Modelling how social network algorithms can influence opinion polarization

Arruda, Henrique Ferraz de; Cardoso, Felipe Maciel; Arruda, Guilherme Ferraz de; Hernández, Alexis R.; Costa, Luciano da Fontoura; Moreno, Yamir

doi:10.1016/j.ins.2021.12.069

Cited by 43 publications

(26 citation statements)

References 56 publications

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“…namely considering the minimum JSD. The best approximating distributions are shown in Figure 3, along with the optimal distribution obtained applying the same procedure to two recent opinion dynamics models, from Baumann et al [47] and Arruda et al [48]. The results are shown in Figure 3.…”

Section: Resultsmentioning

confidence: 91%

“…Left column: real opinion distributions measured in [23]. From second to last column: best approximation of the observed data provided by our and competing opinion dynamic models: Baumann [47] and Arruda [48].…”

Section: Resultsmentioning

confidence: 98%

“…In this paper, we address this issue, providing a double contribution. First, we propose a flexible opinion dynamic model to investigate the interplay between the users' attitudes and algorithmic influence; next, we perform a comparative analysis with other models [47,48] by fitting with data from different social media platforms. This allows us to establish a link between an observed opinion distribution and the corresponding level of underlying algorithmic bias, providing a synthetic description of each social media in terms of model parameters that could be used as a reference for applying polarization-reduction policies.…”

Section: Introductionmentioning

confidence: 99%

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The dynamics of online polarization

Valensise¹,

Cinelli²

2022

Preprint

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Several studies pointed out that users seek the information they like the most, filter out dissenting information, and join groups of like-minded users around shared narratives. Feed algorithms may burst such a configuration toward polarization, thus influencing how information (and misinformation) spreads online. However, despite the extensive evidence and data about polarized opinion spaces and echo chambers, the interplay between human and algorithmic factors in shaping these phenomena remains unclear. In this work, we propose an opinion dynamic model mimicking human attitudes and algorithmic features. We quantitatively assess the adherence of the model's prediction to empirical data and compare the model performances with other state-of-the-art models. We finally provide a synthetic description of social media platforms regarding the model's parameters space that may be used to fine-tune feed algorithms to eventually smooth extreme polarization.

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Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The dynamics of online polarization

Valensise¹,

Cinelli²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Real-world online social networks are instead temporal [59], with both nodes and links changing in time due to a variety of mechanisms and external factors influencing how people use media platforms and choose their acquaintances. If extended to temporal networks [29], our framework will potentially describe a feedback loop between algorithmic content filtering and network/state dynamics that segregates the social network into groups of similarly-minded people (as suggested by recent studies [60]), further promoting the polarization effect we already see in static networks. Even if our results focus on binary-state dynamics over simple networks, the rate-equation-based framework is flexible and can be extended to other dynamical descriptions, such as nonlinear dynamical systems with continuous variables [61,62] and higher-order network models [63].…”

Section: Discussionmentioning

confidence: 94%

The effect of algorithmic bias and network structure on coexistence, consensus, and polarization of opinions

Peralta,

Neri,

Kertész

et al. 2021

Preprint

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Individuals of modern societies share ideas and participate in collective processes within a pervasive, variable, and mostly hidden ecosystem of content filtering technologies that determine what information we see online. Despite the impact of these algorithms on daily life and society, little is known about their effect on information transfer and opinion formation. It is thus unclear to what extent algorithmic bias has a harmful influence on collective decision-making, such as a tendency to polarize debate. Here we introduce a general theoretical framework to systematically link models of opinion dynamics, social network structure, and content filtering. We showcase the flexibility of our framework by exploring a family of binary-state opinion dynamics models where information exchange lies in a spectrum from pairwise to group interactions. All models show an opinion polarization regime driven by algorithmic bias and modular network structure. The role of content filtering is, however, surprisingly nuanced; for pairwise interactions it leads to polarization, while for group interactions it promotes coexistence of opinions. This allows us to pinpoint which social interactions are robust against algorithmic bias, and which ones are susceptible to bias-enhanced opinion polarization. Our framework gives theoretical ground for the development of heuristics to tackle harmful effects of online bias, such as information bottlenecks, echo chambers, and opinion radicalization.

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“…This is possible for some of the well-known multi-valued or binary attributes. However, in ever-increasing polarized social networks [ 44 , 45 ] with a wide variety of discourses, discussion topics, and trends, the realization of conflicting attributes is not feasible. Our main idea is to find users with an arbitrary and somewhat unique set of attributes in the entire network and label them as suspected for further investigations.…”

Section: The Proposed Approachmentioning

confidence: 99%

Detection of fickle trolls in large-scale online social networks

Shafiei

Dadlani

2022

J Big Data

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Online social networks have attracted billions of active users over the past decade. These systems play an integral role in the everyday life of many people around the world. As such, these platforms are also attractive for misinformation, hoaxes, and fake news campaigns which usually utilize social trolls and/or social bots for propagation. Detection of so-called social trolls in these platforms is challenging due to their large scale and dynamic nature where users’ data are generated and collected at the scale of multi-billion records per hour. In this paper, we focus on fickle trolls, i.e., a special type of trolling activity in which the trolls change their identity frequently to maximize their social relations. This kind of trolling activity may become irritating for the users and also may pose a serious threat to their privacy. To the best of our knowledge, this is the first work that introduces mechanisms to detect these trolls. In particular, we discuss and analyze troll detection mechanisms on different scales. We prove that the order of centralized single-machine detection algorithm is $$O(n^3)$$ O ( n 3 ) which is slow and impractical for early troll detection in large-scale social platforms comprising of billions of users. We also prove that the streaming approach where data is gradually fed to the system is not practical in many real-world scenarios. In light of such shortcomings, we then propose a massively parallel detection approach. Rigorous evaluations confirm that our proposed method is at least six times faster compared to conventional parallel approaches.

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Modelling how social network algorithms can influence opinion polarization

Cited by 43 publications

References 56 publications

The dynamics of online polarization

The dynamics of online polarization

The effect of algorithmic bias and network structure on coexistence, consensus, and polarization of opinions

Detection of fickle trolls in large-scale online social networks

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