Conflict and Computation on Wikipedia: a Finite-State Machine Analysis of Editor Interactions

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.Comment: 23 pages, 3 figures. Matches published version. Code for HMM fitting available at http://bit.ly/sfihmm ; time series and derived finite state machines at bit.ly/wiki_hm

Dynamics on expanding spaces: modeling the emergence of novelties

Novelties are part of our daily lives. We constantly adopt new technologies, conceive new ideas, meet new people, experiment with new situations. Occasionally, we as individuals, in a complicated cognitive and sometimes fortuitous process, come up with something that is not only new to us, but to our entire society so that what is a personal novelty can turn into an innovation at a global level. Innovations occur throughout social, biological and technological systems and, though we perceive them as a very natural ingredient of our human experience, little is known about the processes determining their emergence. Still the statistical occurrence of innovations shows striking regularities that represent a starting point to get a deeper insight in the whole phenomenology. This paper represents a small step in that direction, focusing on reviewing the scientific attempts to effectively model the emergence of the new and its regularities, with an emphasis on more recent contributions: from the plain Simon's model tracing back to the 1950s, to the newest model of Polya's urn with triggering of one novelty by another. What seems to be key in the successful modelling schemes proposed so far is the idea of looking at evolution as a path in a complex space, physical, conceptual, biological, technological, whose structure and topology get continuously reshaped and expanded by the occurrence of the new. Mathematically it is very interesting to look at the consequences of the interplay between the "actual" and the "possible" and this is the aim of this short review.Comment: 25 pages, 10 figure

Does Astronomy research become too dated for the public? Wikipedia citations to Astronomy and Astrophysics journal articles 1996-2014

Astronomy is a natural science attracting substantial public interest. On a human scale, most individual celestial objects are essentially unchanging but is the same true for interest in astronomy research? This article uses the popular online encyclopedia Wikipedia as a proxy for public interest in academic research and assesses the extent to which it cites astronomy and astrophysics articles published between 1996 and 2014. Automatic Bing searches in Webometric Analyst were used to count the number of citations to astronomy and astrophysics articles from Wikipedia. The results show that older papers from before 2008 are increasingly less likely to be cited. This is true overall and in most of the major language versions of Wikipedia, although it may reflect editors’ interests rather than the public’s interests. This is consistent with a moderate tendency towards obsolescence in public interest in research, although it is probably affected by the dates on which most Wikipedia content on the topic was created. Papers may become obsolete if they report evidence that are later superseded by improved data or if they propose a model that is later replaced

Hume\u27s Penguin, or, Yochai Benkler and the Nature of Peer Production

This Article examines \u27peer production, a term coined and a concept explicated by Yochai Benkler. My own interest in peer production stems from its importance as a new form of user-generated content. User-generated content is particularly interesting if Benkler is right in his claim that the positive analysis of peer-produced content may have normative implications with respect to copyright law--in particular, the implication that copyright law may play a deleterious role in the formation and maintenance of this potentially significant new form of user-generated content. We are in need of a theory of collective action for the social world that is emerging in cyberspace. Benkler\u27s theory of peer production makes an important contribution to this project. The present Article seeks to expand on Benkler\u27s account by demonstrating that collective-action problems are not synonymous with the tragedy of the commons. In particular, one important type of solution to a collective-action problem of a sort not countenanced by Benkler is the convention or coordination norm. This Article will show that not only would a more comprehensive theory of collective action in cyberspace need to fit conventions into its account but also that even Benkler\u27s examples of peer production must take account of conventions as well

Leveraging Sociological Models for Predictive Analytics

Abstract—There is considerable interest in developing techniques for predicting human behavior, for instance to enable emerging contentious situations to be forecast or the nature of ongoing but “hidden ” activities to be inferred. A promising approach to this problem is to identify and collect appropriate empirical data and then apply machine learning methods to these data to generate the predictions. This paper shows the performance of such learning algorithms often can be improved substantially by leveraging sociological models in their development and implementation. In particular, we demonstrate that sociologically-grounded learning algorithms outperform gold-standard methods in three important and challenging tasks: 1.) inferring the (unobserved) nature of relationships in adversarial social networks, 2.) predicting whether nascent social diffusion events will “go viral”, and 3.) anticipating and defending future actions of opponents in adversarial settings. Significantly, the new algorithms perform well even when there is limited data available for their training and execution. Keywords—predictive analysis, sociological models, social networks, empirical analysis, machine learning. I