Radicalization and Polarisation in New Media: The Case of YouTube

YouTube拥有比其他任何社交网站都多的用户(Auxier和Anderson，2021)，它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，它是一个娱乐的游乐场，是一个可以交流想法和发展新观点的聚集地。然而，YouTube作为代言人的角色引发了很多争议。有人认为，该平台可能会导致观点的激进化和两极分化。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。错误信息与平台上的激进化或两极分化之间的相互作用尚未被彻底探索。该项目使用新的文本和视觉数据，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主张。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化措施。我将增加缩略图以增加预测能力。使用视频内容作为变量在这一领域是一个新颖的介绍，而以往的研究主要集中在使用评论。将视频内容添加到此分析中，改进了以前研究激进化的方法。该项目的第一部分涉及激进化。YouTube的推荐系统是用户在该平台上浏览视频的一个关键方面。坊间证据表明，YouTube的算法往往会将观众引导到更极端的内容(Tufekci，2018)。尽管如此，很少有研究调查该算法对用户暴露在更极端内容中的影响。这项研究将从识别多个主题(如疫苗接种、全球变暖、选举)的种子视频开始，并设计一个算法来导航YouTube算法提供的推荐网络。缩略图和评论将从每个视频收集。根据这篇文章和可视数据，我们将构建激进化的措施。然后，这些数据将被用来正式量化我们在网络中移动时激进化的变化。该项目的第二部分涉及两极分化。利用文本数据，我们试图构建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的渠道。对广泛而密集的边际变化的分析使我能够描绘出极化是如何随着时间的推移而演变的。为了测量极化，我们遵循了与Gentzkow等人使用的类似的方法。(2019)，他利用美国国会演讲来衡量政治两极分化。我们引入了一种选择模型，以捕捉视频创建者对内容的选择。脚本文本数据用于测量视频内容。然后，通过估计该模型的选择概率来构建极化度量。这些可以被解释为观察者可以很容易地猜测视频的意识形态来源，因为创作者只选择了一个短语。我们概括了根茨科夫等人的S指标，允许它纳入多维意识形态来源，这更适合YouTube。早期的两极分化指标被认为是有偏见的(Gentzkow等人，2019年)。这是因为选择集相对于所观察到的短语选择来说很大。为了解决这个问题，我们将使用剔除和正则化估计器等策略。

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.