Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube

Alvarado, Oscar; Heuer, Hendrik; Abeele, Vero Vanden; Breiter, Andreas; Verbert, Katrien

doi:10.1145/3415192

Cited by 42 publications

(24 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Research in this space began by investigating algorithmic experiences through qualitative methods such as in-depth interviews. Particularly in uncovering folk theories, qualitative studies about Facebook (Bucher, 2017), Google News (Powers, 2017), YouTube (Alvarado et al, 2020), Spotify (Siles et al, 2020), and TikTok (Klug et al, 2021) asked users to share their experiences with these platforms. These methods allow participants to express a wide range of emotions about and expectations of with algorithms in their own use, which can also vary widely by platform.…”

Section: Methodological Starting Pointsmentioning

confidence: 99%

“…Similarly, YouTube users show a high awareness of the algorithmic process that recommends content on the platform, but can only guess at what data it uses (Alvarado et al, 2020). Notably, TikTok users are acutely aware of the algorithms that shape their For You page and state that they regularly "train" the algorithm, by engaging with videos that encourage or discourage similar content from appearing, (Siles & Meléndez-Moran, 2021).…”

Section: The Evolving Algorithmic Literacy Of Social Media Usersmentioning

confidence: 99%

“…These users viewed the algorithm either as a surveillance buddy with a mutual interest and sharing good music recommendations or a feedback control system to be trained. On YouTube, users' beliefs vary widely about how content is presented to them, among which there is no explicit agreement (Alvarado et al, 2020). Those with previous action beliefs indicate that videos in one's feed are selected as a result of their own actions (e.g., search history, subscriptions), while social media beliefs recognize that others' behaviors (e.g., likes on videos) influence the content in their own feeds.…”

Section: Folk Theoriesmentioning

confidence: 99%

See 2 more Smart Citations

What Do We Know about Algorithmic Literacy? The Status Quo and a Research Agenda for a Growing Field

Oeldorf-Hirsch¹,

Neubaum²

2021

Preprint

View full text Add to dashboard Cite

The increasing emergence of algorithms in our daily use of technologies comes with a growing field of empirical research trying to understand how aware and knowledgeable individuals are about algorithms. This field is marked by a certain diversity in terms of how it theorizes and measures people’s literacy when interacting with algorithms. We propose converging on the term algorithmic literacy that covers different dimensions used by previous research. This article summarizes the state of knowledge on algorithmic literacy by systematically presenting initial steps in theory building and measurement development. Drawing on this, we propose an agenda including five different directions that future research could focus on: 1) theory building to understand algorithmic literacy, 2) addressing the algorithmic divide, 3) uncovering the relationship between algorithmic literacy and attitudes, 4) examining algorithmic literacy as predictor for user behavior, and 5) exploring ways to increase algorithmic literacy.

show abstract

Section: Methodological Starting Pointsmentioning

confidence: 99%

Section: The Evolving Algorithmic Literacy Of Social Media Usersmentioning

confidence: 99%

Section: Folk Theoriesmentioning

confidence: 99%

See 1 more Smart Citation

What Do We Know about Algorithmic Literacy? The Status Quo and a Research Agenda for a Growing Field

Oeldorf-Hirsch¹,

Neubaum²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…interact with YouTube's ML-based curation systems, think the system works [1]. Our semi-structured interviews with participants from Belgium, Costa Rica, and Germany show that users are aware of the existence of the recommendation system on YouTube, but that users' understanding of the system is limited.…”

mentioning

confidence: 88%

Helping People Deal With Disinformation -- A Socio-Technical Perspective

Heuer

2021

Preprint

Self Cite

View full text Add to dashboard Cite

At the latest since the advent of the Internet, disinformation and conspiracy theories have become ubiquitous. Recent examples like QAnon and Pizzagate prove that false information can lead to real violence. Sadly, this link between misinformation and violence has a long history. Anti-semitic conspiracy theories played a central role in the Shoa. In the context of the COVID-19 pandemic, the World Health Organization (WHO) warned that "misinformation costs lives" [13]. The WHO argued that mitigating the harm from misinformation is necessary to manage the COVID-19 infodemic. The recent increase in misinformation in a so-called Post-Truth Era can be linked to societal mega-trends such as a decline in social capital, growing economic inequality, increased polarization, declining trust in science, and an increasingly fractionated media landscape [9].My work is focused on disinformation. Following Claire Wardle [2], I operationalize disinformation as fabricated or deliberately manipulated content like conspiracy theories or rumors. As such, disinformation is a special case of misinformation. While disinformation is connected to an intent to harm, misinformation also includes unintentional mistakes. I believe that disinformation is a challenging, multifaceted phenomenon that requires an appropriate sociotechnical response. In my work, I research 1. why people believe in disinformation, 2. how people can be best supported in recognizing disinformation, and 3. what the potentials and risks of different tools designed to fight disinformation are.My work on disinformation is informed by my background in human-computer interaction and machine learning.The workshop would be a great opportunity for me to discuss the ethical implications of the disinformation detection solutions that I am developing. I would love to discuss ways of enabling fast and effective disinformation detection while ensuring that freedom of speech is protected. MACHINE LEARNING-BASED CURATION SYSTEMSTo understand disinformation in the contemporary media climate, one has to understand social media and the machine learning-based curation systems used on social media. My doctoral thesis provides a socio-technical perspective on users and machine learning-based curation systems [3]. The thesis presents actionable insights on how ML-based curation systems can and should be explained and audited. Motivated by the role that ML-based curation systems play in the dissemination of disinformation, I examined the user beliefs around such systems in detail. In a recent CSCW paper, I, together with my collaborators, examined how users without a technical background, who regularly Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

show abstract

“…Even though these systems organize, select and present information, the understanding of algorithmic curation is limited [22,55]. The prevalence of such opaque and ML-based systems has led to a variety of investigations of the user awareness of algorithmic curation, as work by Eslami et al [22], Rader and Gray [55], and Alvarado et al [2] proves. While YouTube's published research describes the general idea of their recommendation system in a paper by Covington, Adams & Sargin [13], it remains unclear how the system works and what factors it takes into account.…”

Section: Algorithmic Experiencementioning

confidence: 99%

Auditing the Biases Enacted by YouTube for Political Topics in Germany

Heuer,

Hoch,

Breiter

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

With YouTube's growing importance as a news platform, its recommendation system came under increased scrutiny. Recognizing YouTube's recommendation system as a broadcaster of media, we explore the applicability of laws that require broadcasters to give important political, ideological, and social groups adequate opportunity to express themselves in the broadcasted program of the service. We present audits as an important tool to enforce such laws and to ensure that a system operates in the public's interest. To examine whether YouTube is enacting certain biases, we collected video recommendations about political topics by following chains of ten recommendations per video. Our findings suggest that YouTube's recommendation system is enacting important biases. We find that YouTube is recommending increasingly popular but topically unrelated videos. The sadness evoked by the recommended videos decreases while the happiness increases. We discuss the strong popularity bias we identified and analyze the link between the popularity of content and emotions. We also discuss how audits empower researchers and civic hackers to monitor complex machine learning (ML)-based systems like YouTube's recommendation system.

show abstract

Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube

Cited by 42 publications

References 37 publications

What Do We Know about Algorithmic Literacy? The Status Quo and a Research Agenda for a Growing Field

What Do We Know about Algorithmic Literacy? The Status Quo and a Research Agenda for a Growing Field

Helping People Deal With Disinformation -- A Socio-Technical Perspective

Auditing the Biases Enacted by YouTube for Political Topics in Germany

Contact Info

Product

Resources

About