The Effect of People Recommenders on Echo Chambers and Polarization

Cinus, Federico; Minici, Marco; Monti, Corrado; Bonchi, Francesco

doi:10.1609/icwsm.v16i1.19275

Cited by 27 publications

(14 citation statements)

References 35 publications

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“…Satisfaction is assumed as a proxy for the sustainability of the social media platform. Content diversity exposure could play an important role in countering the effects of filter bubbles (Bozdag and van den Hoven, 2015 ; Nikolov et al, 2015 ), echo chambers (Wolfowicz, 2015 ; Bessi, 2016 ; Gillani et al, 2018 ), and ultimately society polarization (Cinus et al, 2022 ). In the results shown in Figure 6 .…”

Section: Preliminary Experimental Resultsmentioning

confidence: 99%

Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion

Ognibene¹,

Taibi²,

Kruschwitz³

et al. 2023

Front. Artif. Intell.

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Social media have become an integral part of our lives, expanding our interlinking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand, however, some serious negative implications of social media have been repeatedly highlighted in recent years, pointing at various threats to society and its more vulnerable members, such as teenagers, in particular, ranging from much-discussed problems such as digital addiction and polarization to manipulative influences of algorithms and further to more teenager-specific issues (e.g., body stereotyping). The impact of social media—both at an individual and societal level—is characterized by the complex interplay between the users' interactions and the intelligent components of the platform. Thus, users' understanding of social media mechanisms plays a determinant role. We thus propose a theoretical framework based on an adaptive “Social Media Virtual Companion” for educating and supporting an entire community, teenage students, to interact in social media environments in order to achieve desirable conditions, defined in terms of a community-specific and participatory designed measure of Collective Well-Being (CWB). This Companion combines automatic processing with expert intervention and guidance. The virtual Companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term by balancing the level of social media threats the users are exposed to, and in the long term by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. We put an emphasis on experts and educators in the educationally managed social media community of the Companion. They play five key roles: (a) use the Companion in classroom-based educational activities; (b) guide the definition of the CWB; (c) provide a hierarchical structure of learning strategies, objectives and activities that will support and contain the adaptive sequencing algorithms of the CWB-RS based on hierarchical reinforcement learning; (d) act as moderators of direct conflicts between the members of the community; and, finally, (e) monitor and address ethical and educational issues that are beyond the intelligent agent's competence and control. This framework offers a possible approach to understanding how to design social media systems and embedded educational interventions that favor a more healthy and positive society. Preliminary results on the performance of the Companion's components and studies of the educational and psychological underlying principles are presented.

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Section: Preliminary Experimental Resultsmentioning

confidence: 99%

Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion

Ognibene¹,

Taibi²,

Kruschwitz³

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…Simulation-based studies in recommender systems. Cinus et al (2022) combine link recommendation and opiniondynamics models in a simulation-based framework, to assess the effect of people recommenders on the evolution of opinions in a social network. They show that, if the initial network exhibits high level of homophily, people recommenders can help creating echo chambers and polarization.…”

Section: Related Workmentioning

confidence: 99%

Exposure Inequality in People Recommender Systems: The Long-Term Effects

Fabbri

Croci

Bonchi³

et al. 2022

ICWSM

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People recommender systems may affect the exposure that users receive in social networking platforms, influencing attention dynamics and potentially strengthening pre-existing inequalities that disproportionately affect certain groups. In this paper we introduce a model to simulate the feedback loop created by multiple rounds of interactions between users and a link recommender in a social network. This allows us to study the long-term consequences of those particular recommendation algorithms. Our model is equipped with several parameters to control: (i) the level of homophily in the network, (ii) the relative size of the groups, (iii) the choice among several state-of-the-art link recommenders, and (iv) the choice among three different stochastic user behavior models, that decide which recommendations are accepted or rejected. Our extensive experimentation with the proposed model shows that a minority group, if homophilic enough, can get a disproportionate advantage in exposure from all link recommenders. Instead, when it is heterophilic, it gets underexposed. Moreover, while the homophily level of the minority affects the speed of the growth of the disparate exposure, the relative size of the minority affects the magnitude of the effect. Finally, link recommenders strengthen exposure inequalities at the individual level, exacerbating the“rich-get-richer” effect: this happens for both the minority and the majority class and independently of their level of homophily.

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“…Despite a multitude of studies of polarization on social media (1-3), it remains disputed whether political polarization in digital public spaces is on the rise. Many analyses conclude that polarization is rapidly advancing (4)(5)(6)(7)(8), while others question this interpretation (9,10). The timeliness and relevance of this issue warrant a closer look at how these claims are made and raise an important question: How can we accurately quantify the level of polarization of a social system?…”

Section: Introductionmentioning

confidence: 99%

Quantifying ideological polarization on a network using generalized Euclidean distance

2023

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An intensely debated topic is whether political polarization on social media is on the rise. We can investigate this question only if we can quantify polarization, by taking into account how extreme the opinions of the people are, how much they organize into echo chambers, and how these echo chambers organize in the network. Current polarization estimates are insensitive to at least one of these factors: They cannot conclusively clarify the opening question. Here, we propose a measure of ideological polarization that can capture the factors we listed. The measure is based on the generalized Euclidean distance, which estimates the distance between two vectors on a network, e.g., representing people’s opinion. This measure can fill the methodological gap left by the state of the art and leads to useful insights when applied to real-world debates happening on social media and to data from the U.S. Congress.

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The Effect of People Recommenders on Echo Chambers and Polarization

Cited by 27 publications

References 35 publications

Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion

Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion

Exposure Inequality in People Recommender Systems: The Long-Term Effects

Quantifying ideological polarization on a network using generalized Euclidean distance

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