Communities on the web rely on open conversation forums for a number of tasks, including governance, information sharing, and decision making. However these forms of collective deliberation can often result in biased outcomes. A prime example are Articles for Deletion (AfD) discussions on Wikipedia, which allow editors to gauge the notability of existing articles, and that, as prior work has suggested, may play a role in perpetuating the notorious gender gap of Wikipedia. Prior attempts to address this question have been hampered by access to narrow observation windows, reliance on limited subsets of both biographies and editorial outcomes, and by potential confounding factors. To address these limitations, here we adopt a competing risk survival framework to fully situate biographical AfD discussions within the full editorial cycle of Wikipedia content. We find that biographies of women are nominated for deletion faster than those of men, despite editors taking longer to reach a consensus for deletion of women, even after controlling for the size of the discussion. Furthermore, we find that AfDs about historical figures show a strong tendency to result into the redirecting or merging of the biography under discussion into other encyclopedic entries, and that there is a striking gender asymmetry: biographies of women are redirected or merged into biographies of men more often than the other way round. Our study provides a more complete picture of the role of AfD in the gender gap of Wikipedia, with implications for the governance of the open knowledge infrastructure of the web.

The volume of information generated online makes it impossible to manually fact-check all claims. Computational approaches for fact checking may be the key to help mitigate the risks of massive misinformation spread. Such approaches can be designed to not only be scalable and effective at assessing veracity of dubious claims, but also to boost a human fact checker's productivity by surfacing relevant facts and patterns to aid their analysis. We present a novel, unsupervised network-flow based approach to determine the truthfulness of a statement of fact expressed in the form of a triple. We view a knowledge graph of background information about real-world entities as a flow network, and show that computational fact checking then amounts to finding a "knowledge stream" connecting the subject and object of the triple. Evaluation on a range of real-world and hand-crafted datasets of facts reveals that this network-flow model can be very effective in discerning true statements from false ones, outperforming existing algorithms on many test cases. Moreover, the model is expressive in its ability to automatically discover several useful patterns and surface relevant facts that may help a human fact checker.

Fashion is a multi-billion dollar industry with social and economic implications worldwide. To gain popularity, brands want to be represented by the top popular models. As new faces are selected using stringent (and often criticized) aesthetic criteria, a priori predictions are made difficult by information cascades and other fundamental trend-setting mechanisms. However, the increasing usage of social media within and without the industry may be affecting this traditional system. We therefore seek to understand the ingredients of success of fashion models in the age of Instagram. Combining data from a comprehensive online fashion database and the popular mobile image-sharing platform, we apply a ma- chine learning framework to predict the tenure of a cohort of new faces for the 2015 Spring/Summer season throughout the subsequent 2015-16 Fall / Winter season. Our framework successfully predicts most of the new popular models who appeared in 2015. In particular, we find that a strong social media presence may be more important than being under contract with a top agency, or than the aesthetic standards sought after by the industry.

Socialization in online communities allows existing members to welcome and recruit newcomers, introduce them to community norms and practices, and sustain their early participation. However, socializing newcomers does not come for free: in large communities, socialization can result in a significant workload for mentors and is hard to scale. In this study we present results from an experiment that measured the effect of a lightweight socialization tool on the activity and retention of newly registered users attempting to edit for the first time Wikipedia. Wikipedia is struggling with the retention of newcomers and our results indicate that a mechanism to elicit lightweight feedback and to provide early mentoring to newcomers improves their chances of becoming long-term contributors.

B¨uchi automata are widely used as a modeling formal- ism in formal verification. The emptiness check procedure is used to carry on the model checking of a model M of a system, against an LTL formula , that expresses the desidered properties the system should satisfy. Algorithms for the emptiness check on B¨uchi automata are able to find a faulty computation, if the language accepted by the au- tomaton of the synchronized product of M and is non empty. Nonetheless, these algorithms don't perform any op- timization on such solution. In this paper we propose an ant colony optimization algorithm for the emptiness check of a subclass of B¨uchi automata, that runs on-the-fly, that is without storing the whole digraph of the automaton in main memory, thus avoiding the problem of exponential growth of the state space. Our approach features a non standard search strategy, with the capability for artificial ants to backtrack during the constructive search, and to modify the graph of the automaton, by removing parts of it when no longer interesting for exploration.