Abstract

In this study, we used a multimodal method to examine a visual dataset posted on Instagram that references #CNN and #FoxNews. Using visual analysis, we inductively categorized the memes into nine major types and identified the most referenced hashtags associated with these memes. Our method provided more insight into the different meanings of memes. Building on the theoretical concept of networked flak, the findings indicate that Instagram memes which mention CNN and Fox News are often politically polarizing and largely critical of these two news channels, especially CNN (25.4%) and to a lesser extent Fox News (15.6%). We argue that routinely trolling news channels creates a polarized political environment and is meant to put pressure on journalists which, if successful, could potentially influence them.

摘要

在这项研究中，我们使用多模式方法来检查 Instagram 上发布的引用#CNN 和#FoxNews 的视觉数据集。使用视觉分析，我们将模因归纳为九种主要类型，并确定了与这些模因相关的引用最多的主题标签。我们的方法提供了对模因不同含义的更多洞察。基于网络攻击的理论概念，调查结果表明，提及 CNN 和福克斯新闻的 Instagram 模因通常在政治上两极分化，并且主要批评这两个新闻频道，尤其是 CNN (25.4%) 和福克斯新闻 (15.6%) ). 我们认为，例行公事的新闻频道制造了两极分化的政治环境，意在向记者施加压力，如果成功，可能会对他们产生影响。