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What Tweets and Retweets on Twitter ...

Wang, Xi.

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What Tweets and Retweets on Twitter Can Tell for the Restaurant Industry: A Big-Data Approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	What Tweets and Retweets on Twitter Can Tell for the Restaurant Industry: A Big-Data Approach./
Author:	Wang, Xi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	90 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-01, Section: A.
Contained By:	Dissertations Abstracts International82-01A.
Subject:	Marketing. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27834685
ISBN:	9798658415925

What Tweets and Retweets on Twitter Can Tell for the Restaurant Industry: A Big-Data Approach.
Wang, Xi.

What Tweets and Retweets on Twitter Can Tell for the Restaurant Industry: A Big-Data Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 90 p.

Source: Dissertations Abstracts International, Volume: 82-01, Section: A.

Thesis (Ph.D.)--Iowa State University, 2020.

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

In the Internet age, the sheer volume of information can be generated and disseminated through online user-generated content (UGC). Within the context of Twitter, the retweeting function is one of the key mechanisms, which enables the information diffusion process among users in the social network. Stimulated by this concern, the purpose of the current study was to investigate the effects of textual content including the sentiments, emotions, and language style matching (LSM) of Twitter, a series of statistical analyses are conducted to the Twitter dataset with around one million pieces of customer tweet information. The results indicated that sentiments, emotions, and LSM have significant influences on customer retweeting behavior. Besides, significant differences were identified of both sentiments and emotions based on both six periods of the timeline analysis and the geographic distance at the city level, state level, and nationwide level. Discussions and implications interpreted the significance of the most valuable findings and suggested some important insights to both academia and industry.

ISBN: 9798658415925Subjects--Topical Terms:

536353
Marketing.
Subjects--Index Terms:

Emotion

What Tweets and Retweets on Twitter Can Tell for the Restaurant Industry: A Big-Data Approach.
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520 $a In the Internet age, the sheer volume of information can be generated and disseminated through online user-generated content (UGC). Within the context of Twitter, the retweeting function is one of the key mechanisms, which enables the information diffusion process among users in the social network. Stimulated by this concern, the purpose of the current study was to investigate the effects of textual content including the sentiments, emotions, and language style matching (LSM) of Twitter, a series of statistical analyses are conducted to the Twitter dataset with around one million pieces of customer tweet information. The results indicated that sentiments, emotions, and LSM have significant influences on customer retweeting behavior. Besides, significant differences were identified of both sentiments and emotions based on both six periods of the timeline analysis and the geographic distance at the city level, state level, and nationwide level. Discussions and implications interpreted the significance of the most valuable findings and suggested some important insights to both academia and industry.
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