毒性的货币化：分析YouTube内容创作者与争议驱动的互动
The Monetisation of Toxicity: Analysing YouTube Content Creators and Controversy-Driven Engagement
AI总结：
本文通过构建包含20个争议YouTube频道、16,349个视频和超1.05亿条评论的数据集，结合货币化线索分类与毒性检测模型，揭示有毒评论虽提升互动却损害货币化，并公开了相关检测列表。
摘要：
YouTube是一个重要的社交媒体平台，在数字文化中扮演着重要角色，其核心是内容创作者。这些创作者经常从事有争议的行为以驱动互动，这可能助长毒性。本文对YouTube上的争议内容进行了定量分析，重点关注争议、毒性和货币化之间的关系。我们引入了一个经过整理的、包含20个从Reddit讨论中提取的争议性YouTube频道的数据集，其中包括16,349个视频和超过1.05亿条评论。我们使用URL和关键词列表，从视频描述中识别货币化线索并将其分类为各种模式，包括联盟营销和直接销售。此外，我们训练了一个机器学习模型来测量这些视频中评论的毒性。我们的研究结果表明，虽然有毒评论与更高的互动相关，但它们对货币化产生负面影响，这表明争议驱动的互动并不一定带来经济收益。我们还观察到货币化策略存在显著差异，一些创作者尽管毒性水平很高，仍表现出广泛的货币化。我们的研究引入了一个经过整理的数据集、用于分类货币化的URL和关键词列表、一个用于测量毒性的机器学习模型，是理解YouTube上争议、互动和货币化之间复杂关系的重要一步。用于检测和分类货币化线索的列表可在https://github.com/thalesbertaglia/toxmon上获取。
英文摘要：
YouTube is a major social media platform that plays a significant role in digital culture, with content creators at its core. These creators often engage in controversial behaviour to drive engagement, which can foster toxicity. This paper presents a quantitative analysis of controversial content on YouTube, focusing on the relationship between controversy, toxicity, and monetisation. We introduce a curated dataset comprising 20 controversial YouTube channels extracted from Reddit discussions, including 16,349 videos and more than 105 million comments. We identify and categorise monetisation cues from video descriptions into various models, including affiliate marketing and direct selling, using lists of URLs and keywords. Additionally, we train a machine learning model to measure the toxicity of comments in these videos. Our findings reveal that while toxic comments correlate with higher engagement, they negatively impact monetisation, indicating that controversy-driven interaction does not necessarily lead to financial gain. We also observed significant variation in monetisation strategies, with some creators showing extensive monetisation despite high toxicity levels. Our study introduces a curated dataset, lists of URLs and keywords to categorise monetisation, a machine learning model to measure toxicity, and is a significant step towards understanding the complex relationship between controversy, engagement, and monetisation on YouTube. The lists used for detecting and categorising monetisation cues are available on https://github.com/thalesbertaglia/toxmon.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
