東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Brand messages on Twitter: Predictin...

Vargo, Chris J.

Linked to FindBook

Google Book

Amazon

博客來

Brand messages on Twitter: Predicting diffusion with textual characteristics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Brand messages on Twitter: Predicting diffusion with textual characteristics./
Author:	Vargo, Chris J.
Description:	126 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-09(E), Section: A.
Contained By:	Dissertation Abstracts International75-09A(E).
Subject:	Speech Communication. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3622497
ISBN:	9781303942860

Brand messages on Twitter: Predicting diffusion with textual characteristics.
Vargo, Chris J.

Brand messages on Twitter: Predicting diffusion with textual characteristics. - 126 p.

Source: Dissertation Abstracts International, Volume: 75-09(E), Section: A.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2014.

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

This dissertation assesses brand messages (i.e. tweets by a brand) on Twitter and the characteristics that predict the amount of engagement (a.k.a. interaction) a tweet receives. Attention is given to theories that speak to characteristics observable in text and how those characteristics affect retweet and favorite counts. Three key concepts include sentiment, arousal and concreteness. For positive sentiment, messages appeared overly positive, but still a small amount of the variance in favorites was explained. Very few tweets had strong levels of arousal, but positive arousal still explained a small amount of the variance in retweet counts. Despite research suggesting that concreteness would boost sharing and interest, concrete tweets were retweeted and shared less than vague tweets. Vagueness explained a small amount of the variance in retweet and favorite counts. The presence of hashtags and images boosted retweet and favorite counts, and also explained variance. Finally, characteristics of the brand itself (e.g. the number of followers the brand had, the number of users it followed and its overall reputation of the brand) boosted retweet and favorite counts, and also explained variance.

ISBN: 9781303942860Subjects--Topical Terms:

1017408
Speech Communication.

Brand messages on Twitter: Predicting diffusion with textual characteristics.
LDR:02219nmm a2200313 4500 001 2057732
005 20150619125624.5
008 170521s2014 ||||||||||||||||| ||eng d
020 $a 9781303942860
035 $a (MiAaPQ)AAI3622497
035 $a AAI3622497
040 $a MiAaPQ $c MiAaPQ
100 1 $a Vargo, Chris J. $3 3171613
245 1 0 $a Brand messages on Twitter: Predicting diffusion with textual characteristics.
300 $a 126 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-09(E), Section: A.
500 $a Adviser: Joe Bob Hester.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation assesses brand messages (i.e. tweets by a brand) on Twitter and the characteristics that predict the amount of engagement (a.k.a. interaction) a tweet receives. Attention is given to theories that speak to characteristics observable in text and how those characteristics affect retweet and favorite counts. Three key concepts include sentiment, arousal and concreteness. For positive sentiment, messages appeared overly positive, but still a small amount of the variance in favorites was explained. Very few tweets had strong levels of arousal, but positive arousal still explained a small amount of the variance in retweet counts. Despite research suggesting that concreteness would boost sharing and interest, concrete tweets were retweeted and shared less than vague tweets. Vagueness explained a small amount of the variance in retweet and favorite counts. The presence of hashtags and images boosted retweet and favorite counts, and also explained variance. Finally, characteristics of the brand itself (e.g. the number of followers the brand had, the number of users it followed and its overall reputation of the brand) boosted retweet and favorite counts, and also explained variance.
590 $a School code: 0153.
650 4 $a Speech Communication. $3 1017408
650 4 $a Business Administration, Marketing. $3 1017573
650 4 $a Sociology, Social Structure and Development. $3 1017425
650 4 $a Web Studies. $3 1026830
690 $a 0459
690 $a 0338
690 $a 0700
690 $a 0646
710 2 $a The University of North Carolina at Chapel Hill. $b Journalism (Mass Communication). $3 2103937
773 0 $t Dissertation Abstracts International $g 75-09A(E).
790 $a 0153
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3622497