Thiago Baesso Procaci

Finding Topical Experts in Question a Answer Communities

Question and Answer (Q&A) communities (such as Stackoverflow) have become important places for information exchange and knowledge creation. Their success relies predominantly on two aspects of the feedback generated by their members: quality and speed. Of these, the former reflects on the reputation of the community, whilst the latter is indicative of the efficiency of the Q&A system to correctly answer a given question. In this paper, we present a three phase study for identifying and recommending topical experts in Q&A communities. The first phase investigates the most relevant criteria for identifying reputable members of the community (often experts in a given field), the second phase introduces an approach based on semantic annotations to ascertain their area of specialism, and the last phase presents a method to recommend experts to answer questions in their areas of expertise. Our evaluation (carried out using real-world data from the Biology Stack Exchange Q&A community) shows that the numbers of answers provided by each member can be used as reliable indicators of expertise, and semantic annotations can be successfully used to identify the topics in which they specialize. Furthermore, on average, 74% of the recommendations suggested by our method were successful.

Finding Experts on Facebook Communities: Who Knows More?

Online communities have become important places for users to share information. In this context, the work described in this article aims to analyze computational methods that could allow us to identify users with the highest expertise levels on a specific knowledge domain in an online community. In this study the authots extracted data from a Java discussion group from an online community-Facebook, captured some important information and represented the community as a graph. Then, the authors compared the Bow-tie structure of this community with the ones from the Web and from a forum that are described in the literature. In addition, the authors tested some graph metrics and algorithms in order to analyze if they could provide a method to find the experts in this online community. The results show that four of the tested metrics can indicate if a user is an expert or not.

Experts and likely to be closed discussions in question and answer communities: An analytical overview

Abstract How do important members of online Question & Answer communities (who we call experts ) behave? And how do they influence the discussions in which they take part? This work reports on an investigation into these questions, which we answer through analyses exploring metrics, machine learning classifiers, and recommendations. We report on several findings: the degree of expertise correlates to behavioral patterns, whereby experts would rarely ask for help, and instead, predominantly provide help to other community members; the inclusion of an expert results in longer discussions. We propose a metric (the weighted sum), which enables us to better quantify the reputations of expert members of the community. We describe the use of four machine learning classifiers for the identification of both expert users and the most significant conversations within these communities. We propose a novel approach for a recommendation system, which utilizes semantic annotations to identify topical experts and to ascertain their respective area of specialism. We foresee the suitability of our expertise-finding methods and findings to support Learning Analytics, and in scenarios where users may apply lessons learnt from our results to improve their status in a community. Our findings can also inform systems for recommending experts and discussions.

Finding Reliable People in Online Communities of Questions and Answers

Online communities of questions and answers became important places for users to get information and share knowledge. We investigated metrics and strategies that allow the identification of users that are willing to help and provide good answers in a community, which we call the reliable people. In order to provide better performance on finding these users, we also raised some strategies for scope reduction. Then, we applied these metrics and strategies to three online communities of questions and answers available on the Web, which also provide user reputation grades, so it would be possible to verify the results on finding the reliable people.