论文标题
在Twitter上对模因的语义搜索
Semantic Search of Memes on Twitter
论文作者
论文摘要
模因正在成为分析社交媒体行为的有用数据来源。但是，要解决的问题是如何正确识别模因。由于每天在社交媒体上发布的模因数量很大，因此需要在大型模因数据集中进行自动方法进行分类和搜索。本文提出并比较了几种自动将图像分类为模因的方法。另外，我们提出了一种方法，该方法允许我们使用文本查询实现从数据集检索模因的系统。我们使用从智利Twitter用户收集的大量模因进行实验评估这些方法，该模因由一群专家注释。尽管一些评估的方法有效，但仍然有改进的余地。
Memes are becoming a useful source of data for analyzing behavior on social media. However, a problem to tackle is how to correctly identify a meme. As the number of memes published every day on social media is huge, there is a need for automatic methods for classifying and searching in large meme datasets. This paper proposes and compares several methods for automatically classifying images as memes. Also, we propose a method that allows us to implement a system for retrieving memes from a dataset using a textual query. We experimentally evaluate the methods using a large dataset of memes collected from Twitter users in Chile, which was annotated by a group of experts. Though some of the evaluated methods are effective, there is still room for improvement.
