Social learning strategies for matters of taste

Analytis, Pantelis P.; Barkoczi, Daniel; Herzog, Stefan M.

doi:10.1038/s41562-018-0343-2

Cited by 29 publications

(27 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a pathbreaking experimental study, Salganik et al (2006) found that information on other listener's musical preferences results in an amplified inequality of popularity when compared to a world of independent listeners. Using social cues in the form of aggregated information might be beneficial for individuals in cultural markets in which preference is a matter of taste, but there are multiple strategies to leverage such information and its fit varies between individuals (Analytis et al, 2018). In the case of artists, during their careers, "small differences in talent become magnified in larger earning differences" (Rosen, 1981).…”

Section: Research Backgroundmentioning

confidence: 99%

Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries

Bello

García

2021

Humanit Soc Sci Commun

View full text Add to dashboard Cite

The digitization of music has changed how we consume, produce, and distribute music. In this paper, we explore the effects of digitization and streaming on the globalization of popular music. While some argue that digitization has led to more diverse cultural markets, others consider that the increasing accessibility to international music would result in a globalized market where a few artists garner all the attention. We tackle this debate by looking at how cross-country diversity in music charts has evolved over 4 years in 39 countries. We analyze two large-scale datasets from Spotify, the most popular streaming platform at the moment, and iTunes, one of the pioneers in digital music distribution. Our analysis reveals an upward trend in music consumption diversity that started in 2017 and spans across platforms. There are now significantly more songs, artists, and record labels populating the top charts than just a few years ago, making national charts more diverse from a global perspective. Furthermore, this process started at the peaks of countries’ charts, where diversity increased at a faster pace than at their bases. We characterize these changes as a process of Cultural Divergence, in which countries are increasingly distinct in terms of the music populating their music charts.

show abstract

Section: Research Backgroundmentioning

confidence: 99%

Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries

Bello

García

2021

Humanit Soc Sci Commun

View full text Add to dashboard Cite

show abstract

“…In contrast to these previous works, in many daily choices under social influence, one generally considers not only one, but several sources of social information, and these sources are rarely chosen randomly [ 41 ]. Even when not actively selecting information sources, one routinely experiences recommended content (e.g., books on Amazon, movies on Netflix, or videos on YouTube) generated by algorithms which incorporate our ‘‘tastes’’ (i.e., previous choices) and that of (similar) others [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Crowd control: Reducing individual estimation bias by sharing biased social information

2021

View full text Add to dashboard Cite

Cognitive biases are widespread in humans and animals alike, and can sometimes be reinforced by social interactions. One prime bias in judgment and decision-making is the human tendency to underestimate large quantities. Previous research on social influence in estimation tasks has generally focused on the impact of single estimates on individual and collective accuracy, showing that randomly sharing estimates does not reduce the underestimation bias. Here, we test a method of social information sharing that exploits the known relationship between the true value and the level of underestimation, and study if it can counteract the underestimation bias. We performed estimation experiments in which participants had to estimate a series of quantities twice, before and after receiving estimates from one or several group members. Our purpose was threefold: to study (i) whether restructuring the sharing of social information can reduce the underestimation bias, (ii) how the number of estimates received affects the sensitivity to social influence and estimation accuracy, and (iii) the mechanisms underlying the integration of multiple estimates. Our restructuring of social interactions successfully countered the underestimation bias. Moreover, we find that sharing more than one estimate also reduces the underestimation bias. Underlying our results are a human tendency to herd, to trust larger estimates than one’s own more than smaller estimates, and to follow disparate social information less. Using a computational modeling approach, we demonstrate that these effects are indeed key to explain the experimental results. Overall, our results show that existing knowledge on biases can be used to dampen their negative effects and boost judgment accuracy, paving the way for combating other cognitive biases threatening collective systems.

show abstract

“…collaborative filtering algorithms (Das et al 2007;Koren and Bell 2015;Ricci, Rokach, and Shapira 2011). Recently, researchers have shown that individual social influence can be affected by an individual's position in the population distribution and similarity with others (Pipergias Analytis et al 2020;Analytis, Barkoczi, and Herzog 2018). Similar findings may thus be observed on more realistic content recommendation algorithms.…”

Section: Discussionmentioning

confidence: 84%

Indirect Causal Influence of a Single Bot on Opinion Dynamics Through a Simple Recommendation Algorithm

Pescetelli

Barkoczi

Cebrián

2022

Studies in Computational Intelligence

Self Cite

View full text Add to dashboard Cite

The ability of social and political bots to influence public opinion is often difficult to estimate. Recent studies found that hyper-partisan accounts often directly interact with already highly polarised users on Twitter and are unlikely to influence the general population's average opinion. In this study, we suggest that social bots, trolls and zealots may affect people's views not just via a direct interaction (e.g. retweets, at-mentions and likes) and via indirect causal pathways through infiltrating platforms' content recommendation systems. Using a simple agent-based opinion-dynamics simulation, we isolate the effect of a single bot -representing only 1% of the population -on the average opinion of Bayesian agents when we remove all direct connections between the bot and human agents. We compare this experimental condition with an identical baseline condition where such a bot is absent. We used the same random seed in both simulations so that all other conditions remained identical. Results show that, even in the absence of direct connections, the mere presence of the bot is sufficient to shift the average population opinion. Furthermore, we observe that the presence of the bot significantly affects the opinion of almost all agents in the population. Overall, these findings offer a proof of concept that bots and hyperpartisan accounts can influence average population opinions not only by directly interacting with human accounts but also by shifting platforms' recommendation engines' internal representations.

show abstract

Social learning strategies for matters of taste

Cited by 29 publications

References 89 publications

Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries

Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries

Crowd control: Reducing individual estimation bias by sharing biased social information

Indirect Causal Influence of a Single Bot on Opinion Dynamics Through a Simple Recommendation Algorithm

Contact Info

Product

Resources

About