Analysis of Tweet Form's effect on users' engagement on Twitter

被引:19
作者
Han, Xu [1 ]
Gu, Xingyu [1 ]
Peng, Shuai [2 ]
机构
[1] Johns Hopkins Univ, Carey Business Sch, Informat Syst, Washington, DC 20036 USA
[2] Johns Hopkins Univ, WarmStone Co Ltd, Changsha, Hunan, Peoples R China
关键词
social media marketing; Twitter; Tweet Form; user engagement;
D O I
10.1080/23311975.2018.1564168
中图分类号
F [经济];
学科分类号
02 ;
摘要
This research focuses on the effects on users' engagement of different tweet forms including text length, text sentiment and the usage of hashtag, mention, video or picture URL. In the first part, we analyze the tweets of five companies from the apparel industry and finds out that there is no universal form that can boost user's engagement, but in company scale, the effects of different forms between companies are various due to company attributes. Hence, in our second research, we expand the dataset and analyze the formats of tweets from 70 brands focusing on the attribute of the industry section. The conclusion shows that industries such as luxury and hardware technology are more digital sensitive and benefit more using more hashtag and video or picture URL while industry such as software industry is more digital insensitive. The result could provide evidence and guidance for different categories of companies to design tweets with high customer engagements and serve as a reference for enterprises on other media platforms.
引用
收藏
页码:1 / 15
页数:15
相关论文
共 24 条
[1]  
Antoine H. A., 2016, COMPUTER INTERNET LA, V33, P6
[2]   Communicating in 140 Characters. How Journalists in Spain use Twitter [J].
Arrabal-Sanchez, Gabriel ;
De-Aguilera-Moyano, Miguel .
COMUNICAR, 2016, 24 (46) :9-17
[3]  
Bao P., 2013, MENTION EFFECT INFOR
[4]  
Burnett R., 2015, Images and videos-Media for communication
[5]  
Burton S, 2011, J CONSUM MARK, V28, P491, DOI 10.1108/07363761111181473
[6]  
Byrum K. L., 2014, A comparison of the source, media format, and sentiment in generating source credibility, information credibility, corporate brand reputation, purchase intention, and social media engagement in a corporate social responsibility campaign presented via soci
[7]  
Cho CH, 2012, PROGR NEUROPSYCHOPHA, P1
[8]   Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network [J].
Ghiassi, M. ;
Skinner, J. ;
Zimbra, D. .
EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (16) :6266-6282
[9]   Customer engagement in a Facebook brand community [J].
Gummerus, Johanna ;
Liljander, Veronica ;
Weman, Emil ;
Pihlstrom, Minna .
MANAGEMENT RESEARCH REVIEW, 2012, 35 (09) :857-877
[10]   Consumer Brand Engagement in Social Media: Conceptualization, Scale Development and Validation [J].
Hollebeek, Linda D. ;
Glynn, Mark S. ;
Brodie, Roderick J. .
JOURNAL OF INTERACTIVE MARKETING, 2014, 28 (02) :149-165