Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026
Examining the Impact of YouTube's Video Recommendation Algorithm on Pro- or Anti-Tobacco Messaging.
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Pearson George D H, Silver Nathan A, Koris Kristiann, McKay Tatum, Schillo Barbara A, Rath Jessica M
Аннотация
INTRODUCTION: Information seeking is among the most common uses of YouTube, the most popular social media site among youth. YouTube's recommendation algorithm drives approximately 70% of views but content quality varies greatly. Thus, it is important to understand how YouTube's algorithm impacts the content viewed by those seeking information on tobacco.
METHODS: The most common YouTube queries for e-cigarettes, oral nicotine products, cigarillos and nicotine (from Google trends) were used to create a dataset comprising of unique starting ("seed") videos paired with their recommended videos on the website sidebar, creating N = 5182 potential "journeys" from seed to recommended video. Video descriptions were coded for tobacco relevancy, pro or anti-tobacco stance (k = .94), and source type (e.g., organic creators, media outlets, public health institutions, or self-defined medical experts) (mean k = .88). The viewpoint and source type of journeys were later analyzed.
RESULTS: While most journeys led to recommended videos agreeing with the "seed's" stance (56.1%), 10.4% of journeys starting with pro-tobacco videos led to anti-tobacco videos, while 13.3% of journeys starting with anti-tobacco videos led to pro-tobacco videos. Pro- to anti-tobacco journeys most frequently led to content created by news (29.7%) or Self Described Medical Expert sources (28.1%). However, among anti-to pro journeys, 81.6% led to SDME videos.
CONCLUSIONS: SDMEs play a key role in driving discordant journeys from anti- to pro-tobacco videos, potentially exposing health information seekers to content that may undermine their health goals. Future research is needed to understand the ways recommendation feeds influence content selection and exposure to videos that promote tobacco use.
IMPLICATIONS: As the majority of views on YouTube are a result of the platform's algorithm, public health campaigners and practitioners need to be aware of what videos their content appears recommended on (only 10% of recommendations on pro-tobacco content led to anti-tobacco content), as well as what videos users may be recommended when watching their content (as shown by the majority of anti- to pro-tobacco journeys leading to SDMEs). Analyzing the types of videos recommended to users by the YouTube algorithm allows researchers, practitioners and regulators to better understand how populations become exposed to pro- and anti-tobacco messaging.
METHODS: The most common YouTube queries for e-cigarettes, oral nicotine products, cigarillos and nicotine (from Google trends) were used to create a dataset comprising of unique starting ("seed") videos paired with their recommended videos on the website sidebar, creating N = 5182 potential "journeys" from seed to recommended video. Video descriptions were coded for tobacco relevancy, pro or anti-tobacco stance (k = .94), and source type (e.g., organic creators, media outlets, public health institutions, or self-defined medical experts) (mean k = .88). The viewpoint and source type of journeys were later analyzed.
RESULTS: While most journeys led to recommended videos agreeing with the "seed's" stance (56.1%), 10.4% of journeys starting with pro-tobacco videos led to anti-tobacco videos, while 13.3% of journeys starting with anti-tobacco videos led to pro-tobacco videos. Pro- to anti-tobacco journeys most frequently led to content created by news (29.7%) or Self Described Medical Expert sources (28.1%). However, among anti-to pro journeys, 81.6% led to SDME videos.
CONCLUSIONS: SDMEs play a key role in driving discordant journeys from anti- to pro-tobacco videos, potentially exposing health information seekers to content that may undermine their health goals. Future research is needed to understand the ways recommendation feeds influence content selection and exposure to videos that promote tobacco use.
IMPLICATIONS: As the majority of views on YouTube are a result of the platform's algorithm, public health campaigners and practitioners need to be aware of what videos their content appears recommended on (only 10% of recommendations on pro-tobacco content led to anti-tobacco content), as well as what videos users may be recommended when watching their content (as shown by the majority of anti- to pro-tobacco journeys leading to SDMEs). Analyzing the types of videos recommended to users by the YouTube algorithm allows researchers, practitioners and regulators to better understand how populations become exposed to pro- and anti-tobacco messaging.