Abstract：
BACKGROUND: The COVID-19 pandemic caused a critical public health crisis worldwide, and policymakers are using lockdowns to control the virus. However, there has been a noticeable increase in aggressive social behaviors that threaten social stability. Lockdown measures might negatively affect mental health and lead to an increase in aggressive emotions. Discovering the relationship between lockdown and increased aggression is crucial for formulating appropriate policies that address these adverse societal effects. We applied natural language processing (NLP) technology to internet data, so as to investigate the social and emotional impacts of lockdowns.
OBJECTIVE: This research aimed to understand the relationship between lockdown and increased aggression using NLP technology to analyze the following 3 kinds of aggressive emotions: anger, offensive language, and hate speech, in spatiotemporal ranges of tweets in the United States.
METHODS: We conducted a longitudinal internet study of 11,455 Twitter users by analyzing aggressive emotions in 1,281,362 tweets they posted from 2019 to 2020. We selected 3 common aggressive emotions (anger, offensive language, and hate speech) on the internet as the subject of analysis. To detect the emotions in the tweets, we trained a Bidirectional Encoder Representations from Transformers (BERT) model to analyze the percentage of aggressive tweets in every state and every week. Then, we used the difference-in-differences estimation to measure the impact of lockdown status on increasing aggressive tweets. Since most other independent factors that might affect the results, such as seasonal and regional factors, have been ruled out by time and state fixed effects, a significant result in this difference-in-differences analysis can not only indicate a concrete positive correlation but also point to a causal relationship.
RESULTS: In the first 6 months of lockdown in 2020, aggression levels in all users increased compared to the same period in 2019. Notably, users under lockdown demonstrated greater levels of aggression than those not under lockdown. Our difference-in-differences estimation discovered a statistically significant positive correlation between lockdown and increased aggression (anger: P=.002, offensive language: P<.001, hate speech: P=.005). It can be inferred from such results that there exist causal relations.
CONCLUSIONS: Understanding the relationship between lockdown and aggression can help policymakers address the personal and societal impacts of lockdown. Applying NLP technology and using big data on social media can provide crucial and timely information for this effort.
OBJECTIVE: This research aimed to understand the relationship between lockdown and increased aggression using NLP technology to analyze the following 3 kinds of aggressive emotions: anger, offensive language, and hate speech, in spatiotemporal ranges of tweets in the United States.
METHODS: We conducted a longitudinal internet study of 11,455 Twitter users by analyzing aggressive emotions in 1,281,362 tweets they posted from 2019 to 2020. We selected 3 common aggressive emotions (anger, offensive language, and hate speech) on the internet as the subject of analysis. To detect the emotions in the tweets, we trained a Bidirectional Encoder Representations from Transformers (BERT) model to analyze the percentage of aggressive tweets in every state and every week. Then, we used the difference-in-differences estimation to measure the impact of lockdown status on increasing aggressive tweets. Since most other independent factors that might affect the results, such as seasonal and regional factors, have been ruled out by time and state fixed effects, a significant result in this difference-in-differences analysis can not only indicate a concrete positive correlation but also point to a causal relationship.
RESULTS: In the first 6 months of lockdown in 2020, aggression levels in all users increased compared to the same period in 2019. Notably, users under lockdown demonstrated greater levels of aggression than those not under lockdown. Our difference-in-differences estimation discovered a statistically significant positive correlation between lockdown and increased aggression (anger: P=.002, offensive language: P<.001, hate speech: P=.005). It can be inferred from such results that there exist causal relations.
CONCLUSIONS: Understanding the relationship between lockdown and aggression can help policymakers address the personal and societal impacts of lockdown. Applying NLP technology and using big data on social media can provide crucial and timely information for this effort.
摘要：
COVID-19大流行在全球范围内引发了严重的公共卫生危机，政策制定者正在使用封锁措施来控制病毒。然而,威胁社会稳定的侵略性社会行为明显增加。锁定措施可能会对心理健康产生负面影响，并导致攻击性情绪增加。发现封锁和增加侵略之间的关系对于制定解决这些不利社会影响的适当政策至关重要。我们将自然语言处理（NLP）技术应用于互联网数据，以便调查封锁对社会和情感的影响。
这项研究旨在了解封锁与增加攻击性之间的关系，使用NLP技术分析以下3种攻击性情绪：愤怒，攻击性语言,仇恨言论,在美国的推文时空范围内。我们对11,455名Twitter用户进行了一项纵向互联网研究，分析了他们从2019年到2020年发布的1,281,362条推文的攻击性情绪。我们选择了3种常见的攻击性情绪（愤怒，攻击性语言,和仇恨言论)在互联网上作为分析的主题。为了检测推文中的情绪，我们训练了一个来自变形金刚的双向编码器表示（BERT）模型，以分析每个州和每个星期的攻击性推文百分比。然后，我们使用差异中的差异估计来衡量封锁状态对不断增加的攻击性推文的影响.由于大多数其他可能影响结果的独立因素，如季节性和区域性因素，被时间和国家的固定效应排除在外,这种差异分析的重要结果不仅可以表明具体的正相关，而且可以指出因果关系。
在2020年封锁的前6个月，与2019年同期相比，所有用户的攻击性水平都有所上升。值得注意的是,被封锁的用户比未被封锁的用户表现出更高的攻击性。我们的差异估计发现，封锁与攻击性增加之间存在统计学上的显着正相关（愤怒：P=.002，攻击性语言：P<.001，仇恨言论：P=.005）。从这些结果可以推断存在因果关系。
了解封锁和侵略之间的关系可以帮助政策制定者解决封锁对个人和社会的影响。应用NLP技术和在社交媒体上使用大数据可以为这项工作提供关键和及时的信息。
这项研究旨在了解封锁与增加攻击性之间的关系，使用NLP技术分析以下3种攻击性情绪：愤怒，攻击性语言,仇恨言论,在美国的推文时空范围内。我们对11,455名Twitter用户进行了一项纵向互联网研究，分析了他们从2019年到2020年发布的1,281,362条推文的攻击性情绪。我们选择了3种常见的攻击性情绪（愤怒，攻击性语言,和仇恨言论)在互联网上作为分析的主题。为了检测推文中的情绪，我们训练了一个来自变形金刚的双向编码器表示（BERT）模型，以分析每个州和每个星期的攻击性推文百分比。然后，我们使用差异中的差异估计来衡量封锁状态对不断增加的攻击性推文的影响.由于大多数其他可能影响结果的独立因素，如季节性和区域性因素，被时间和国家的固定效应排除在外,这种差异分析的重要结果不仅可以表明具体的正相关，而且可以指出因果关系。
在2020年封锁的前6个月，与2019年同期相比，所有用户的攻击性水平都有所上升。值得注意的是,被封锁的用户比未被封锁的用户表现出更高的攻击性。我们的差异估计发现，封锁与攻击性增加之间存在统计学上的显着正相关（愤怒：P=.002，攻击性语言：P<.001，仇恨言论：P=.005）。从这些结果可以推断存在因果关系。
了解封锁和侵略之间的关系可以帮助政策制定者解决封锁对个人和社会的影响。应用NLP技术和在社交媒体上使用大数据可以为这项工作提供关键和及时的信息。
