A Tale of Two Internet News Platforms-Real vs. Fake: An Elaboration Likelihood Model Perspective
A Tale of Two Internet News Platforms-Real vs. Fake: An Elaboration Likelihood Model Perspective
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DOI：
10.24251/hicss.2018.500
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发表时间：
2018-01
期刊：
影响因子：
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通讯作者：
Babajide Osatuyi;Jerald Hughes
中科院分区：
文献类型：
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作者：
Babajide Osatuyi;Jerald Hughes
本文介绍了对互联网上传播的真实新闻与虚假新闻进行现场分析的结果。精化似然模型（ELM）被用作理论框架来研究真实和虚假内容生成器用来说服读者的信息呈现机制。 ELM 提出了信息可以告知态度变化的两种途径：高认知努力的中心途径和低认知努力的外围途径。我们假设假新闻网站通过提供较少的总体信息和提供更多的负面情感线索来偏向外围路线。数据是从发布真实新闻和假新闻的互联网平台收集的。结果表明，假新闻平台传播的信息量低于信誉良好的平台。内容分析表明，与真实新闻相比，具有商业影响的虚假新闻的效价通常更为负面。讨论了我们的研究结果对理论和实践的影响。
This paper presents findings from a field analysis of real vs. fake news propagated on the Internet. Elaboration Likelihood Model (ELM) was used as a theoretical framework to investigate information presentation mechanisms used by real and fake content generators to persuade readers. ELM theorizes two routes through which information can inform attitudinal changes: a central route of high cognitive effort, and a peripheral route of low cognitive effort. We hypothesize that fake news sites favor the peripheral route by providing less information overall, and by providing more negative affective cues. Data was gathered from Internet platforms that publish real news and fake news. Results indicate that the amount of information disseminated by fake news platforms is lower than that of reputable platforms. Content analysis reveals that fake news with business impact are typically more negative in their valence compared to real news. Implications of our findings for theory and practice are discussed.