	Business administration. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13883119
ISBN:	9781392160732

The Impact of Negative Twitter Feeds on Washington Metropolitan Area Transit Authority Ridership.
Scott, Kim Venitta.

The Impact of Negative Twitter Feeds on Washington Metropolitan Area Transit Authority Ridership. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 155 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: A.

Thesis (Ph.D.)--Hampton University, 2019.

This item must not be sold to any third party vendors.

Few studies have focused on the effects of Twitter data on public transportation. Of the studies, few examined the relationship between Twitter and public transportation ridership. Using data from Washington Metropolitan Area Transit Authority (WMATA) and unsuckdcmetro, the most influential consumer-run Twitter handle dedicated to WMATA, this study examined whether negative Twitter sentiment (average daily negative sentiment [ADNS]) and volume-related variables (i.e., tweets, retweets, and likes) affect the change in ridership after negative sentiment days. Tweets from unsuckdcmetro were collected for approximately one year and analyzed using linguistic inquiry and word count (LIWC) to determined days in which sentiment about WMATA was predominately negative. A total of 64 negative days were found. Ridership data were used to determine the percentage change in ridership (PCR) of Day +1, Day +2, Day +3, and Day +4 after negative sentiment day, which were used as the dependent variables.Both analysis of variance (ANOVA) and post-hoc tests were used to explore the relationships between the different levels of each independent variable (i.e., ADNS, the daily number of tweets, the daily number of retweets, and daily number of likes) and each of the dependent variables. The results showed that all variables, except the number of tweets, were statistically significant to the PCR, as the volume of each variable increased. Additional analysis was performed using linear regression models to determine if any of the independent variables could be used as a predictor of PCR. There was statistically significant regression in the PCR using ADNS values over 2.00 on Day +3. There were no significant predictions in PCR using any of the other independent variables.

ISBN: 9781392160732Subjects--Topical Terms:

3168311
Business administration.

The Impact of Negative Twitter Feeds on Washington Metropolitan Area Transit Authority Ridership.
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245 1 4 $a The Impact of Negative Twitter Feeds on Washington Metropolitan Area Transit Authority Ridership.
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300 $a 155 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: A.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Maheshwari, Sharad K.
502 $a Thesis (Ph.D.)--Hampton University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Few studies have focused on the effects of Twitter data on public transportation. Of the studies, few examined the relationship between Twitter and public transportation ridership. Using data from Washington Metropolitan Area Transit Authority (WMATA) and unsuckdcmetro, the most influential consumer-run Twitter handle dedicated to WMATA, this study examined whether negative Twitter sentiment (average daily negative sentiment [ADNS]) and volume-related variables (i.e., tweets, retweets, and likes) affect the change in ridership after negative sentiment days. Tweets from unsuckdcmetro were collected for approximately one year and analyzed using linguistic inquiry and word count (LIWC) to determined days in which sentiment about WMATA was predominately negative. A total of 64 negative days were found. Ridership data were used to determine the percentage change in ridership (PCR) of Day +1, Day +2, Day +3, and Day +4 after negative sentiment day, which were used as the dependent variables.Both analysis of variance (ANOVA) and post-hoc tests were used to explore the relationships between the different levels of each independent variable (i.e., ADNS, the daily number of tweets, the daily number of retweets, and daily number of likes) and each of the dependent variables. The results showed that all variables, except the number of tweets, were statistically significant to the PCR, as the volume of each variable increased. Additional analysis was performed using linear regression models to determine if any of the independent variables could be used as a predictor of PCR. There was statistically significant regression in the PCR using ADNS values over 2.00 on Day +3. There were no significant predictions in PCR using any of the other independent variables.
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