A Tutorial on Collecting and Processing Longitudinal Social Media Data

Grace M. Leffler, Xin Tong
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Abstract

Longitudinal research using social media data has been under-explored in social and behavioral sciences. Despite its great potential, longitudinal analysis using social media data faces unique challenges. Researchers must consider many influential factors and incorporate them when designing their studies and conducting analyses. Over the past decade, best practices have originated from both studies focusing on social media data in general and those applying longitudinal designs. This tutorial aims to educate those unfamiliar with such a growing field, outlining the different steps that may exist within data collection, data processing, and data analysis of longitudinal social media data. To illustrate these techniques, we apply our basic steps to a Twitter dataset about the 2020 U.S. wildfires, examining sentiment throughout the wildfire period.
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收集和处理纵向社交媒体数据教程
在社会和行为科学中,利用社交媒体数据进行的纵向研究尚未得到充分的探索。尽管潜力巨大,但利用社交媒体数据进行纵向分析面临着独特的挑战。研究人员在设计研究和进行分析时必须考虑许多有影响的因素,并将它们结合起来。在过去的十年中,最佳实践来源于关注社交媒体数据的一般研究和应用纵向设计的研究。本教程旨在教育那些不熟悉这样一个不断发展的领域的人,概述了纵向社交媒体数据的数据收集、数据处理和数据分析中可能存在的不同步骤。为了说明这些技术,我们将基本步骤应用于关于2020年美国野火的Twitter数据集,检查整个野火期间的情绪。
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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