14 research outputs found
Selective Exposure: Implications for Information Elaboration in Asynchronous Online Discussions
Selective exposure is an inhibitor to teaching and learning in an IT-enabled learning environment because in electronic environments, students have greater freedom over what they choose to access and read. This study will use in-class field experiments in order to examine the impact of information presentation and familiarity of the source on the quality of information elaboration through the mediating factor of selective exposure. Selective exposure is an individual’s tendency to seek confirmatory (as opposed to non-confirmatory) information related to a choice that has been made by the individual. Information elaboration requires attending to the decision-related information, processing that information, and analyzing the information to present a coherent argument related thereto. The integrative quality of information elaboration depends on the extent to which non-confirmatory and confirmatory opinions are attended to, processed, and combined to lead to a decision. This research will contribute to the literature on IT-enabled teaching and learning
Minimizing Polarization and Disagreement in Social Networks
The rise of social media and online social networks has been a disruptive
force in society. Opinions are increasingly shaped by interactions on online
social media, and social phenomena including disagreement and polarization are
now tightly woven into everyday life. In this work we initiate the study of the
following question: given agents, each with its own initial opinion that
reflects its core value on a topic, and an opinion dynamics model, what is the
structure of a social network that minimizes {\em polarization} and {\em
disagreement} simultaneously?
This question is central to recommender systems: should a recommender system
prefer a link suggestion between two online users with similar mindsets in
order to keep disagreement low, or between two users with different opinions in
order to expose each to the other's viewpoint of the world, and decrease
overall levels of polarization? Our contributions include a mathematical
formalization of this question as an optimization problem and an exact,
time-efficient algorithm. We also prove that there always exists a network with
edges that is a approximation to the optimum.
For a fixed graph, we additionally show how to optimize our objective function
over the agents' innate opinions in polynomial time.
We perform an empirical study of our proposed methods on synthetic and
real-world data that verify their value as mining tools to better understand
the trade-off between of disagreement and polarization. We find that there is a
lot of space to reduce both polarization and disagreement in real-world
networks; for instance, on a Reddit network where users exchange comments on
politics, our methods achieve a -fold reduction in polarization
and disagreement.Comment: 19 pages (accepted, WWW 2018
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We are the Change that we Seek: Information Interactions During a Change of Viewpoint
There has been considerable hype about filter bubbles and echo chambers influencing the views of information consumers. The fear is that these technologies are undermining democracy by swaying opinion and creating an uninformed, polarised populace. The literature in this space is mostly techno-centric, addressing the impact of technology. In contrast, our work is the first research in the information interaction field to examine changing viewpoints from a human-centric perspective. It provides a new understanding of view change and how we might support informed, autonomous view change behaviour. We interviewed 18 participants about a self-identified change of view, and the information touchpoints they engaged with along the way. In this paper we present the information types and sources that informed changes of viewpoint, and the ways in which our participants interacted with that information. We describe our findings in the context of the techno-centric literature and suggest principles for designing digital information environments that support user autonomy and reflection in viewpoint formation
Maximizing the Diversity of Exposure in a Social Network
Social-media platforms have created new ways for citizens to stay informed
and participate in public debates. However, to enable a healthy environment for
information sharing, social deliberation, and opinion formation, citizens need
to be exposed to sufficiently diverse viewpoints that challenge their
assumptions, instead of being trapped inside filter bubbles. In this paper, we
take a step in this direction and propose a novel approach to maximize the
diversity of exposure in a social network. We formulate the problem in the
context of information propagation, as a task of recommending a small number of
news articles to selected users. We propose a realistic setting where we take
into account content and user leanings, and the probability of further sharing
an article. This setting allows us to capture the balance between maximizing
the spread of information and ensuring the exposure of users to diverse
viewpoints.
The resulting problem can be cast as maximizing a monotone and submodular
function subject to a matroid constraint on the allocation of articles to
users. It is a challenging generalization of the influence maximization
problem. Yet, we are able to devise scalable approximation algorithms by
introducing a novel extension to the notion of random reverse-reachable sets.
We experimentally demonstrate the efficiency and scalability of our algorithm
on several real-world datasets
Discovering Polarized Communities in Signed Networks
Signed networks contain edge annotations to indicate whether each interaction
is friendly (positive edge) or antagonistic (negative edge). The model is
simple but powerful and it can capture novel and interesting structural
properties of real-world phenomena. The analysis of signed networks has many
applications from modeling discussions in social media, to mining user reviews,
and to recommending products in e-commerce sites. In this paper we consider the
problem of discovering polarized communities in signed networks. In particular,
we search for two communities (subsets of the network vertices) where within
communities there are mostly positive edges while across communities there are
mostly negative edges. We formulate this novel problem as a "discrete
eigenvector" problem, which we show to be NP-hard. We then develop two
intuitive spectral algorithms: one deterministic, and one randomized with
quality guarantee (where is the number of vertices in the
graph), tight up to constant factors. We validate our algorithms against
non-trivial baselines on real-world signed networks. Our experiments confirm
that our algorithms produce higher quality solutions, are much faster and can
scale to much larger networks than the baselines, and are able to detect
ground-truth polarized communities
Polarización en redes sociales ayuda a que los influencers tengan más influencia: análisis y dos estrategias de inoculación
Este trabajo explora simulaciones de debates polarizados desde una premisa general y teórica. EspecÃficamente, trata sobre la existencia de una vÃa verosÃmil para un subgrupo en una red social en lÃnea para encontrar un desacuerdo beneficioso y cuál podrÃa ser ese beneficio. Se propone un marco metodológico que representa los factores clave que impulsan la participación en las redes sociales, incluida la acumulación iterativa de influencia y la dinámica para el tratamiento asimétrico de mensajes durante un desacuerdo. Se muestra que, antes de un evento de polarización, se logra una tendencia hacia una distribución más uniforme de relativa influencia, lo que entonces se invierte por el evento de polarización. Se debaten las razones de esta reversión y cómo tiene un análogo verosÃmil en los sistemas del mundo real. Además, se propone un par de estrategias de inoculación, cuyo objetivo es devolver la tendencia hacia una influencia uniforme entre los usuarios, mientras que se abstiene de violar la privacidad del usuario (por mantener el tema agnóstico) y de las operaciones de eliminación de usuarios.
 
RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions
The topology of the hyperlink graph among pages expressing different opinions
may influence the exposure of readers to diverse content. Structural bias may
trap a reader in a polarized bubble with no access to other opinions. We model
readers' behavior as random walks. A node is in a polarized bubble if the
expected length of a random walk from it to a page of different opinion is
large. The structural bias of a graph is the sum of the radii of
highly-polarized bubbles. We study the problem of decreasing the structural
bias through edge insertions. Healing all nodes with high polarized bubble
radius is hard to approximate within a logarithmic factor, so we focus on
finding the best edges to insert to maximally reduce the structural bias.
We present RePBubLik, an algorithm that leverages a variant of the random walk
closeness centrality to select the edges to insert. RePBubLik obtains, under
mild conditions, a constant-factor approximation. It reduces the structural
bias faster than existing edge-recommendation methods, including some designed
to reduce the polarization of a graph