Modeling algorithmic bias: simplicial complexes and evolving network topologies

Pansanella, Valentina; Rossetti, Giulio; Milli, Letizia

doi:10.1007/s41109-022-00495-7

Cited by 10 publications

(7 citation statements)

References 47 publications

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“…However, one can imagine that, after repeated negative interactions, a pair of agents will no longer interact with each other, which entails a change in network structure. Researchers have previously modeled such ideas, along with network restructuring to consider new social interactions, using adaptive networks with edge rewiring [28,53]. A possible area of further study is the investigation of which models effectively have "mediator" or "bridging" nodes that assist in bringing together the opinions of agents that are unreceptive to each other or no longer interact.…”

Section: Tablementioning

confidence: 99%

Bounded-Confidence Models of Opinion Dynamics with Adaptive Confidence Bounds

J.¹,

Luo²,

Porter³

2023

Preprint

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People's opinions change with time as they interact with each other. In a boundedconfidence model (BCM) of opinion dynamics, individuals (which are represented by the nodes of a network) have continuous-valued opinions and are influenced only by neighboring nodes whose opinions are within their confidence bound. In this paper, we formulate and analyze discrete-time BCMs with heterogeneous and adaptive confidence bounds. We introduce two new models: (1) a BCM with synchronous opinion updates that generalizes the Hegselmann-Krause (HK) and (2) a BCM with asynchronous opinion updates that generalizes the Deffuant-Weisbuch (DW) model. We analytically and numerically explore our adaptive BCMs' limiting behaviors, including the confidencebound dynamics, the formation of clusters of nodes with similar opinions, and the time evolution of an "effective graph", which is a time-dependent subgraph of a network with edges between nodes that can currently influence each other. For a wide range of parameters that control the increase and decrease of confidence bounds, we demonstrate for a variety of networks that our adaptive BCMs result in fewer major opinion clusters and longer convergence times than the baseline (i.e., nonadaptive) BCMs. We also show that our adaptive BCMs can have pairs of adjacent nodes that converge to the same opinion but are not able to influence each other. This qualitative behavior does not occur in the associated baseline BCMs.

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Section: Tablementioning

confidence: 99%

Bounded-Confidence Models of Opinion Dynamics with Adaptive Confidence Bounds

J.¹,

Luo²,

Porter³

2023

Preprint

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“…Polarization on social media creates and maintains echo chambers, which reduces the amount of diversity in the user experience (Pansanella et al. 2022 ). One important part of the conversation on coordinated action is how organic the action is.…”

Section: Introductionmentioning

confidence: 99%

“…These collective information dissemination activities can segregate the online discourse into multiple, sometimes polarizing, narrative groups . Polarization on social media creates and maintains echo chambers, which reduces the amount of diversity in the user experience (Pansanella et al 2022). One important part of the conversation on coordinated action is how organic the action is.…”

Section: Introductionmentioning

confidence: 99%

A combined synchronization index for evaluating collective action social media

Ng¹,

Carley²

2023

Appl Netw Sci

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Social media has provided a citizen voice, giving rise to grassroots collective action, where users deploy a concerted effort to disseminate online narratives and even carry out offline protests. Sometimes these collective action are aided by inorganic synchronization, which arise from bot actors. It is thus important to identify the synchronicity of emerging discourse on social media and the indications of organic/inorganic activity within the conversations. This provides a way of profiling an event for possibility of offline protests and violence. In this study, we build on past definitions of synchronous activity on social media— simultaneous user action–and develop a Combined Synchronization Index (CSI) which adopts a hierarchical approach in measuring user synchronicity. We apply this index on six political and social activism events on Twitter and analyzed three action types: synchronicity by hashtag, URL and @mentions.The CSI provides an overall quantification of synchronization across all action types within an event, which allows ranking of a spectrum of synchronicity across the six events. Human users have higher synchronous scores than bot users in most events; and bots and humans exhibits the most synchronized activities across all events as compared to other pairs (i.e., bot-bot and human-human). We further rely on the harmony and dissonance of CSI-Network scores with network centrality metrics to observe the presence of organic/inorganic synchronization. We hope this work aids in investigating synchronized action within social media in a collective manner.

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“…events, organisations, people and beliefs. Many automatic ways for performing stance detection have been suggested in the literature and applied to a wide variety of phenomena, like understanding misinformation engagement [49], bias in online recommendation systems [26], and distorted perceptions about COVID-19 vaccines [20] and lockdowns [43]. These automatic approaches mostly focus on the valence or sentiment of words used to address or describe the target event, e.g.…”

Section: Introductionmentioning

confidence: 99%

Introducing mindset streams to investigate stances towards STEM in high school students and experts

Brian¹,

Stella²

2023

Preprint

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We introduce mindset streams for assessing ways of bridging two target concepts in concept maps. We focus on behavioural forma mentis networks (BFMN), which map the associative and affective dimensions of memory recalls. Inspired by trains of thoughts taking several paths to link ideas, mindset streams are defined as BFMN subgraphs induced by all shortest paths between two target concepts, e.g. all recalls in shortest paths bridging "math" and "learning". These streams quantify the following features of the mindset encoded in a BFMN: (i) semantic content (i.e. which ideas mediate connections between targets?), (ii) valence coherence/conflict (i.e. are connections mediated by entwining ideas perceived negatively, positively or neutrally?), and (iii) semantic relevance (i.e. are the bridges between targets peripheral or central for the connectivity/betweenness of the BFMN?). We investigate mindset streams between "maths"/"physics" and key motivational aspects of learning ("fun","work","failure") in two BFMNs, encoding how 159 students and 59 experts perceived and associated concepts about Science Technology Engineering and Maths (STEM), respectively. Statistical comparisons against configuration models show that high schoolers bridge "maths" and "fun" only through overabundant levels of valence-conflicting associations, contrasting negatively perceived domain knowledge with peer-related positive experiences. This conflict is absent in the researchers' mindset stream, which rather bridges "math" and "fun" through positive, science-related associations. The mindset streams of both groups bridge "maths" and "physics" to "work" through mostly positive career-related jargon. Students' mindset streams of "failure" and "math"/"physics" are dominated by negative associations with test anxiety, whereas researchers integrate "failure" and "math"/"physics" in semantically richer and more positive contexts, denoting failure itself as a cornerstone of STEM learning. We discuss our findings and future research directions in view of relevant psychology/education literature.

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Modeling algorithmic bias: simplicial complexes and evolving network topologies

Cited by 10 publications

References 47 publications

Bounded-Confidence Models of Opinion Dynamics with Adaptive Confidence Bounds

Bounded-Confidence Models of Opinion Dynamics with Adaptive Confidence Bounds

A combined synchronization index for evaluating collective action social media

Introducing mindset streams to investigate stances towards STEM in high school students and experts

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