Crafting audience engagement in social media conversations
- Observing inconsistent results in prior studies, this paper applies the elaboration likelihood model to investigate the impact of affective and cognitive cues embedded in social media messages on audience engagement during a political event. Leveraging a rich dataset in the context of the 2020 U.S. presidential elections containing more than 3 million tweets, we found the prominence of both cue types. For the overall sample, positivity and sentiment are negatively related to engagement. In contrast, the post-hoc sub-sample analysis of tweets from famous users shows that emotionally charged content is more engaging. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with a vast number of followers. Prosocial orientation (“we-talk”) is consistently associated with more likes, comments, and retweets in the overall sample and sub-samples.
Author details: | Linus HagemannORCiD, Olga AbramovaORCiDGND |
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Handle: | http://hdl.handle.net/10125/79729 |
ISBN: | 978-0-9981331-5-7 |
Title of parent work (English): | Proceedings of the 55th Hawaii International Conference on System Sciences |
Subtitle (English): | evidence from the U.S. 2020 presidential elections |
Publisher: | HICSS Conference Office University of Hawaii at Manoa |
Place of publishing: | Honolulu |
Publication type: | Conference Proceeding |
Language: | English |
Date of first publication: | 2022/01/04 |
Publication year: | 2022 |
Release date: | 2024/03/08 |
Tag: | big data; engagement; mediated conversation; sentiment analysis; social media |
Number of pages: | 10 |
First page: | 3222 |
Last Page: | 3231 |
Organizational units: | Wirtschafts- und Sozialwissenschaftliche Fakultät / Wirtschaftswissenschaften / Fachgruppe Betriebswirtschaftslehre |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Peer review: | Nicht ermittelbar |
Publishing method: | Open Access / Gold Open-Access |
License (German): | CC-BY-NC-ND - Namensnennung, nicht kommerziell, keine Bearbeitungen 4.0 International |