Influence and Information Flow in Online Social Networks

2021 ◽  
pp. 502-520
Author(s):  
Afrand Agah ◽  
Mehran Asadi
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Online Social Networks ◽  
Large Data ◽  
Marketing Strategies ◽  
Divide And Conquer ◽  
Data Sets ◽  
Divide And Conquer Algorithm ◽  
Flow Of Information

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

Influence and Information Flow in Online Social Networks

2017 ◽  
Vol 9 (4) ◽  
pp. 1-17
Author(s):  
Afrand Agah ◽  
Mehran Asadi
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Data ◽  
Marketing Strategies ◽  
Large Data Sets ◽  
Divide And Conquer ◽  
Data Sets ◽  
Divide And Conquer Algorithm ◽  
Flow Of Information

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

scholarly journals A meritocratic network formation model for the rise of social media influencers

2021 ◽  
Author(s):  
Nicolò Pagan ◽  
Wenjun Mei ◽  
Cheng Li ◽  
Florian Dörfler
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Network Formation ◽  
Large Data ◽  
Network Size ◽  
Large Data Sets ◽  
Data Sets ◽  
Strategic Incentives ◽  
Quality Ranking ◽  
Social Network Formation

Abstract Many of today’s most used online social networks such as Instagram, Youtube, Twitter, or Twitch are based on User-Generated Content (UGC), and the exploration of this content is enhanced by the integrated search engines. Prior multidisciplinary effort on studying social network formation processes has privileged topological elements or socio-strategic incentives. Here, we propose an untouched meritocratic approach inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We statistically and numerically analyze the network equilibria properties: while the expected outdegree of the nodes remains bounded by the logarithm of the network size, the expected indegree follows a Zipf’s law with respect to the quality ranking. Notably, our quality-based mechanism provides an intuitive explanation of the origin of the Zipf’s regularity in growing social networks. Our theoretical results are empirically validated against large data-sets collected from Twitch, a fast-growing platform for online gamers.

Information Diffusion in Social Networks

2011 ◽  
pp. 146-163 ◽  
Author(s):  
Dmitry Zinoviev
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Diffusion Processes ◽  
Small World ◽  
The Social ◽  
Strength Of Weak Ties ◽  
Strategic Position

The issue of information diffusion in small-world social networks was first systematically brought to light by Mark Granovetter in his seminal paper “The Strength of Weak Ties” in 1973 and has been an area of active academic studies in the past three decades. This chapter discusses information proliferation mechanisms in massive online social networks (MOSN). In particular, the following aspects of information diffusion processes are addressed: the role and the strategic position of influential spreaders of information; the pathways in the social networks that serve as conduits for communication and information flow; mathematical models describing proliferation processes; short-term and long-term dynamics of information diffusion, and secrecy of information diffusion.

Social Network Citizenship

2012 ◽  
pp. 339-357 ◽  
Author(s):  
Mina Seraj ◽  
Aysegul Toker
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Conceptual Model ◽  
Online Social Networks ◽  
Marketing Strategies ◽  
Social Media Marketing ◽  
Marketing Practice

This chapter describes and discusses the specificities of membership commitment to online social networks. While delineating these specificities, we introduce the concept of social network citizenship (SNC) to define the characteristics of committed network members. A conceptual model involving commencement, creation, change, and commitment is developed in order to establish the antecedents of this new concept. In addition, the implications for marketing practice are discussed to reveal how companies can acquire social network citizens to retain their social media marketing strategies successful.

scholarly journals Awesome women and bad feminists: the role of online social networks and peer support for feminist practice in academia

2019 ◽  
Vol 27 (3) ◽  
pp. 415-435
Author(s):  
Hannah Bayfield ◽  
Laura Colebrooke ◽  
Hannah Pitt ◽  
Rhiannon Pugh ◽  
Natalia Stutter
Keyword(s):  
Higher Education ◽  
Social Networks ◽  
Peer Support ◽  
Online Social Networks ◽  
Online Collaboration ◽  
Online Networks ◽  
Feminist Practice ◽  
Digital Spaces

In her book, ‘Bad Feminist’, Roxane Gay claims this label shamelessly, embracing the contradictory aspects of enacting feminist practice while fundamentally being ‘flawed human[s]’. This article tells a story inspired by and enacting Roxane Gay’s approach in academia, written by five cis-gendered women geographers. It is the story of a proactive, everyday feminist initiative to survive as women in an academic precariat fuelled by globalised, neoliberalised higher education. We reflect on what it means to be (bad) feminists in that context, and how we respond as academics. We share experiences of an online space used to support one another through post-doctoral life, a simple message thread, which has established an important role in our development as academics and feminists. This article, written through online collaboration, mirrors and enacts processes fundamental to our online network, demonstrating the significance and potential of safe digital spaces for peer support. Excerpts from the chat reflect critically on struggles and solutions we have co-developed. Through this, we celebrate and validate a strategy we know that we and others like us find invaluable for our wellbeing and survival. Finally, we reflect on the inherent limitations of exclusive online networks as tools for feminist resistance.

The Emergence of Agency in Online Social Networks

2010 ◽  
pp. 102-118 ◽  
Author(s):  
Jillianne R. Code ◽  
Nicholas E. Zaparyniuk
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Virtual Communities ◽  
Emergent Property ◽  
Psychological Processes ◽  
Social Situations ◽  
Group Interactions ◽  
Online Interactions ◽  
Practical Implications

Social and group interactions in online and virtual communities develop and evolve from expressions of human agency. The exploration of the emergence of agency in social situations is of critical importance to understanding the psychology of agency and group interactions in social networks. This chapter explores how agency emerges from social interactions, how this emergence influences the development of social networks, and the role of social software’s potential as a powerful tool for educational purposes. Practical implications of agency as an emergent property within social networks provide a psychological framework that forms the basis for pedagogy of social interactivity. This chapter identifies and discusses the psychological processes necessary for the development of agency and to further understanding of individual’s engagement in online interactions for socialization and learning.

Learning to Correlate Accounts Across Online Social Networks: An Embedding-Based Approach

2020 ◽  
Vol 32 (3) ◽  
pp. 714-729
Author(s):  
Fan Zhou ◽  
Kunpeng Zhang ◽  
Shuying Xie ◽  
Xucheng Luo
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Dimensional Space ◽  
Synthetic Data ◽  
Language Models ◽  
Data Sets ◽  
User Behaviors ◽  
Latent Representations ◽  
Multiple Accounts ◽  
Cross Site

Cross-site account correlation correlates users who have multiple accounts but the same identity across online social networks (OSNs). Being able to identify cross-site users is important for a variety of applications in social networks, security, and electronic commerce, such as social link prediction and cross-domain recommendation. Because of either heterogeneous characteristics of platforms or some unobserved but intrinsic individual factors, the same individuals are likely to behave differently across OSNs, which accordingly causes many challenges for correlating accounts. Traditionally, account correlation is measured by analyzing user-generated content, such as writing style, rules of naming user accounts, or some existing metadata (e.g., account profile, account historical activities). Accounts can be correlated by de-anonymizing user behaviors, which is sometimes infeasible since such data are not often available. In this work, we propose a method, called ACCount eMbedding (ACCM), to go beyond text data and leverage semantics of network structures, a possibility that has not been well explored so far. ACCM aims to correlate accounts with high accuracy by exploiting the semantic information among accounts through random walks. It models and understands latent representations of accounts using an embedding framework similar to sequences of words in natural language models. It also learns a transformation matrix to project node representations into a common dimensional space for comparison. With evaluations on both real-world and synthetic data sets, we empirically demonstrate that ACCM provides performance improvement compared with several state-of-the-art baselines in correlating user accounts between OSNs.

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