How social media influencers persuade audiences in socio-political campaigns : an elaboration likelihood model perspective
Social media influencers (SMIs) can serve as key disseminators of information and promote socio-political messages to other users. This two-part study uses the Elaboration Likelihood Model (ELM) to understand how influencers persuade other users in socio-political contexts. Both Study 1 and 2 examin...
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Main Authors: | , , , |
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2022
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Online Access: | https://hdl.handle.net/10356/155901 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Social media influencers (SMIs) can serve as key disseminators of information and promote socio-political messages to other users. This two-part study uses the Elaboration Likelihood Model (ELM) to understand how influencers persuade other users in socio-political contexts. Both Study 1 and 2 examine how central and peripheral route factors (perceived argument quality, perceived trustworthiness, perceived expertise, and perceived attractiveness) affect attitude towards policy and behavioral intention to share information after viewing SMI content. Study 1 was conducted in the US regarding immigration policy, while Study 2 was conducted in Singapore regarding workplace anti-discrimination legislation. Each study recruited 400 participants for a survey experiment, where information accuracy and information quantity were manipulated to either high or low conditions, displayed as one of two SMI’s posts. Respondents were randomly exposed to one of eight stimuli, and the ELM factors, attitude and intention to share information was measured. The results differed slightly in both studies. The ELM factors were significantly associated with attitude, and this was moderated by perceived ethnic threat and issue involvement in Study 2. The ELM variables also had an indirect effect on intention to share information. Information accuracy and information quantity had some effect on the ELM variables. |
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