Understanding the relationship between structure and sentiment is essential in highlighting future operations with online social networks. More specifically, within popular conversation on Twitter. This paper provides a development on the relationship between the two variables: structure, defined as the composition of a directed network, and sentiment, a quantified value of the positive/negative connotations of a conversation. We highlight thread sentiment to be inversely proportional to the strength and connectivity of a network. The second portion of this paper highlights differences in query types, specifically how the aforementioned behavior differs within four key query types. This paper focuses on topical, event-based, geographic, and individual queries as orientations which have differing behavior. Using cross-query analysis, we see that the relationship between structure and sentiment, though still inversely proportional, differs greatly across query types. We find this relationship to be the most clear within the individual queries and the least prevalent within the event-based queries. This paper provides a sociological progression in our understanding of opinion and networks, while providing a methodological advancement for future studies on similar subjects.
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少数群体一直在使用社交媒体来组织社会运动,从而产生深远的社会影响。黑人生活问题(BLM)和停止亚洲仇恨(SAH)是两个成功的社会运动,在Twitter上蔓延开来,促进了抗议活动和活动,反对种族主义,并提高公众对少数群体面临的其他社会挑战的认识。但是,以前的研究主要对与用户的推文或访谈进行了定性分析,这些推文或访谈可能无法全面和有效地代表所有推文。很少有研究以严格,量化和以数据为中心的方法探讨了BLM和SAH对话中的Twitter主题。因此,在这项研究中,我们采用了一种混合方法来全面分析BLM和SAH Twitter主题。我们实施了(1)潜在的DIRICHLET分配模型,以了解顶级高级单词和主题以及(2)开放编码分析,以确定整个推文中的特定主题。我们通过#BlackLivesMatter和#Stopasianhate主题标签收集了超过一百万条推文,并比较了它们的主题。我们的发现表明,这些推文在深度上讨论了各种有影响力的话题,社会正义,社会运动和情感情感都是两种运动的共同主题,尽管每个运动都有独特的子主题。我们的研究尤其是社交媒体平台上的社会运动的主题分析,以及有关AI,伦理和社会相互作用的文献。
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最近受到在线叙述驱动的疫苗犹豫会大大降低了疫苗接种策略的功效,例如Covid-19。尽管医学界对可用疫苗的安全性和有效性达成了广泛的共识,但许多社交媒体使用者仍被有关疫苗的虚假信息淹没,并且柔和或不愿意接种疫苗。这项研究的目的是通过开发能够自动识别负责传播反疫苗叙事的用户的系统来更好地理解反疫苗情绪。我们引入了一个公开可用的Python软件包,能够分析Twitter配置文件,以评估该个人资料将来分享反疫苗情绪的可能性。该软件包是使用文本嵌入方法,神经网络和自动数据集生成的,并接受了数百万条推文培训。我们发现,该模型可以准确地检测出抗疫苗用户,直到他们推文抗Vaccine主题标签或关键字。我们还展示了文本分析如何通过检测Twitter和常规用户之间的抗疫苗传播器之间的道德和情感差异来帮助我们理解反疫苗讨论的示例。我们的结果将帮助研究人员和政策制定者了解用户如何成为反疫苗感以及他们在Twitter上讨论的内容。政策制定者可以利用此信息进行更好的针对性的运动,以揭露有害的反疫苗接种神话。
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从三个研究趋势中汇集了考虑(合作的诚实信号,社会语义网络和同性恋理论),我们假设字词使用相似性并具有类似的社交网络位置与员工数字交互的水平相关联。为了验证我们的假设,我们分析了近1600名员工的沟通,在大公司的Intranet通信论坛上互动。我们研究了他们的社会动态和“诚实信号”,在过去的研究中证明有利于员工的参与和合作。我们发现这个词使用相似性是交互的主要驱动因素,远远超过网络位置的其他语言特征或相似性。我们的结果表明根据目标受众仔细选择语言,并对公司经理和在线社区管理员进行实际影响。例如,了解如何更好的使用语言可以支持开发知识共享实践或内部通信活动。
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With Twitter's growth and popularity, a huge number of views are shared by users on various topics, making this platform a valuable information source on various political, social, and economic issues. This paper investigates English tweets on the Russia-Ukraine war to analyze trends reflecting users' opinions and sentiments regarding the conflict. The tweets' positive and negative sentiments are analyzed using a BERT-based model, and the time series associated with the frequency of positive and negative tweets for various countries is calculated. Then, we propose a method based on the neighborhood average for modeling and clustering the time series of countries. The clustering results provide valuable insight into public opinion regarding this conflict. Among other things, we can mention the similar thoughts of users from the United States, Canada, the United Kingdom, and most Western European countries versus the shared views of Eastern European, Scandinavian, Asian, and South American nations toward the conflict.
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本文描述了一个关于人们的话语的大型全球数据集以及在Twitter平台上对Covid-19的大流行的反应。从2020年1月28日至2022年6月1日,我们收集并处理了超过2900万个唯一用户的Twitter帖子,使用了四个关键字:“ Corona”,“ Wuhan”,“ NCOV”和“ COVID”。利用概率主题建模和预训练的基于机器学习的情感识别算法,我们将每个推文标记为具有十七个属性,包括a)十个二进制属性,指示了Tweet的相关性(1)或与前十名检测到的主题,B )五个定量情绪属性表示价或情感的强度程度(从0:极为消极到1:极为积极)以及恐惧,愤怒,悲伤和幸福情感的强度程度(从0:完全不是1到1 :极度强烈),c)两个分类属性表明情绪(非常负面,消极,中立或混合,积极,非常积极)以及主导的情感(恐惧,愤怒,悲伤,幸福,没有特定的情感),主要是推文表达。我们讨论技术有效性,并报告这些属性的描述性统计,其时间分布和地理表示。本文最后讨论了数据集在传播,心理学,公共卫生,经济学和流行病学中的用法。
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社交媒体的回声室是一个重要的问题,可以引起许多负面后果,最近影响对Covid-19的响应。回声室促进病毒的阴谋理论,发现与疫苗犹豫不决,较少遵守面具授权,以及社会疏散的实践。此外,回声室的问题与政治极化等其他相关问题相连,以及误导的传播。回声室被定义为用户网络,用户只与支持其预先存在的信仰和意见的意见相互作用,并且他们排除和诋毁其他观点。本调查旨在从社会计算的角度检查社交媒体上的回声室现象,并为可能的解决方案提供蓝图。我们调查了相关文献,了解回声室的属性以及它们如何影响个人和社会。此外,我们展示了算法和心理的机制,这导致了回声室的形成。这些机制可以以两种形式表现出:(1)社交媒体推荐系统的偏见和(2)内部偏见,如确认偏见和精梳性。虽然减轻内部偏见是非常挑战的,但努力消除推荐系统的偏见。这些推荐系统利用我们自己的偏见来个性化内容建议,以使我们参与其中才能观看更多广告。因此,我们进一步研究了回声室检测和预防的不同计算方法,主要基于推荐系统。
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Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.
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即使互联网和社交媒体增加了人们可能会消耗的新闻和信息量,大多数用户才会暴露于加强其职位的内容,并将其与其他思想社区隔离。这种环境对我们的生活产生了极大的影响,如严重的政治极化,轻松传播的假新闻,政治极端主义,仇恨团体以及缺乏丰富的辩论等。因此,鼓励不同的用户组之间的对话并打破封闭的社区对健康社会的重要性。在本文中,我们使用自然语言处理技术和图形机学习算法来表征和研究在Twitter上打破社区的用户。特别是,我们从150万用户收集了900万个Twitter消息,并构建了转发网络。我们确定了他们的社区和与他们相关的讨论主题。通过这些数据,我们为社交媒体用户分类提供了一种机器学习框架,该分类检测到“社区分手”,即从他们的封闭社区到另一个用户的用户。三个Twitter极化政治数据集中的一个特征重要性分析表明,这些用户的PageRank值低,表明改变是推动的,因为他们的消息在其社区中没有响应。这种方法还允许我们确定其特定的兴趣主题,提供了这种用户的全面表征。
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Large language models have recently attracted significant attention due to their impressive performance on a variety of tasks. ChatGPT developed by OpenAI is one such implementation of a large, pre-trained language model that has gained immense popularity among early adopters, where certain users go to the extent of characterizing it as a disruptive technology in many domains. Understanding such early adopters' sentiments is important because it can provide insights into the potential success or failure of the technology, as well as its strengths and weaknesses. In this paper, we conduct a mixed-method study using 10,732 tweets from early ChatGPT users. We first use topic modelling to identify the main topics and then perform an in-depth qualitative sentiment analysis of each topic. Our results show that the majority of the early adopters have expressed overwhelmingly positive sentiments related to topics such as Disruptions to software development, Entertainment and exercising creativity. Only a limited percentage of users expressed concerns about issues such as the potential for misuse of ChatGPT, especially regarding topics such as Impact on educational aspects. We discuss these findings by providing specific examples for each topic and then detail implications related to addressing these concerns for both researchers and users.
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这项工作介绍了一种新方法,以考虑文本分析中的主观性和一般上下文依赖性,并用作示例检测文本中传达的情绪。所提出的方法通过Marvin Minsky(1974)利用Mikolov等人的文本向量化的框架理论的计算版本来考虑主观性。 (2013),用于基于它们出现的上下文生成单词的分布式表示。我们的方法是基于三个组成部分:1。代表观点的框架/“房间”; 2.代表分析标准的基准 - 在这种情况下,情绪分类,从罗伯特·普特金(1980)的人类情绪研究; 3.要分析的文件。通过使用单词之间的相似性测量,我们能够在我们的案例研究中提取基准中的元素中的元素的相对相关性 - 对于要分析的文件。我们的方法提供了一种措施,考虑到读取文档的实体的角度。该方法可以应用于评估主体性与理解文本的相对值或含义相关的所有情况。主观性可以不限于人体反应,但它可用于提供具有与给定域(“房间”)相关的解释的文本。为了评估我们的方法,我们在政治领域中使用了测试案例。
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本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片(Sota)。它填补了空白,因为现有的调查文章在其范围内或被约会。我们包括两个重要方面,目前正在挖掘和建模社交媒体的重要性:动态和网络。社会动态对于了解影响影响或疾病的传播,友谊的形成,友谊的形成等,另一方面,可以捕获各种复杂关系,提供额外的洞察力和识别否则将不会被注意的重要模式。
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通过使信息生产和复制民主化的技术,社交媒体中每日互动的很大一部分被谣言感染了。尽管对谣言检测和验证进行了广泛的研究,但到目前为止,尚未考虑计算谣言传播力量的问题。为了解决这一研究差距,本研究寻求一个模型来计算谣言(SPR)作为基于内容特征的功能的两类功能:虚假谣言(FR)和真实谣言(TR)。为此,将采用Allport和Postman的理论,它声称重要性和歧义是谣言和谣言的力量的关键变量。引入了两个类别的“重要性”(28个功能)和“歧义”(14个功能)的42个内容功能以计算SPR。提出的模型将在两个数据集(Twitter和Telegram)上进行评估。结果表明,(i)虚假谣言文件的传播力量很少不仅仅是真正的谣言。 (ii)两组虚假谣言和真实谣言的SPR平均值之间存在显着差异。 (iii)SPR作为标准可以对区分虚假谣言和真实谣言产生积极影响。
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自Covid-19大流行病开始以来,疫苗一直是公共话语中的重要话题。疫苗周围的讨论被两极分化,因为有些人认为它们是结束大流行的重要措施,而另一些人则犹豫不决或发现它们有害。这项研究调查了与Twitter上的Covid-19疫苗有关的帖子,并着重于对疫苗有负姿态的帖子。收集了与COVID-19疫苗相关的16,713,238个英文推文的数据集,收集了涵盖从2020年3月1日至2021年7月31日的该期间。我们使用Scikit-Learn Python库来应用支持向量机(SVM)分类器针对Covid-19疫苗的推文具有负姿态。总共使用了5,163个推文来训练分类器,其中有2,484个推文由我们手动注释并公开提供。我们使用Berttopic模型来提取和调查负推文中讨论的主题以及它们如何随时间变化。我们表明,随着疫苗的推出,对COVID-19疫苗的负面影响随时间而下降。我们确定了37个讨论主题,并随着时间的推移介绍了各自的重要性。我们表明,流行的主题包括阴谋讨论,例如5G塔和微芯片,但还涉及涉及疫苗接种安全性和副作用以及对政策的担忧。我们的研究表明,即使是不受欢迎的观点或阴谋论,与广受欢迎的讨论主题(例如Covid-19疫苗)配对时,也会变得广泛。了解问题和讨论的主题以及它们如何随着时间的变化对于政策制定者和公共卫生当局提供更好和时间的信息和政策,以促进未来类似危机的人口接种。
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近几十年来,随着数据的可用,统计语言学已大大提高。这使研究人员能够研究语言的统计特性如何随时间变化。在这项工作中,我们使用来自Twitter的数据来探索英语和西班牙语,考虑到不同尺度的排名多样性:时间(从3到96小时),空间(从3公里到3000+km Radii)和语法(从字母组到五角形到Pentagrams) )。我们发现所有三个量表都是相关的。但是,最大的变化来自语法量表的变化。在最低的语法量表(会标)上,排名多样性曲线最相似,独立于其他量表,语言和国家的价值。随着语法量表的增长,等级多样性曲线的变化更大,具体取决于时间和空间量表以及语言和国家。我们还研究了Twitter特定令牌的统计数据:表情符号,主题标签和用户提及。这些特殊类型的令牌表现出一种sigmoid的行为作为等级多样性函数。我们的结果有助于量化似乎普遍存在的语言统计数据的各个方面,这可能导致变化。
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Current research on users` perspectives of cyber security and privacy related to traditional and smart devices at home is very active, but the focus is often more on specific modern devices such as mobile and smart IoT devices in a home context. In addition, most were based on smaller-scale empirical studies such as online surveys and interviews. We endeavour to fill these research gaps by conducting a larger-scale study based on a real-world dataset of 413,985 tweets posted by non-expert users on Twitter in six months of three consecutive years (January and February in 2019, 2020 and 2021). Two machine learning-based classifiers were developed to identify the 413,985 tweets. We analysed this dataset to understand non-expert users` cyber security and privacy perspectives, including the yearly trend and the impact of the COVID-19 pandemic. We applied topic modelling, sentiment analysis and qualitative analysis of selected tweets in the dataset, leading to various interesting findings. For instance, we observed a 54% increase in non-expert users` tweets on cyber security and/or privacy related topics in 2021, compared to before the start of global COVID-19 lockdowns (January 2019 to February 2020). We also observed an increased level of help-seeking tweets during the COVID-19 pandemic. Our analysis revealed a diverse range of topics discussed by non-expert users across the three years, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, and security and privacy issues involving different stakeholders. Overall negative sentiment was observed across almost all topics non-expert users discussed on Twitter in all the three years. Our results confirm the multi-faceted nature of non-expert users` perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on different facets of such perspectives.
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社交媒体通常在选举活动中被公众使用,以表达他们对不同问题的看法。在各种社交媒体渠道中,Twitter为研究人员和政客提供了一个有效的平台,以探索有关经济和外交政策等广泛主题的公众舆论。当前的文献主要集中于分析推文的内容而无需考虑用户的性别。这项研究收集和分析了大量推文,并使用计算,人类编码和统计分析来识别2020年美国总统选举期间发布的300,000多个推文中的主题。我们的发现是基于广泛的主题,例如税收,气候变化和Covid-19-19。在主题中,女性和男性用户之间存在着显着差异,超过70%的主题。
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社交媒体在现代社会中尤其是在西方世界中的政策制定方面已经变得极其影响力(例如,48%的欧洲人每天或几乎每天都使用社交媒体)。 Twitter之类的平台使用户可以关注政客,从而使公民更多地参与政治讨论。同样,政客们使用Twitter来表达他们的观点,在当前主题上进行辩论,并促进其政治议程,以影响选民行为。先前的研究表明,传达负面情绪的推文可能会更频繁地转发。在本文中,我们试图分析来自不同国家的政客的推文,并探索他们的推文是否遵循相同的趋势。利用最先进的预训练的语言模型,我们对从希腊,西班牙和英国的成千上万的推文进行了情感分析,包括权威的行政部门。我们通过系统地探索和分析有影响力和不流行的推文之间的差异来实现这一目标。我们的分析表明,政治家的负面推文更广泛地传播,尤其是在最近的时代,并突出了情感和受欢迎程度相交的有趣趋势。
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Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming -- siloed in so called ``echo chambers'' of exclusively like-minded discussants. Yet, to date we lack sufficient means to measure viewpoint diversity in conversations. To this end, in this paper, we operationalize two viewpoint metrics proposed for recommender systems and adapt them to the context of social media conversations. This is the first study to apply these two metrics (Representation and Fragmentation) to real world data and to consider the implications for online conversations specifically. We apply these measures to two topics -- daylight savings time (DST), which serves as a control, and the more politically polarized topic of immigration. We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST. Further, we find that while pro-immigrant views receive consistent pushback on the platform, anti-immigrant views largely operate within echo chambers. We observe less severe yet similar patterns for DST. Taken together, Representation and Fragmentation paint a meaningful and important new picture of viewpoint diversity.
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Following the outbreak of a global pandemic, online content is filled with hate speech. Donald Trump's ''Chinese Virus'' tweet shifted the blame for the spread of the Covid-19 virus to China and the Chinese people, which triggered a new round of anti-China hate both online and offline. This research intends to examine China-related hate speech on Twitter during the two years following the burst of the pandemic (2020 and 2021). Through Twitter's API, in total 2,172,333 tweets hashtagged #china posted during the time were collected. By employing multiple state-of-the-art pretrained language models for hate speech detection, we identify a wide range of hate of various types, resulting in an automatically labeled anti-China hate speech dataset. We identify a hateful rate in #china tweets of 2.5% in 2020 and 1.9% in 2021. This is well above the average rate of online hate speech on Twitter at 0.6% identified in Gao et al., 2017. We further analyzed the longitudinal development of #china tweets and those identified as hateful in 2020 and 2021 through visualizing the daily number and hate rate over the two years. Our keyword analysis of hate speech in #china tweets reveals the most frequently mentioned terms in the hateful #china tweets, which can be used for further social science studies.
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