Radicalization and Polarisation in New Media: The Case of YouTube

新媒体中的激进化和两极分化：以 YouTube 为例

• 批准号: 2722475
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2722475
• 关键词: Radicalization; Polarisation; New; Media; Case

Having more users than any other social networking site (Auxier and Anderson, 2021), YouTube is one of the most influential forms of new media in modern society. As a platform it is a playground for entertainment and an agora in which ideas can be exchanged and new opinions can be developed. However, YouTube has caused much controversy with its role as an agora. It has been suggested that the platform can contribute to radicalization and polarisation of opinions. It is also blamed to be a hotbed of fake news. Unfortunately, there is little empirical evidence to substantiate these claims. The interaction between misinformation and radicalisation or polarisation on the platform hasn't been thoroughly explored.This project uses novel text and visual data, along with recent advances in machine learning (ML) and natural language processing (NLP) to formally assess these claims.Using the categories assigned to YouTube channels in Ribeiro et al. (2019) as labels, I will use ML algorithms to predict radicalisation measures of each video. I will augment thumbnail images to increase predictive power. The use of video content as a variable is a novel introduction in this area, whereas previous studies have mainly focused on using comments. Adding video content to this analysis improves upon previously used methods to study radicalization. The first part of the project concerns radicalisation. YouTube's recommendation system is a key aspect of the way in which users explore videos on the platform. Anecdotal evidence has suggested that YouTube's algorithm tends to lead viewers to content that is more extreme (Tufekci, 2018). In spite of this, few studies have investigated the effects of the algorithm on users' exposure to more extreme content.This research will begin by identifying seed videos across a number of topics (e.g., vaccination, global warming, elections) and designing an algorithm to navigate through the recommendation network provided by the YouTube algorithm. Thumbnail images and comments will be collected from each video. From this text and visual data, measures of radicalization will be constructed. These will then be used to formally quantify how radicalization changes as we move through the network. The second part of the project concerns polarisation. Using text data, we seek to construct measures of polarisation on YouTube. This will allow us to identify how polarisation has evolved over time, both within existing channels and through the creation of new channels. The analysis on changes across the extensive and intensive margins enable me to paint a picture of how polarisation has evolved over time.In order to measure polarisation we follow a method similar to that used in Gentzkow et al. (2019), who used US congressional speech to measure political polarisation. We introduce a choice model in order to capture content choices made by video creators. Transcript text data is used to measure video content. Polarisation measures are then constructed by estimating choice probabilities from this model. These can be interpreted as the ease with which an observer can guess the ideological origin of a video, given the creator's single choice of phrase. We generalise Gentzkow et al.'s measure, allowing it to incorporate multidimensional ideological origins, which are more suited to YouTube. Earlier polarisation measures are argued to be biased (Gentzkow et al., 2019). This is because the choice set is large relative to the choice of phrases that is observed. In order to address this, we will use strategies such as leave-out and regularised estimators.

YouTube 拥有比任何其他社交网站更多的用户（Auxier 和 Anderson，2021），是现代社会中最具影响力的新媒体形式之一。作为一个平台，它是一个娱乐游乐场，也是一个可以交流想法和发展新观点的集市。然而，YouTube因其集市的角色而引起了很多争议。有人认为，该平台可能会导致观点激进化和两极分化。它还被指责为假新闻的温床。不幸的是，几乎没有经验证据来证实这些说法。平台上的错误信息与激进化或两极分化之间的相互作用尚未得到彻底探索。该项目使用新颖的文本和视觉数据，以及机器学习 (ML) 和自然语言处理 (NLP) 的最新进展来正式评估这些说法。使用 Ribeiro 等人分配给 YouTube 频道的类别。 (2019) 作为标签，我将使用 ML 算法来预测每个视频的激进措施。我将增强缩略图以提高预测能力。使用视频内容作为变量是该领域的新颖介绍，而之前的研究主要集中在使用评论。在该分析中添加视频内容改进了以前使用的研究激进化的方法。该项目的第一部分涉及激进化。 YouTube 的推荐系统是用户在平台上浏览视频方式的一个关键方面。轶事证据表明，YouTube 的算法往往会引导观看者观看更极端的内容（Tufekci，2018）。尽管如此，很少有研究调查该算法对用户接触更极端内容的影响。这项研究将首先识别多个主题（例如疫苗接种、全球变暖、选举）的种子视频，并设计一种算法来导航 YouTube 算法提供的推荐网络。将从每个视频中收集缩略图和评论。根据这些文本和视觉数据，将构建激进化的措施。然后，这些将用于正式量化当我们在网络中移动时激进化如何变化。该项目的第二部分涉及极化。我们利用文本数据来构建 YouTube 上的两极分化衡量标准。这将使我们能够确定极化是如何随着时间的推移而演变的，无论是在现有渠道内还是通过创建新渠道。对广泛边缘和密集边缘变化的分析使我能够描绘出极化如何随时间演变的图景。为了测量极化，我们遵循与 Gentzkow 等人使用的方法类似的方法。 （2019），他使用美国国会演讲来衡量政治两极分化。我们引入了一个选择模型来捕获视频创作者做出的内容选择。文字记录数据用于衡量视频内容。然后通过估计该模型的选择概率来构建极化度量。这些可以解释为观察者在考虑到创作者对短语的单一选择的情况下猜测视频的意识形态起源的容易程度。我们概括了 Gentzkow 等人的措施，使其能够纳入多维意识形态起源，这更适合 YouTube。早期的极化措施被认为是有偏见的（Gentzkow 等人，2019）。这是因为选择集相对于观察到的短语选择来说很大。为了解决这个问题，我们将使用诸如遗漏和正则化估计器等策略。