http://www.hpl.hp.com/research/scl/papers/howopinions/wine.pdf How web discourse evolves. To appear in the Proceedings of the Workshop on Internet and Network Economics-2008 opinion formation social networks polarization crowdsourcing WINE No aspect of the massive participation in content creation that the web enables is more evident than in the countless number of opinions, news and product reviews that are constantly posted on the Internet. Given their importance we have analyzed their temporal evo- lution in a number of scenarios. We have found that while ignorance of previous views leads to a uniform sampling of the range of opinions among a community, exposure of previous opinions to potential review- ers induces a trend following process which leads to the expression of increasingly extreme views. Moreover, when the expression of an opinion is costly and previous views are known, a selection bias softens the ex- treme views, as people exhibit a tendency to speak out differently from previous opinions. These findings are not only robust but also suggest simple procedures to extract given types of opinions from the population at large. Fang Wu and Bernardo A. Huberman 2008-09-11 12:00:00 http://www.hpl.hp.com/research/idl/papers/opinion_expression/ How opinions evolve online. We performed a massive study of the dynamics of group delibera- tion among several websites containing millions of opinions on topics ranging from books to media. Contrary to the common phenomenon of group polarization observed offline, we measured a strong tendency towards moderate views in the course of time. This phenomenon possi- bly operates through a self-selection bias whereby previous comments and ratings elicit contrarian views that soften the previous opinions. Fang Wu and Bernardo A. Huberman 2008-05-15 17:00:00 ratings opinion formation polarization reviews Dennis M. Wilkinson 2008-04-10 00:00:00 social networks attention opinion formation Online peer production systems have enabled people to coactively create, share, classify, and rate content on an unprecedented scale. This paper describes strong macroscopic regularities in how people contribute to peer production systems, and shows how these regularities arise from simple dynamical rules. First, it is demonstrated that the probability a person stops contributing varies inversely with the number of contributions he has made. This rule leads to a power law distribution for the number of contributions per person in which a small number of very active users make most of the contributions. The rule also implies that the power law exponent is proportional to the effort required to contribute, as justified by the data. Second, the level of activity per topic is shown to follow a lognormal distribution generated by a stochastic reinfo cement mechanism. A small number of very popular topics thus accumulate the vast majority of contributions. These trends are demonstrated to hold across hundreds of millions of contributions to four disparate peer production systems of differing scope, interface style, and purpose. Simple behavioral rules hold across hundreds of millions of contributions to disparate online peer production efforts. http://www.hpl.hp.com/research/scl/papers/regularities/