Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children

Papadamou, Kostantinos; Papasavva, Antonis; Zannettou, Savvas; Blackburn, Jeremy; Kourtellis, Nicolas; Leontiadis, Ilias; Stringhini, Gianluca; Sirivianos, Michael

doi:10.1609/icwsm.v14i1.7320

Cited by 62 publications

(37 citation statements)

References 18 publications

(20 reference statements)

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“…Social media algorithms that prioritize attractively and revenue‐generating content over viewer safety may further contribute to exposure to harmful behaviors, as children and parents have less control over the content they engage in (Radesky et al., 2020). For instance, one study found that videos appropriate for children 1–5 years old lead to an inappropriate video recommendation 3.5% of the time within 10 recommendations (Papadamou et al., 2020). Social media platforms allow for anonymized interactions with fewer boundaries and regulations (Lowry, Zhang, Wang, & Siponen, 2016) and have demonstrated more propagation of hateful posts as compared to nonhateful ones (Mathew, Dutt, Goyal, & Mukherjee, 2019).…”

Section: Discussionmentioning

confidence: 99%

Contemporary screen time modalities and disruptive behavior disorders in children: a prospective cohort study

Chu

Ganson

Murray

et al. 2022

Child Psychology Psychiatry

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Background: Cross-sectional studies have demonstrated associations between screen time and disruptive behavior disorders (conduct disorder and oppositional defiant disorder); however, prospective associations remain unknown. This study's objective was to determine the prospective associations of contemporary screen time modalities with conduct and oppositional defiant disorder in a national cohort of 9-11-year-old children. Methods: We analyzed data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 11,875). Modified Poisson regression analyses were conducted to estimate the associations between baseline child-reported screen time (total and by modality) and parent-reported conduct or oppositional defiant disorder based on the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS-5) at 1-year follow-up, adjusting for potential confounders. Results: Participants reported an average of 4 hr of total screen time per day at baseline. Each hour of total screen time per day was prospectively associated with a 7% higher prevalence of conduct disorder (95% CI 1.03-1.11) and a 5% higher prevalence of oppositional defiant disorder (95% CI 1.03-1.08) at 1-year follow-up. Each hour of social media per day was associated with a 62% higher prevalence of conduct disorder (95% CI 1.39-1.87). Each hour of video chat (prevalence ratio [PR] 1.21, 95% CI 1.06-1.37), texting (PR 1.19, 95% CI 1.07-1.33), television/movies (PR 1.17, 95% CI 1.10-1.25), and video games (PR 1.14, 95% CI 1.07-1.21) per day was associated with a higher prevalence of the oppositional defiant disorder. When examining thresholds, exposure to >4 hr of total screen time per day was associated with a higher prevalence of conduct disorder (69%) and oppositional defiant disorder (46%). Conclusions: Higher screen time was prospectively associated with a higher prevalence of new-onset disruptive behavior disorders. The strongest association was between social media and conduct disorder, indicating that future research and interventions may focus on social media platforms to prevent conduct disorder.

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Section: Discussionmentioning

confidence: 99%

Contemporary screen time modalities and disruptive behavior disorders in children: a prospective cohort study

Chu

Ganson

Murray

et al. 2022

Child Psychology Psychiatry

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“…As our work measures a specific social phenomenon (the QAnon movement), we also rely on research that has measured other phenomena, for example, attacks on the Syrian White Helmets (Wilson and Starbird 2020), the Manosphere (Ribeiro et al 2021), disturbing videos targeting children (Papadamou et al 2020), and rumors in crisis events (Starbird et al 2018b). Most similar to our investigation is from Papasavva et al who investigate the posting behavior, emergent themes, and toxicity of the QAnon movement through characterizing the /v/GreatAwakening subcommunity on voat.co (Papasavva et al 2020).…”

Section: Related Workmentioning

confidence: 99%

No Calm in the Storm: Investigating QAnon Website Relationships

Hanley

Kumar

Durumeric

2022

ICWSM

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QAnon is a far-right conspiracy theory whose followers largely organize online. In this work, we use web crawls seeded from two of the largest QAnon hotbeds on the Internet, Voat and 8kun, to build a QAnon-centered domain-based hyperlink graph. We use this graph to identify, understand, and learn about the set of websites that spread QAnon content online. Specifically, we curate the largest list of QAnon centered websites to date, from which we document the types of QAnon sites, their hosting providers, as well as their popularity. We further analyze QAnon websites' connection to mainstream news and misinformation online, highlighting the outsized role misinformation websites play in spreading the conspiracy. Finally, we leverage the observed relationship between QAnon and misinformation sites to build a highly accurate random forest classifier that distinguishes between misinformation and authentic news sites. Our results demonstrate new and effective ways to study the growing presence of conspiracy theories and misinformation on the Internet.

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“…Figure 1 depicts the recommended video interface. The top 10 is the number that has been used in the literature for examining recommendation effects [43,44]. Note that this list was not the same list as our API search query for fenbendazole.…”

Section: Youtube Data Collectionmentioning

confidence: 99%

Understanding the Social Mechanism of Cancer Misinformation Spread on YouTube and Lessons Learned: Infodemiological Study

Yoon¹,

You²,

Kwon³

et al. 2022

J Med Internet Res

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Background A knowledge gap exists between the list of required actions and the action plan for countering cancer misinformation on social media. Little attention has been paid to a social media strategy for disseminating factual information while also disrupting misinformation on social media networks. Objective The aim of this study was to, first, identify the spread structure of cancer misinformation on YouTube. We asked the question, “How do YouTube videos play an important role in spreading information about the self-administration of anthelmintics for dogs as a cancer medicine for humans?” Second, the study aimed to suggest an action strategy for disrupting misinformation diffusion on YouTube by exploiting the network logic of YouTube information flow and the recommendation system. We asked the question, “What would be a feasible and effective strategy to block cancer misinformation diffusion on YouTube?” Methods The study used the YouTube case of the self-administration of anthelmintics for dogs as an alternative cancer medicine in South Korea. We gathered Korean YouTube videos about the self-administration of fenbendazole. Using the YouTube application programming interface for the query “fenbendazole,” 702 videos from 227 channels were compiled. Then, videos with at least 50,000 views, uploaded between September 2019 and September 2020, were selected from the collection, resulting in 90 videos. Finally, 10 recommended videos for each of the 90 videos were compiled, totaling 573 videos. Social network visualization for the recommended videos was used to identify three intervention strategies for disrupting the YouTube misinformation network. Results The study found evidence of complex contagion by human and machine recommendation systems. By exposing stakeholders to multiple information sources on fenbendazole self-administration and by linking them through a recommendation algorithm, YouTube has become the perfect infrastructure for reinforcing the belief that fenbendazole can cure cancer, despite government warnings about the risks and dangers of self-administration. Conclusions Health authorities should upload pertinent information through multiple channels and should exploit the existing YouTube recommendation algorithm to disrupt the misinformation network. Considering the viewing habits of patients and caregivers, the direct use of YouTube hospital channels is more effective than the indirect use of YouTube news media channels or government channels that report public announcements and statements. Reinforcing through multiple channels is the key.

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Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children

Cited by 62 publications

References 18 publications

Contemporary screen time modalities and disruptive behavior disorders in children: a prospective cohort study

Contemporary screen time modalities and disruptive behavior disorders in children: a prospective cohort study

No Calm in the Storm: Investigating QAnon Website Relationships

Understanding the Social Mechanism of Cancer Misinformation Spread on YouTube and Lessons Learned: Infodemiological Study

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