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Unveiling the Veiled Threat: The Impact of Bots on COVID-19 Health Communication 揭开隐性威胁的面纱:机器人对 COVID-19 健康传播的影响
IF 4.1 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-10 DOI: 10.1177/08944393241275641
Ali Unlu, Sophie Truong, Nitin Sawhney, Tuukka Tammi
This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated between misinformation and vaccine stance detection. Bot-like Twitter accounts were identified using the Botometer software, and further analysis was implemented to distinguish COVID-19 specific bot accounts from regular bots. Network analysis illuminated the communication patterns of COVID-19 bots within retweet and mention networks. The findings reveal that these bots exhibit distinct characteristics and tactics that enable them to influence public discourse, particularly showing an increased activity in COVID-19-related conversations. Topic modeling analysis uncovers that COVID-19 bots predominantly focused on themes such as safety, political/conspiracy theories, and personal choice. The study highlights the critical need to develop effective strategies for detecting and countering bot influence. Essential actions include using clear and concise language in health communications, establishing strategic partnerships during crises, and ensuring the authenticity of user accounts on digital platforms. The findings underscore the pivotal role of bots in propagating misinformation related to COVID-19 and vaccines, highlighting the necessity of identifying and mitigating bot activities for effective intervention.
本文介绍了一项综合研究的结果,该研究考察了三年来机器人对推特上传播 COVID-19 错误信息和负面疫苗立场的影响。研究采用了三方方法:文本分类、主题建模和网络分析来探讨这一现象。文本分类利用图尔库大学 FinBERT 预训练嵌入模型,区分了错误信息和疫苗立场检测。使用 Botometer 软件识别了类似机器人的 Twitter 账户,并通过进一步分析将 COVID-19 特定机器人账户与普通机器人账户区分开来。网络分析揭示了 COVID-19 机器人在转发和提及网络中的传播模式。研究结果表明,这些机器人表现出了与众不同的特征和策略,使其能够影响公众言论,尤其是在与 COVID-19 相关的对话中表现出更高的活跃度。主题建模分析发现,COVID-19 机器人主要关注安全、政治/阴谋论和个人选择等主题。这项研究强调了制定有效策略来检测和对抗僵尸影响的迫切需要。基本行动包括在健康传播中使用简洁明了的语言,在危机期间建立战略合作伙伴关系,以及确保数字平台上用户账户的真实性。研究结果强调了机器人在传播与 COVID-19 和疫苗有关的错误信息中的关键作用,突出了识别和减少机器人活动以进行有效干预的必要性。
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引用次数: 0
To Follow or Not to Follow: Estimating Political Opinion From Twitter Data Using a Network-Based Machine Learning Approach 关注或不关注:使用基于网络的机器学习方法从推特数据中估计政治观点
IF 4.1 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-04 DOI: 10.1177/08944393241279418
Nils Brandenstein, Christian Montag, Cornelia Sindermann
Studying political opinions of citizens stands as a fundamental pursuit for both policymakers and researchers. While traditional surveys remain the primary method to investigate individual political opinions, the advent of social media data (SMD) offers novel prospects. However, the number of studies using SMD to extract individuals’ political opinions are limited and differ greatly in their methodological approaches and levels of success. Recent studies highlight the benefits of analyzing individuals’ social media network structure to estimate political opinions. Nevertheless, current methodologies exhibit limitations, including the use of simplistic linear models and a predominant focus on samples from the United States. Addressing these issues, we employ an unsupervised Variational Autoencoder (VAE) machine learning model to extract individual opinion estimates from SMD of N = 276 008 German Twitter (now called ’X’) users, compare its performance to a linear model and validate model estimates on self-reported opinion measures. Our findings suggest that the VAE captures Twitter users’ network structure more precisely, leading to higher accuracy in following decision predictions and associations with self-reported political ideology and voting intentions. Our study emphasizes the need for advanced analytical approaches capable to capture complex relationships in social media networks when studying political opinion, at least in non-US contexts.
研究公民的政治观点是政策制定者和研究人员的基本追求。虽然传统调查仍是调查个人政治观点的主要方法,但社交媒体数据(SMD)的出现提供了新的前景。然而,利用社交媒体数据提取个人政治观点的研究数量有限,而且在方法论和成功程度上也大相径庭。最近的研究强调了分析个人社交媒体网络结构来估计政治观点的好处。然而,目前的方法也有局限性,包括使用简单的线性模型和主要关注美国样本。为了解决这些问题,我们采用了一种无监督变异自动编码器(VAE)机器学习模型,从 N = 276 008 名德国 Twitter(现称为 "X")用户的 SMD 中提取个人意见估计值,将其性能与线性模型进行比较,并在自我报告的意见测量中验证模型估计值。我们的研究结果表明,VAE 能更精确地捕捉推特用户的网络结构,从而提高关注决策预测的准确性,并与自我报告的政治意识形态和投票意向相关联。我们的研究强调,在研究政治观点时,至少在非美国背景下,需要能够捕捉社交媒体网络中复杂关系的先进分析方法。
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引用次数: 0
Does the Media’s Partisanship Influence News Coverage on Artificial Intelligence Issues? Media Coverage Analysis on Artificial Intelligence Issues 媒体的党派倾向会影响对人工智能问题的新闻报道吗?人工智能问题的媒体报道分析
IF 4.1 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-02 DOI: 10.1177/08944393241268526
Mikyung Chang
This study aims to analyze news coverage on artificial intelligence (AI) issues and highlight the characteristics and differences in reporting based on media partisanship. By examining AI-related news in the South Korean media, this study reveals how conservative and progressive outlets frame the issue differently. The analysis found that conservative media coverage predominantly focuses on positive aspects, emphasizing development value frames such as the benefits and societal progress brought by AI. In contrast, progressive media often highlight crisis value frames, focusing on issues like side effects, ethical concerns, and legislation surrounding AI. These partisan differences reflect fundamental societal priorities and influence public discourse and policy agendas. Understanding media framing is crucial for fostering informed public dialogue on the societal significance of AI and promoting evidence-based decision-making. By recognizing partisan biases and critically evaluating media coverage, citizens can engage in constructive discourse beyond ideological divides. This study underscores the role of the media in promoting interdisciplinary discussions about the future trajectory of AI and in preparing society for its impacts. Ultimately, evidence-based public discourse is essential for shaping responsible AI policies and mitigating potential risks in the digital age.
本研究旨在分析有关人工智能(AI)问题的新闻报道,并强调基于媒体党派立场的报道特点和差异。通过研究韩国媒体中与人工智能相关的新闻,本研究揭示了保守派和进步派媒体是如何以不同的方式报道这一问题的。分析发现,保守派媒体的报道主要集中在积极方面,强调发展价值框架,如人工智能带来的好处和社会进步。相比之下,进步媒体往往强调危机价值框架,关注人工智能的副作用、伦理问题和立法等问题。这些党派差异反映了基本的社会优先事项,并影响着公共讨论和政策议程。要就人工智能的社会意义促进知情的公共对话,并推动基于证据的决策,了解媒体的框架至关重要。通过认识党派偏见并批判性地评估媒体报道,公民可以超越意识形态分歧参与建设性对话。本研究强调了媒体在促进有关人工智能未来发展轨迹的跨学科讨论以及为社会应对其影响做好准备方面所发挥的作用。最终,以证据为基础的公共讨论对于制定负责任的人工智能政策和降低数字时代的潜在风险至关重要。
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引用次数: 0
TikTok Brain: An Investigation of Short-Form Video Use, Self-Control, and Phubbing 嘀嗒大脑对短视频使用、自控力和幻觉的研究
IF 4.1 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-29 DOI: 10.1177/08944393241279422
Meredith E. David, James A. Roberts
Phubbing (phone snubbing) has become the norm in (im)polite society. A vast majority of US adults report using their phones during a recent social interaction. Using one’s phone in the presence of others has been shown to have a negative impact on relationships among co-workers, friends, family, and romantic partners. Recent research suggests viewing short-form videos (SFVs) (e.g., TikTok) is more addictive/immersive than traditional social media (e.g., Facebook) leading to a greater likelihood of phubbing others. Across two studies, the present research investigates the relationship between SFV viewing and phubbing and the possible mediating effect of self-control. We also test whether TikTok has a stronger relationship with phubbing than Instagram Reels and YouTube Shorts, two popular SFV purveyors. Study 1 (282 college students) finds that viewing TikTok videos is positively associated with phubbing others and this relationship is mediated by self-control. Interestingly, Study 1 also finds that this relationship does not hold for Instagram Reels and YouTube shorts. Using two different measures of self-control, Study 2 (198 adults) provides additional support for the mediating effect of self-control on the SFV viewing—phubbing relationship. Again, the model is only supported for TikTok SFV viewing, not Instagram or YouTube. In sum, the viewing of carefully curated short TikTok videos, often 30–60 seconds in length, undermines self-control which is associated with increased phubbing behavior. Implications of the present study’s findings expand far beyond phubbing. Self-control plays a central role in nearly all human decision making and behavior. Suggestions for future research are offered.
在(不)礼貌的社会中,Phubbing(抢手机)已成为一种常态。绝大多数美国成年人都表示在最近的社交活动中使用过手机。事实证明,在他人面前使用手机会对同事、朋友、家人和恋人之间的关系产生负面影响。最近的研究表明,观看短视频(SFV)(如 TikTok)比观看传统社交媒体(如 Facebook)更容易上瘾/沉浸其中,从而导致更有可能使用手机与他人聊天。通过两项研究,本研究调查了观看 SFV 与辱骂他人之间的关系,以及自我控制可能产生的中介效应。我们还测试了 TikTok 是否比 Instagram Reels 和 YouTube Shorts 这两个流行的 SFV 传播者与钓鱼行为有更强的关系。研究 1(282 名大学生)发现,观看 TikTok 视频与 "蹭热度 "正相关,而这种关系是由自控力中介的。有趣的是,研究 1 还发现这种关系在 Instagram Reels 和 YouTube 短片中并不成立。研究 2(198 名成人)使用了两种不同的自控力测量方法,进一步证实了自控力对观看自制视频与辱骂他人之间关系的中介作用。同样,该模型只支持 TikTok SFV 观看,而不支持 Instagram 或 YouTube。总之,观看经过精心策划的 TikTok 短视频(通常长度为 30-60 秒)会削弱自控力,而自控力又与蹭网行为的增加有关。本研究结果的意义远不止于视频聊天。自我控制在人类几乎所有的决策和行为中都起着核心作用。本研究为今后的研究提出了建议。
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引用次数: 0
CORA: An Open-Source Software Tool for Combinational Regularity Analysis CORA:用于组合规律性分析的开源软件工具
IF 4.1 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-29 DOI: 10.1177/08944393241275640
Lusine Mkrtchyan, Alrik Thiem, Zuzana Sebechlebská
Modern Configurational Comparative Methods (CCMs), such as Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), have gained in popularity among social scientists over the last thirty years. A new CCM called Combinational Regularity Analysis (CORA) has recently joined this family of methods. In this article, we provide a software tutorial for the open-source package CORA, which implements the eponymous method. In particular, we demonstrate how to use CORA to discover shared causes of complex effects and how to interpret corresponding solutions correctly, how to mine configurational data to identify minimum-size tuples of solution-generating inputs, and how to visualize solutions by means of logic diagrams.
定性比较分析法(QCA)和巧合分析法(CNA)等现代配置比较法(CCM)在过去三十年里越来越受到社会科学家的青睐。最近,一种名为 "组合规律性分析(CORA)"的新 CCM 也加入了这一方法家族。在本文中,我们将为实现同名方法的 CORA 开源软件包提供软件教程。特别是,我们演示了如何使用 CORA 发现复杂效应的共同原因以及如何正确解释相应的解决方案,如何挖掘配置数据以识别解决方案生成输入的最小尺寸元组,以及如何通过逻辑图可视化解决方案。
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引用次数: 0
Remember, You Can Complete This Survey Online! Web Survey Links and QR Codes in a Mixed-Mode Web and Mail General Population Survey 请记住,您可以在线完成本调查!混合模式网络和邮件普通人群调查中的网络调查链接和 QR 代码
IF 4.1 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-24 DOI: 10.1177/08944393241277553
Kristen Olson, Amanda Ganshert
Recruitment materials for concurrent mixed-mode self-administered web and mail surveys must communicate information about multiple modes simultaneously. Providing the link to the web survey on the cover of the paper questionnaire or including a QR code to access the web survey may increase the visibility of the web mode and thus increase the proportion of people who participate via the web, but whether including either piece of information does so has received surprisingly little empirical attention. In this paper, we examine the results of experiments embedded in two general population probability-based concurrent mixed-mode surveys of Nebraska adults. First, in the Labor Availability Survey of the Greater Omaha Area, respondents were randomly assigned to receive the web link and login information on the cover or the paper questionnaire without this information (all had web information in the cover letter). We then replicated and extended this experiment in the Labor Availability Survey of Northeast Nebraska. The questionnaire cover experiment was fully crossed with the presence or absence of a QR code to access the web survey. Neither of these design features affected response rates or speed of response, but the link on the questionnaire significantly increased the proportion of respondents who participated by web and the QR code significantly increased the proportion of respondents who participated by smartphone. Sample composition was largely unaffected on most characteristics, although the respondent pool was less similar to the population on education when the link was on the questionnaire. About 20% of respondents used a smartphone when typing in a survey link, but virtually all respondents used a smartphone when scanning the QR code. Survey researchers can include a link on the cover of the questionnaire to increase web participation rates in mixed-mode surveys. QR codes can be used when smartphone participation is desired.
同时进行的混合模式自填式网络调查和邮件调查的招募材料必须同时传达多种模式的信息。在纸质问卷的封面上提供网络调查的链接或加入二维码以访问网络调查,可能会提高网络模式的可见度,从而增加通过网络参与调查的人数比例,但令人惊讶的是,这两种信息是否都能起到这样的作用,却很少受到实证研究的关注。在本文中,我们研究了在对内布拉斯加州成年人进行的两次基于普通人群概率的并行混合模式调查中嵌入的实验结果。首先,在大奥马哈地区劳动力可用性调查中,受访者被随机分配到收到封面上的网络链接和登录信息或没有这些信息的纸质问卷中(所有问卷的封面信中都有网络信息)。随后,我们在内布拉斯加州东北部劳动力可用性调查中复制并扩展了这一实验。问卷封面实验与是否有二维码访问网络调查完全交叉进行。这些设计特征都没有影响回复率或回复速度,但问卷上的链接显著提高了通过网络参与调查的受访者比例,而二维码则显著提高了通过智能手机参与调查的受访者比例。虽然在问卷上设置链接时,受访者的教育程度与人口的相似度较低,但大多数特征对样本组成基本没有影响。约 20% 的受访者在输入调查链接时使用了智能手机,但几乎所有受访者在扫描二维码时都使用了智能手机。调查研究人员可以在问卷封面上加入链接,以提高混合模式调查中的网络参与率。当需要智能手机参与时,可以使用 QR 代码。
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引用次数: 0
Understanding Narratives of Uncertainty in Fertility Intentions of Dutch Women: A Neural Topic Modeling Approach 理解荷兰妇女生育意愿中的不确定性叙述:神经主题建模方法
IF 4.1 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-24 DOI: 10.1177/08944393241269406
Xiao Xu, Anne Gauthier, Gert Stulp, Antal van den Bosch
Uncertainty in fertility intentions is a major obstacle to understanding contemporary trends in fertility decision-making and its outcomes. Quantifying this uncertainty by structural factors such as income, ethnicity, and housing conditions is recognized as insufficient. A recently proposed framework on subjective narratives has opened up a new way to gauge factors behind fertility decision-making and uncertainty. Through surveys, such narratives can be elicited with open-ended questions (OEQs). However, analyzing answers to OEQs typically involves extensive human coding, imposing constraints on sample size. Natural Language Processing (NLP) techniques assist researchers in grasping aspects of the underlying reasoning behind responses with much less human effort. In this study, using automatic neural topic modeling methods, we identify and interpret topics and themes underlying the narratives on fertility intention uncertainty of women in the Netherlands. We used Contextualized Topic Models (CTMs), a neural topic model using pre-trained representations of Dutch language, to conduct our analyses. Our results show that nine topics dominate the narratives about fertility planning, with age and health-related issues as the most prominent ones. In addition, we found that uncertainty in fertility intentions is not homogeneous, as women who feel uncertain due to real-life constraints and those who have no fertility plans at all put their stress on vastly different narratives.
生育意愿的不确定性是理解当代生育决策趋势及其结果的主要障碍。根据收入、种族和住房条件等结构性因素对这种不确定性进行量化被认为是不够的。最近提出的主观叙述框架为衡量生育决策和不确定性背后的因素开辟了一条新途径。通过调查,可以用开放式问题(OEQs)引出此类叙述。然而,分析开放式问题的答案通常需要大量的人工编码,这对样本量造成了限制。自然语言处理(NLP)技术可以帮助研究人员以更少的人力掌握回答背后的基本推理。在本研究中,我们使用自动神经主题建模方法,识别并解释了荷兰妇女关于生育意愿不确定性的叙述背后的主题和题材。我们使用语境化主题模型 (CTM)(一种使用预先训练的荷兰语表征的神经主题模型)进行分析。结果显示,在有关生育计划的叙述中,有九个话题占据主导地位,其中年龄和健康相关问题最为突出。此外,我们还发现,生育意愿的不确定性并不一致,因为因现实生活限制而感到不确定的妇女和根本没有生育计划的妇女所强调的叙述内容大相径庭。
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引用次数: 0
Video Game Feedback Learning and Aggressive or Prosocial Effects 电子游戏反馈学习与攻击性或亲社会效应
IF 4.1 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-23 DOI: 10.1177/08944393241277556
Boyu Qiu, Wei Zhang
There is a close connection between video games and social life, and researchers are interested in whether and how video games shape aggression and prosocial behaviors. However, there are great inconsistencies across studies on this topic. These mixed results may be due in part to a focus on learning models that were relevant in research on traditional media like television but are less useful in research on video games. Unlike other media, video games are characterized by frequent game-player interactions and immediate feedback, and there is evidence that in-game rewards and punishments can shape aggressive or prosocial behavior inside and outside the game. We argue that reinforcement learning may help us to understand the effects of video games on aggressive and prosocial behaviors, and propose a conceptual model based on this argument.
电子游戏与社会生活密切相关,研究人员对电子游戏是否以及如何影响攻击行为和亲社会行为很感兴趣。然而,有关这一主题的研究结果却很不一致。研究结果参差不齐的部分原因可能是,研究人员把重点放在了学习模型上,而这些模型在电视等传统媒体的研究中很有意义,但在电子游戏的研究中却不那么有用。与其他媒体不同,电子游戏的特点是游戏玩家之间的频繁互动和即时反馈,而且有证据表明,游戏中的奖励和惩罚可以塑造游戏内外的攻击性或亲社会行为。我们认为,强化学习可以帮助我们理解电子游戏对攻击行为和亲社会行为的影响,并基于这一论点提出了一个概念模型。
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引用次数: 0
Agents of Discord: Modeling the Impact of Political Bots on Opinion Polarization in Social Networks 不和谐代理:模拟政治机器人对社交网络舆论两极分化的影响
IF 4.1 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-16 DOI: 10.1177/08944393241270382
Hsiu-Chi Lu, Hsuan-wei Lee
The pervasive presence and influence of political bots have become the subject of extensive research in recent years. Studies have revealed that a significant percentage of active accounts are bots, contributing to the polarization of public sentiment online. This study employs an agent-based model in conducting computer simulations of complex social networks, to elucidate how bots, representing diverse ideological perspectives, exacerbate societal divisions. To investigate the dynamics of opinion diffusion and shed light on the phenomenon of polarization caused by the activities of political bots, we introduced bots into a bounded-confidence opinion dynamic model for different social networks, whereby the effects of bots on other agents were studied to provide a comprehensive understanding of their influence on opinion dynamics. The simulations showed that the symmetrical deployment of bots on both sides of the opinion spectrum intensifies polarization. These effects were observed within specific tolerance and homophily ranges, with low and high user tolerances slowing down polarization. Moreover, the average path length of the network and the centrality of the bots had a significant impact on the result. Finally, polarization tends to be lower when humans exhibit reduced confidence in bots. This research not only offers valuable insights into the implications of bot activities on the polarization of public opinion and current state of digital society but also provides suggestions to mitigate bot-driven polarization.
政治机器人的普遍存在和影响已成为近年来广泛研究的主题。研究显示,活跃账户中有很大一部分是机器人,这加剧了网络上公众情绪的两极分化。本研究采用基于代理的模型对复杂的社交网络进行计算机模拟,以阐明代表不同意识形态观点的机器人是如何加剧社会分化的。为了研究舆论扩散的动态,揭示政治机器人活动造成的两极分化现象,我们将机器人引入不同社交网络的有界信任舆论动态模型,研究机器人对其他代理的影响,以全面了解它们对舆论动态的影响。模拟结果表明,在舆论频谱两侧对称部署机器人会加剧两极分化。这些影响是在特定的容忍度和同质性范围内观察到的,低用户容忍度和高用户容忍度都会减缓极化。此外,网络的平均路径长度和机器人的中心性对结果也有显著影响。最后,当人类对机器人的信任度降低时,极化程度也会降低。这项研究不仅就机器人活动对舆论极化和数字社会现状的影响提供了宝贵的见解,还为缓解机器人驱动的极化提供了建议。
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引用次数: 0
Using Twitter to Detect Polling Place Issue Reports on U.S. Election Days 使用 Twitter 检测美国大选日投票站问题报告
IF 4.1 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-10 DOI: 10.1177/08944393241269420
Prathm Juneja, Luciano Floridi
In this article, we analyze whether Twitter can be used to detect relative reports of issues at polling places. We use 20,322 tweets geolocated to U.S. states that match a series of keywords on the 2010, 2012, 2014, 2016, and 2018 general election days. We fine-tune BERTweet, a pre-trained language model, using a training set of 6,365 tweets labeled as issues or non-issues. We develop a model with an accuracy of 96.9% and a recall of 72.2%, and another model with an accuracy of 90.5% and a recall of 93.5%, far exceeding the performance of baseline models. Based on these results, we argue that these BERTweet-based models are promising methods for detecting reports of polling place issues on U.S. election days. We suggest that outputs from these models can be used to supplement existing voter protection efforts and to research the impact of policies, demographics, and other variables on voting access.
在本文中,我们分析了 Twitter 是否可用于检测投票站问题的相关报告。我们使用了 2010、2012、2014、2016 和 2018 年大选日与一系列关键词相匹配的 20,322 条推文,这些推文的地理位置位于美国各州。我们使用标注为问题或非问题的 6365 条推文的训练集,对预先训练好的语言模型 BERTweet 进行了微调。我们开发的一个模型准确率为 96.9%,召回率为 72.2%,另一个模型准确率为 90.5%,召回率为 93.5%,远远超过基线模型的性能。基于这些结果,我们认为这些基于 BERTweet 的模型是检测美国大选日投票站问题报告的有效方法。我们建议,这些模型的输出结果可用于补充现有的选民保护工作,以及研究政策、人口统计和其他变量对投票机会的影响。
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引用次数: 0
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