Special Issue "Computational Social Sciences: Contagion, Collective Behaviors, and Networks"

A special issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: closed (15 February 2016)

Special Issue Editor

Guest Editor
Dr. Emilio Ferrara

Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, USA
Website | E-Mail
Interests: network science; machine learning; data science; data mining; computational social science

Special Issue Information

Dear Colleagues,

The role of socio-technical systems in shaping social collectives is acquiring an increasing importance in our now interconnected world. However, our understanding of the complex dynamics governing the interplay between socio-technical systems and our society is still shallow. The intrinsic complex nature of the social dynamics occurring in online and offline social networks still challenges our efforts, both in terms of modeling and analysis of real-world data.

Recent work demonstrated the possibility of answering social questions at unprecedented scales, by leveraging data from socio-technical platforms, such as Facebook, Twitter, Wikipedia, and weblogs. This new wave of research, under the umbrella of Computational Social Sciences, aims at modeling, and at times predicting, offline and online events. Popular applications include elections forecasting, opinion dynamics, emotional contagion, and predicting movie revenues or stock market oscillations. Similar data provided insights into the mechanisms driving the formation of groups of interests, topical communities, and the evolution of social networks. They also have been used to study polarization phenomena in political discussion, diffusion of information, and the dynamics of collective attention.

The aim of this Special Issue is to address the question of ICT-mediated social phenomena emerging over multiple scales, ranging from the interactions of individuals to the emergence of self-organized global movements. We would like to gather researchers from different disciplines and methodological backgrounds to discuss new ideas, research questions, recent results, and future challenges in this emerging area of research and public interest.

Particular attention will be devoted to the following topics:

- Viral spreading in online social networks, public attention and popularity
- Crowd-sourcing and wisdom of crowds
- Temporally evolving networks
- Group formation, evolution, and group behavior analysis
- Modeling, tracking, and forecasting dynamic groups in social media
- Community detection and dynamic community structure analysis
- Social simulation, cultural, opinion, and normative dynamics
- Models of social capital, collective action, and social movements
- Coevolution of network and behavior

Dr. Emilio Ferrara
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 550 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

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Open AccessArticle Conflict and Computation on Wikipedia: A Finite-State Machine Analysis of Editor Interactions
Future Internet 2016, 8(3), 31; doi:10.3390/fi8030031
Received: 16 February 2016 / Revised: 14 June 2016 / Accepted: 29 June 2016 / Published: 8 July 2016
Cited by 2 | PDF Full-text (504 KB) | HTML Full-text | XML Full-text
Abstract
What is the boundary between a vigorous argument and a breakdown of relations? What drives a group of individuals across it? Taking Wikipedia as a test case, we use a hidden Markov model to approximate the computational structure and social grammar of more
[...] Read more.
What is the boundary between a vigorous argument and a breakdown of relations? What drives a group of individuals across it? Taking Wikipedia as a test case, we use a hidden Markov model to approximate the computational structure and social grammar of more than a decade of cooperation and conflict among its editors. Across a wide range of pages, we discover a bursty war/peace structure where the systems can become trapped, sometimes for months, in a computational subspace associated with significantly higher levels of conflict-tracking “revert” actions. Distinct patterns of behavior characterize the lower-conflict subspace, including tit-for-tat reversion. While a fraction of the transitions between these subspaces are associated with top-down actions taken by administrators, the effects are weak. Surprisingly, we find no statistical signal that transitions are associated with the appearance of particularly anti-social users, and only weak association with significant news events outside the system. These findings are consistent with transitions being driven by decentralized processes with no clear locus of control. Models of belief revision in the presence of a common resource for information-sharing predict the existence of two distinct phases: a disordered high-conflict phase, and a frozen phase with spontaneously-broken symmetry. The bistability we observe empirically may be a consequence of editor turn-over, which drives the system to a critical point between them. Full article
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Open AccessArticle The Evolution of Wikipedia’s Norm Network
Future Internet 2016, 8(2), 14; doi:10.3390/fi8020014
Received: 4 December 2015 / Revised: 25 March 2016 / Accepted: 6 April 2016 / Published: 20 April 2016
Cited by 4 | PDF Full-text (3066 KB) | HTML Full-text | XML Full-text
Abstract
Social norms have traditionally been difficult to quantify. In any particular society, their sheer number and complex interdependencies often limit a system-level analysis. One exception is that of the network of norms that sustain the online Wikipedia community. We study the fifteen-year evolution
[...] Read more.
Social norms have traditionally been difficult to quantify. In any particular society, their sheer number and complex interdependencies often limit a system-level analysis. One exception is that of the network of norms that sustain the online Wikipedia community. We study the fifteen-year evolution of this network using the interconnected set of pages that establish, describe, and interpret the community’s norms. Despite Wikipedia’s reputation for ad hoc governance, we find that its normative evolution is highly conservative. The earliest users create norms that both dominate the network and persist over time. These core norms govern both content and interpersonal interactions using abstract principles such as neutrality, verifiability, and assume good faith. As the network grows, norm neighborhoods decouple topologically from each other, while increasing in semantic coherence. Taken together, these results suggest that the evolution of Wikipedia’s norm network is akin to bureaucratic systems that predate the information age. Full article
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Open AccessArticle A Method for Assessing the Performance of e-Government Twitter Accounts
Future Internet 2016, 8(2), 12; doi:10.3390/fi8020012
Received: 12 January 2016 / Revised: 22 March 2016 / Accepted: 11 April 2016 / Published: 18 April 2016
PDF Full-text (709 KB) | HTML Full-text | XML Full-text
Abstract
This paper introduces a method for assessing the influence of Twitter accounts of central e-government agencies. It first stresses the importance of activity and popularity of the e-government accounts, and also the importance of community formation among followers-citizens, as the two main stages
[...] Read more.
This paper introduces a method for assessing the influence of Twitter accounts of central e-government agencies. It first stresses the importance of activity and popularity of the e-government accounts, and also the importance of community formation among followers-citizens, as the two main stages of e-government adoption. The proposed approach combines activity and popularity of the accounts and followers’ community characteristics in a ranking system, using an idea originally introduced to measure blogosphere authority. A Twitter Authority Index is produced. The method is demonstrated through an extended example: 56 Twitter accounts of ministries of EU countries are sorted according to their indexes in the proposed ranking system. Detailed values for the ministries’ accounts and average values for the countries that the ministries belong to are reported and commented. Full article
Open AccessArticle Optimal Referral Reward Considering Customer’s Budget Constraint
Future Internet 2015, 7(4), 516-529; doi:10.3390/fi7040516
Received: 10 September 2015 / Revised: 10 December 2015 / Accepted: 11 December 2015 / Published: 21 December 2015
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Abstract
Everyone likes Porsche but few can afford it. Budget constraints always play a critical role in a customer’s decision-making. The literature disproportionally focuses on how firms can induce customer valuations toward the product, but does not address how to assess the influence of
[...] Read more.
Everyone likes Porsche but few can afford it. Budget constraints always play a critical role in a customer’s decision-making. The literature disproportionally focuses on how firms can induce customer valuations toward the product, but does not address how to assess the influence of budget constraints. We study these questions in the context of a referral reward program (RRP). RRP is a prominent marketing strategy that utilizes recommendations passed from existing customers to their friends and effectively stimulates word of mouth (WoM). We build a stylized game-theoretical model with a nested Stackelberg game involving three players: a firm, an existing customer, and a potential customer who is a friend of the existing customer. The budget is the friend’s private information. We show that RRPs might be optimal when the friend has either a low or a high valuation, but they work differently in each situation because of the budget. Furthermore, there are two budget thresholds, a fixed one and a variable one, which limit a firm’s ability to use rewards. Full article
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Review

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Open AccessFeature PaperReview Information Is Not a Virus, and Other Consequences of Human Cognitive Limits
Future Internet 2016, 8(2), 21; doi:10.3390/fi8020021
Received: 4 February 2016 / Revised: 25 April 2016 / Accepted: 27 April 2016 / Published: 13 May 2016
Cited by 6 | PDF Full-text (370 KB) | HTML Full-text | XML Full-text
Abstract
The many decisions that people make about what to pay attention to online shape the spread of information in online social networks. Due to the constraints of available time and cognitive resources, the ease of discovery strongly impacts how people allocate their attention
[...] Read more.
The many decisions that people make about what to pay attention to online shape the spread of information in online social networks. Due to the constraints of available time and cognitive resources, the ease of discovery strongly impacts how people allocate their attention to social media content. As a consequence, the position of information in an individual’s social feed, as well as explicit social signals about its popularity, determine whether it will be seen, and the likelihood that it will be shared with followers. Accounting for these cognitive limits simplifies mechanics of information diffusion in online social networks and explains puzzling empirical observations: (i) information generally fails to spread in social media and (ii) highly connected people are less likely to re-share information. Studies of information diffusion on different social media platforms reviewed here suggest that the interplay between human cognitive limits and network structure differentiates the spread of information from other social contagions, such as the spread of a virus through a population. Full article
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