HeyLo: Visualizing User Interests from Twitter Using Emoji in Mixed Reality

H. Harris, Makayla Thompson, Isaac Griffith, P. Bodily
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引用次数: 1

Abstract

We tackle the problem of analyzing a user's interests from social media content and subsequently visualizing these interests in an extended reality environment. We compare five models for extracting interests from Twitter users and how we can measure the effectiveness of these models. We also look at how these interest extraction models fit in the context of HeyLo, an extended reality computational creativity (XRCC) framework for visualizing potential conversational topics. The chosen interests for a particular person are visualized using emoji. We accomplish this by using an emoji2vector model to find the closest related emoji to a given interest. We perform a comparative analysis between the five interest extraction models on real-world users and their tweets, evaluating specificity, variance, and relevance.
在混合现实中使用表情符号来可视化Twitter上的用户兴趣
我们解决了从社交媒体内容中分析用户兴趣并随后在扩展现实环境中可视化这些兴趣的问题。我们比较了从Twitter用户中提取兴趣的五种模型,以及我们如何衡量这些模型的有效性。我们还研究了这些兴趣提取模型如何适合于HeyLo的上下文,这是一个用于可视化潜在会话主题的扩展现实计算创造力(XRCC)框架。一个特定的人选择的兴趣是用表情符号可视化的。我们通过使用emoji2vector模型来找到与给定兴趣最接近的相关表情符号来实现这一点。我们对现实世界用户及其推文的五种兴趣提取模型进行了比较分析,评估了特异性、方差和相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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