Automatic identification of best answers in online enquiry communities

Online communities are prime sources of information. The Web is rich with forums and Question Answering (Q&A) communities where people go to seek answers to all kinds of questions. Most systems employ manual answer-rating procedures to encourage people to provide quality answers and to help users locate the best answers in a given thread. However, in the datasets we collected from three online communities, we found that half their threads lacked best answer markings. This stresses the need for methods to assess the quality of available answers to: 1) provide automated ratings to fill in for, or support, manually assigned ones, and; 2) to assist users when browsing such answers by filtering in potential best answers. In this paper, we collected data from three online communities and converted it to RDF based on the SIOC ontology. We then explored an approach for predicting best answers using a combination of content, user, and thread features. We show how the influence of such features on predicting best answers differs across communities. Further we demonstrate how certain features unique to some of our community systems can boost predictability of best answers

Features that Predict the Acceptability of Java and JavaScript Answers on Stack Overflow

Context: It is not uncommon for a new team member to join an existing Agile software development team, even after development has started. This new team member faces a number of challenges before they are integrated into the team and can contribute productively to team progress. Ideally, each newcomer should be supported in this transition through an effective team onboarding program, although prior evidence suggests that this is challenging for many organisations. Objective: We seek to understand how Agile teams address the challenge of team onboarding in order to inform future onboarding design. Method: We conducted an interview survey of eleven participants from eight organisations to investigate what onboarding activities are common across Agile software development teams. We also identify common goals of onboarding from a synthesis of literature. A repertory grid instrument is used to map the contributions of onboarding techniques to onboarding goals. Results: Our study reveals that a broad range of team onboarding techniques, both formal and informal, are used in practice. It also shows that particular techniques that have high contributions to a given goal or set of goals. Conclusions: In presenting a set of onboarding goals to consider and an evidence-based mechanism for selecting techniques to achieve the desired goals it is expected that this study will contribute to better-informed onboarding design and planning. An increase in practitioner awareness of the options for supporting new team members is also an expected outcome.Comment: Conference, 11 pages, 3 figures, 2 table

Characterisation of business documents: an approach to the automation of quality assessment

This thesis explores a new approach to automatic characterisation of business documents of different levels of document effectiveness. Supervised text categorisation techniques are used to derive text features that characterise a specific type of business document in accordance with pre-assigned levels of document utility. The documents in question are the executive summary sections of a representative sample of sales proposal documents. The executive summaries are first rated by domain experts against a quality framework comprising pre-selected dimensions of document quality. An automatic analysis of the texts shows that certain words, word sequences, and patterns of words have the capacity to discriminate between executive summaries of varying levels of document effectiveness. Function words, which are frequently ignored in many text classification tasks, are retained and are shown to provide an important element of the word patterns. Automatic text classifiers that utilise these features are shown to categorise previously unseen executive summaries at an acceptable level of classification performance. The outcomes of the research are applied to the development of a new computer application. The application identifies, in the text of a new executive summary, word patterns that discriminate between sets of summaries previously categorised into different levels of document utility. The action of highlighting the respective categories of discriminating word patterns directs authors to areas of text that may need further attention. A trial of a prototype of the application suggests that it provides an effective way to help sales professionals improve the content and quality of the text of this type of business document. Moreover, as the approach is suitably generic, it could be applied to different types of document in different domains