Examining algorithmic biases in YouTube ? s recommendations of vaccine videos
Examining algorithmic biases in YouTube ? s recommendations of vaccine videos
复制标题
DOI：
10.1016/j.ijmedinf.2020.104175
复制
发表时间：
2020-08-01
影响因子：
4.9
通讯作者：
Gruzd, Anatoliy
中科院分区：
文献类型：
--
作者：
Abul-Fottouh, Deena;Song, Melodie Yunju;Gruzd, Anatoliy
目的调查YouTube如何推荐接种相关视频。材料与方法我们使用社交网络分析来评估YouTube如何向用户推荐接种相关视频。结果支持接种疫苗的视频(64.75%)多于反对接种疫苗的视频(19.98%)，15.27%的视频在情绪上是中立的。YouTube更有可能推荐中立和支持疫苗的视频，而不是反对疫苗的视频。与我们之前的研究相比，支持疫苗的视频更有可能推荐其他支持疫苗的视频，反之亦然。讨论与我们之前的研究相比，支持疫苗的视频的推荐数量显著增加，这表明YouTube对有害内容的妖魔化政策以及对推荐算法的其他更改可能有效地降低了反疫苗视频的可见度。然而，也有人担心，由于在推荐网络中观察到的同源效应，反对疫苗的视频不太可能将用户引导到支持疫苗的视频。结论该研究展示了YouTube的推荐系统对用户在YouTube上发现的疫苗信息类型的影响。最后，我们对算法透明度在YouTube等社交媒体平台如何决定向用户推荐哪些内容方面的重要性进行了一般性讨论。
ObjectiveThis research examines how YouTube recommends vaccination-related videos.Materials and methodsWe used a social network analysis to evaluate how YouTube recommends vaccination related videos to its users.ResultsMore pro-vaccine videos (64.75%) than anti-vaccine (19.98%) videos are on YouTube, with 15.27% of videos being neutral in sentiment. YouTube was more likely to recommend neutral and pro-vaccine videos than anti-vaccine videos. There is a homophily effect in which pro-vaccine videos were more likely to recommend other pro-vaccine videos than anti-vaccine ones, and vice versa.DiscussionCompared to our prior study, the number of recommendations for pro-vaccine videos has significantly increased, suggesting that YouTube’s demonization policy of harmful content and other changes to their recommender algorithm might have been effective in reducing the visibility of anti-vaccine videos. However, there are concerns that anti-vaccine videos are less likely to lead users to pro-vaccine videos due to the homophily effect observed in the recommendation network.ConclusionThe study demonstrates the influence of YouTube’s recommender systems on the types of vaccine information users discover on YouTube. We conclude with a general discussion of the importance of algorithmic transparency in how social media platforms like YouTube decide what content to feature and recommend to its users.