1,395 research outputs found
Modeling Evolutionary Dynamics of Lurking in Social Networks
Lurking is a complex user-behavioral phenomenon that occurs in all
large-scale online communities and social networks. It generally refers to the
behavior characterizing users that benefit from the information produced by
others in the community without actively contributing back to the production of
social content. The amount and evolution of lurkers may strongly affect an
online social environment, therefore understanding the lurking dynamics and
identifying strategies to curb this trend are relevant problems. In this
regard, we introduce the Lurker Game, i.e., a model for analyzing the
transitions from a lurking to a non-lurking (i.e., active) user role, and vice
versa, in terms of evolutionary game theory. We evaluate the proposed Lurker
Game by arranging agents on complex networks and analyzing the system
evolution, seeking relations between the network topology and the final
equilibrium of the game. Results suggest that the Lurker Game is suitable to
model the lurking dynamics, showing how the adoption of rewarding mechanisms
combined with the modeling of hypothetical heterogeneity of users' interests
may lead users in an online community towards a cooperative behavior.Comment: 13 pages, 5 figures. Accepted at CompleNet 201
How to Find Opinion Leader on the Online Social Network?
Online social networks (OSNs) provide a platform for individuals to share
information, exchange ideas and build social connections beyond in-person
interactions. For a specific topic or community, opinion leaders are
individuals who have a significant influence on others' opinions. Detecting and
modeling opinion leaders is crucial as they play a vital role in shaping public
opinion and driving online conversations. Existing research have extensively
explored various methods for detecting opinion leaders, but there is a lack of
consensus between definitions and methods. It is important to note that the
term "important node" in graph theory does not necessarily align with the
concept of "opinion leader" in social psychology. This paper aims to address
this issue by introducing the methodologies for identifying influential nodes
in OSNs and providing a corresponding definition of opinion leaders in relation
to social psychology. The key novelty is to review connections and
cross-compare different approaches that have origins in: graph theory, natural
language processing, social psychology, control theory, and graph sampling. We
discuss how they tell a different technical tale of influence and also propose
how some of the approaches can be combined via networked dynamical systems
modeling. A case study is performed on Twitter data to compare the performance
of different methodologies discussed. The primary objective of this work is to
elucidate the progression of opinion leader detection on OSNs and inspire
further research in understanding the dynamics of opinion evolution within the
field
Influence Maximization in Social Networks: A Survey
Online social networks have become an important platform for people to
communicate, share knowledge and disseminate information. Given the widespread
usage of social media, individuals' ideas, preferences and behavior are often
influenced by their peers or friends in the social networks that they
participate in. Since the last decade, influence maximization (IM) problem has
been extensively adopted to model the diffusion of innovations and ideas. The
purpose of IM is to select a set of k seed nodes who can influence the most
individuals in the network.
In this survey, we present a systematical study over the researches and
future directions with respect to IM problem. We review the information
diffusion models and analyze a variety of algorithms for the classic IM
algorithms. We propose a taxonomy for potential readers to understand the key
techniques and challenges. We also organize the milestone works in time order
such that the readers of this survey can experience the research roadmap in
this field. Moreover, we also categorize other application-oriented IM studies
and correspondingly study each of them. What's more, we list a series of open
questions as the future directions for IM-related researches, where a potential
reader of this survey can easily observe what should be done next in this
field
Tutorial: Are You My Neighbor?: Bringing Order to Neighbor Computing Problems
Finding nearest neighbors is an important topic that has attracted much attention over the years and has applications in many fields, such as market basket analysis, plagiarism and anomaly detection, community detection, ligand-based virtual screening, etc. As data are easier and easier to collect, finding neighbors has become a potential bottleneck in analysis pipelines. Performing pairwise comparisons given the massive datasets of today is no longer feasible. The high computational complexity of the task has led researchers to develop approximate methods, which find many but not all of the nearest neighbors. Yet, for some types of data, efficient exact solutions have been found by carefully partitioning or filtering the search space in a way that avoids most unnecessary comparisons.In recent years, there have been several fundamental advances in our ability to efficiently identify appropriate neighbors, especially in non-traditional data, such as graphs or document collections. In this tutorial, we provide an in-depth overview of recent methods for finding (nearest) neighbors, focusing on the intuition behind choices made in the design of those algorithms and on the utility of the methods in real-world applications. Our tutorial aims to provide a unifying view of neighbor computing problems, spanning from numerical data to graph data, from categorical data to sequential data, and related application scenarios. For each type of data, we will review the current state-of-the-art approaches used to identify neighbors and discuss how neighbor search methods are used to solve important problems
Modeling dynamic community acceptance of mining using agent-based modeling
This research attempts to provide fundamental understanding into the relationship between perceived sustainability of mineral projects and community acceptance. The main objective is to apply agent-based modeling (ABM) and discrete choice modeling to understand changes in community acceptance over time due to changes in community demographics and perceptions. This objective focuses on: 1) formulating agent utility functions for ABM, based on discrete choice theory; 2) applying ABM to account for the effect of information diffusion on community acceptance; and 3) explaining the relationship between initial conditions, topology, and rate of interactions, on one hand, and community acceptance on the other hand.
To achieve this objective, the research relies on discrete choice theory, agent-based modeling, innovation and diffusion theory, and stochastic processes. Discrete choice models of individual preferences of mining projects were used to formulate utility functions for this research. To account for the effect of information diffusion on community acceptance, an agent-based model was developed to describe changes in community acceptance over time, as a function of changing demographics and perceived sustainability impacts. The model was validated with discrete choice experimental data on acceptance of mining in Salt Lake City, Utah. The validated model was used in simulation experiments to explain the model\u27s sensitivity to initial conditions, topology, and rate of interactions. The research shows that the model, with the base case social network, is more sensitive to homophily and number of early adopters than average degree (number of friends). Also, the dynamics of information diffusion are sensitive to differences in clustering in the social networks. Though the research examined the effect of three networks that differ due to the type of homophily, it is their differences in clustering due to homophily that was correlated to information diffusion dynamics --Abstract, page iii
A unifying framework for fairness-aware influence maximization
The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studied over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms
Carthago Delenda Est: Co-opetitive Indirect Information Diffusion Model for Influence Operations on Online Social Media
For a state or non-state actor whose credibility is bankrupt, relying on bots
to conduct non-attributable, non-accountable, and
seemingly-grassroots-but-decentralized-in-actuality influence/information
operations (info ops) on social media can help circumvent the issue of trust
deficit while advancing its interests. Planning and/or defending against
decentralized info ops can be aided by computational simulations in lieu of
ethically-fraught live experiments on social media. In this study, we introduce
Diluvsion, an agent-based model for contested information propagation efforts
on Twitter-like social media. The model emphasizes a user's belief in an
opinion (stance) being impacted by the perception of potentially illusory
popular support from constant incoming floods of indirect information, floods
that can be cooperatively engineered in an uncoordinated manner by bots as they
compete to spread their stances. Our model, which has been validated against
real-world data, is an advancement over previous models because we account for
engagement metrics in influencing stance adoption, non-social tie spreading of
information, neutrality as a stance that can be spread, and themes that are
analogous to media's framing effect and are symbiotic with respect to stance
propagation. The strengths of the Diluvsion model are demonstrated in
simulations of orthodox info ops, e.g., maximizing adoption of one stance;
creating echo chambers; inducing polarization; and unorthodox info ops, e.g.,
simultaneous support of multiple stances as a Trojan horse tactic for the
dissemination of a theme.Comment: 60 pages, 9 figures, 1 tabl
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