Since the fatal shooting of 17-year old Black teenager Trayvon Martin in February 2012 by a White neighborhood watchman, George Zimmerman in Sanford, Florida, there has been a significant increase in digital activism addressing police-brutality related and racially-motivated incidents in the United States. In this work, we administer an innovative study of digital activism by exploiting social media as an authoritative tool to examine and analyze the linguistic cues and thematic relationships in these three mediums. We conduct a multi-level text analysis on 36,984,559 tweets to investigate users' behaviors to examine the language used and understand the impact of digital activism on social media within each social movement on a sentence-level, word-level, and topic-level. Our results show that excessive use of racially-related or prejudicial hashtags were used by the counter protests which portray potential discriminatory tendencies. Consequently, our findings highlight that social activism done by Black Lives Matter activists does not diverge from the social issues and topics involving police-brutality related and racially-motivated killings of Black individuals due to the shape of its topical graph that topics and conversations encircling the largest component directly relate to the topic of Black Lives Matter. Finally, we see that both Blue Lives Matter and All Lives Matter movements depict a different directive, as the topics of Blue Lives Matter or All Lives Matter do not reside in the center. These findings suggest that topics and conversations within each social movement are skewed, random or possessed racially-related undertones, and thus, deviating from the prominent social injustice issues.

本研究利用社交媒体作为权威工具对3种数字活动进行研究，对36,984,559个推文进行多级文本分析，探讨用户行为和语言使用的影响，研究结果显示反对抗议者过度使用涉及种族或偏见的主题标签，而Black Lives Matter活动家的社交活动并没有偏离与警察暴力相关和种族动机杀死黑人的社会问题和话题，这表明每个社交运动的话题和对话往往具有偏见，与主要社会不公问题偏离。

什么是真正重要的？使用语言线索分析#BlackLivesMatter运动及其反对抗议活动：2013至2020年