Journal article
Which social media posts generate the most buzz? Evidence from WeChat
Internet research, Vol.32(1), pp.273-291
01/18/2022
DOI: 10.1108/INTR-12-2019-0534
Abstract
Purpose Following the hierarchy-of-effects model, this study aims to propose a two-step process framework to investigate social media post efficacy via attraction and likes. Design/methodology/approach The study analyzes 113,785 social media posts from 126 WeChat official accounts to explore how external (headline features and account type) and internal (content features and media type) features impact social media post attractions and likes, respectively. Findings The antecedents of post attraction differ from those of post likes. First, headline features (punctuation, length, sentiment and lexical density) and account type significantly influence social media post attraction. Second, content features (depth, tone, domain specificity, lexical density and readability) and media type affect social media post likes. Originality/value First, this study considers online user engagement as a two-step process regarding social media posts and explores different influencing factors. Second, the study constructs new variables (account type and domain specificity) in each stage of the two-step process model.
Details
- Title: Subtitle
- Which social media posts generate the most buzz? Evidence from WeChat
- Creators
- Jie She - Changsha UniversityTao Zhang - Shanghai Univ Finance & Econ, Informat Management & Engn Coll, Shanghai, Peoples R ChinaQun Chen - Shanghai Publishing & Printing Coll, Shanghai, Peoples R ChinaJianzhang Zhang - Hangzhou Normal UniversityWeiguo Fan - University of IowaHongwei Wang - Tongji UniversityQingqing Chang - Shanghai Lixin Univ Accounting & Finance, Sch Informat Management, Shanghai, Peoples R China
- Resource Type
- Journal article
- Publication Details
- Internet research, Vol.32(1), pp.273-291
- Publisher
- Emerald Group Publishing
- DOI
- 10.1108/INTR-12-2019-0534
- ISSN
- 1066-2243
- eISSN
- 2054-5657
- Number of pages
- 19
- Grant note
- 20511101403 / Shanghai Municipal RD Foundation 18BTQ058 / National Social Science Fund General Project of China 19YJA630116 / Humanities and Social Sciences Planning Fund of Ministry of education of China 18ZDA088 / Major Program of National Fund of Philosophy and Social Science of China 61976057; 72074033 / National Natural Science Fund of China; National Natural Science Foundation of China (NSFC) 19ZR1417200 / Natural Science Fund of Shanghai; Natural Science Foundation of Shanghai 19DZ2254600 / Shanghai Engineering and Technology Research Center for Financial Intellegence 20C0164 / Scientific Research of Hunan Education Department of China
- Language
- English
- Date published
- 01/18/2022
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380459102771
Metrics
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