Electronics and Computer Science, WAIS

Community capacity is used to monitor socioeconomic development. It is composed of a number of dimensions that can be measured to understand issues possibly arising in the implementation of a policy or of a project targeting a community. Measuring these dimensions is thus highly valuable for policymakers and local administrator, though expensive and time consuming. To address this issue, we evaluated their estimation through a machine learning technique—Random Forests—applied to secondary open government data and determined the most important variables for prediction. We focused on two dimensions: sense of community and participation. The variables included in the data sets used to train the predictive models complied with two criteria: nationwide availability and sufficiently fine-grained geographic breakdown, that is, neighborhood level. Our resultant models are more accurate than others based on traditional statistics found in the literature, showing the feasibility of the approach. The most determinant variables in our models were only partially in agreement with the most influential factors for sense of community and participation according to the social science literature consulted, providing a starting point for future investigation under a social science perspective. Moreover, due to the lack of geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighborhood level.

Wikidata is a collaboratively-edited knowledge graph; it expresses knowledge in the form of subject-property-value triples, which can be enhanced with references to add provenance information. Understanding the quality of Wikidata is key to its widespread adoption as a knowledge resource. We analyse one aspect of Wikidata quality, provenance, in terms of relevance and authoritativeness of its external references. We follow a two-staged approach. First, we perform a crowd-sourced evaluation of references. Second, we use the judgements collected in the first stage to train a machine learning model to predict reference quality on a large-scale. The features chosen for the models were related to reference editing and the semantics of the triples they referred to. 61% of the references evaluated were relevant and authoritative. Bad references were often links that changed and either stopped working or pointed to other pages. The machine learning models outperformed the baseline and were able to accurately predict non-relevant and non-authoritative references. Further work should focus on implementing our approach in Wikidata to help editors find bad references.

Wikidata is a community-driven knowledge graph which has drawn much attention from researchers and practitioners since its inception in 2012. The large user pool behind this project has been able to produce information spanning over several domains, which is openly released and can be reused to feed any information-based application. Collaborative production processes in Wikidata have not yet been explored. Understanding them is key to prevent potentially harmful community dynamics and ensure the sustainability of the project in the long run. We performed a regression analysis to investigate how the contribution of different types of users, i.e. bots and human editors, registered or anonymous, influences outcome quality in Wikidata. Moreover, we looked at the effects of tenure and interest diversity among registered users. Our findings show that a balanced contribution of bots and human editors positively influence outcome quality, whereas higher numbers of anonymous edits may hinder performance. Tenure and interest diversity within groups also lead to higher quality. These results may be helpful to identify and address groups that are likely to underperform in Wikidata. Further work should analyse in detail the respective contributions of bots and registered users.

Wikidata is a community-driven knowledge graph, strongly linked to Wikipedia. However, the connection between the two projects has been sporadically explored. We investigated the relationship between the two projects in terms of the information they contain by looking at their external references. Our findings show that while only a small number of sources is directly reused across Wikidata and Wikipedia, references often point to the same domain. Furthermore, Wikidata appears to use less Anglo-American-centred sources. These results deserve further in-depth investigation.

Page 1. Southampton Accessibility Tools M Wald, University of Southampton, UK +442380593667 M.Wald@soton.ac.uk EA Draffan, S, Skuse University of Southampton, UK R Newman, C Phethean University of Southampton, UK ABSTRACT In this paper, we describe three tools that facilitate 'crowdsourcing' open source development to help overcome accessibility, usability and productivity issues identified by disabled students.