"Make it difficult": using disfluency to minimize higher motivation effects on fake (and real) news dissemination on social media

被引:0
|
作者
Viacava, JuanJose Camou [1 ]
Borbacoelho, Brunodossantos [1 ]
Sabin, Giovane [1 ]
Lopes, Gloria Faria [1 ]
机构
[1] Pontificia Univ Catolica Parana PUCPR, Curitiba, PR, Brazil
来源
REVISTA BRASILEIRA DE MARKETING | 2024年 / 23卷 / 03期
关键词
eWOM; Fluency; Fake news; Perceived truth; WORD-OF-MOUTH; PROCESSING FLUENCY; SOURCE CREDIBILITY; ENGAGEMENT; REPETITION; JUDGMENTS; TRUTH; EXPERIENCES; CONFIDENCE; CONSUMERS;
D O I
10.5585/remark.v23i3.24363
中图分类号
F [经济];
学科分类号
02 ;
摘要
Purpose: To investigate the effect of disfluency (perceived difficulty) and prior motivation to do and disseminate electronic word-of-mouth (eWOM) on headlines/posts online, as well as the mediating role of perceived truth. Design/methodology: This study involves three online experiments emulating "X" (former Twitter) messages and Instagram/Facebook posts. Disfluency was measured in Experiment 1 and manipulated in Experiments 2 and 3 while also measuring prior motivation to disseminate eWOM. Findings: higher prior motivation increased fake and authentic news dissemination, but disfluency diminished this effect through its influence on perceived truth. Originality/value: These results demonstrate that people tend to disseminate authentic and fake news owing to a carryover effect, and this tendency is affected by prior eWOM motivation. Disfluency can, thus, not only help prevent fake news dissemination but also inhibit authentic (real) news dissemination. These effects are due to perceived truth, not attention or perceived relevance, and only affect people with higher eWOM motivation. Because the perceptual disfluency manipulations tested are like what occurs daily (i.e., "dark theme" in smartphones and Instagram's use of font colors), we propose that similar proceedings can decrease the mass propagation of widely disseminated fake news.
引用
收藏
页数:58
相关论文
共 41 条
  • [41] Identifying Fake News written on Albanian language in social media using Naive Bayes, SVM, Logistic Regression, Decision Tree and Random Forest algorithms
    Hoti, Arber H.
    Hoti, Mergim H.
    Hoti, Hamdi
    Salihu, Armend
    2022 11TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING (MECO), 2022, : 259 - 264