论文标题
在真实和协调的模因中绘制视觉主题
Mapping Visual Themes among Authentic and Coordinated Memes
论文作者
论文摘要
是什么与国家参与者创造的模因区分开来？我利用自我监督的视觉模型DeepCluster（Caron等，2019）学习模因的低维视觉嵌入，并将K-均值应用于共同集群真实和协调的模因，而没有其他输入。我发现真实和协调的模因具有很大一部分的视觉主题，但程度不同。俄罗斯IRA帐户的协调模因在名人，报价，屏幕截图，军事和性别方面促进了更多主题。正宗的Reddit模因包括更多带有漫画和电影角色的主题。低维嵌入的简单逻辑回归可以从Reddit模因中辨别IRA模因，其外样品测试精度为0.84。
What distinguishes authentic memes from those created by state actors? I utilize a self-supervised vision model, DeepCluster (Caron et al. 2019), to learn low-dimensional visual embeddings of memes and apply K-means to jointly cluster authentic and coordinated memes without additional inputs. I find that authentic and coordinated memes share a large fraction of visual themes but with varying degrees. Coordinated memes from Russian IRA accounts promote more themes around celebrities, quotes, screenshots, military, and gender. Authentic Reddit memes include more themes with comics and movie characters. A simple logistic regression on the low-dimensional embeddings can discern IRA memes from Reddit memes with an out-sample testing accuracy of 0.84.
