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Brand messages on Twitter: Predictin...

Vargo, Chris J.

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Brand messages on Twitter: Predicting diffusion with textual characteristics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Brand messages on Twitter: Predicting diffusion with textual characteristics./
作者:	Vargo, Chris J.
面頁冊數:	126 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-09(E), Section: A.
Contained By:	Dissertation Abstracts International75-09A(E).
標題:	Speech Communication. -
電子資源:	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.
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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.
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