Jonathas Magalhães

Predicting potential responders in twitter: a query routing algorithm

A phenomenon not so recent is the substantial increase in popularity and use of online social networks. With that has emerged a new way to find information online: the social query, which consists of posting a question in a social network and wait for responses from close friends. Usually, a question is posted to be visible to everyone, but we believe that this is not the best way: there will be the possibility of receiving several responses (including wrong), keep receiving answers where there is no need, do not receive answers, etc. The query router problem consists of finding the most able individual in the personal social network of the questioner. This work presents an algorithm to Routing Questions in Twitter. The model was validated through its predict capacity and the results shows that its recommendations match in half cases only when combined with a technique to enrich the information present in the question.

Sistemas de Recomendação de Recursos Educacionais: conceitos, técnicas e aplicações

Nos ultimos anos tem-se observado um crescimento acentuado no volume de recursos digitais educacionais disponiveis na Web aberta ou mesmo em bibliotecas digitais privadas. Portanto, trata-se de um grande volume de recursos, tornando uma tarefa complicada para um usuario ter acesso ao que lhe interessa. Para abordar esse problema, surgiram recentemente os Sistemas de Recomendacao personalizada. Entretanto, esse tipo de sistema precisa ser mais bem disseminado para os usuarios de recursos educacionais, a exemplo de estudantes e professores, pois ajudarao significativamente no desempenho de suas atividades. Neste capitulo, os conceitos e as principais tecnicas e algoritmos de filtragem de informacao serao vistos com aplicacoes na recomendacao de recursos digitais educacionais, demonstrando o potencial desse tipo de sistema.

Enhancing the Status Message Question Asking Process on Facebook

People have been using Social Networks to search for help by broadcasting messages that reflect their information needs. However, several factors, usually not considered by the user, influence the outcome of receiving or not an answer. In this work, we aim to increase the users’ chances of finding someone who could help them. For this purpose, we propose a mobile app called Social Query, which guides the users through some steps before they share the problem with their friends. As far as we know, this is the first work to merge these three aspects of the social search: Question Rephrasing, Expert Search Filtering and Expertise Finding. To evaluate our proposal, we ran a questionnaire in which users considered Useful most functions of the app.

Routing Questions in Twitter: An Effective Way to Qualify Peer Helpers

Essentially social query consists in sharing a question on a social network and waiting for responses. The usual strategy of sharing a public question is inefficient because it does not ensure an answer, either quality or agility. We assume that directing the question to a specific user is the best way to get a right and quick answer. However, choosing this person could be a hard task and by choosing a wrong one we could receive an incorrect answer or the person might just ignore the question. In this paper, we proposed, implemented and evaluated a model based on a multi-criteria approach to find the most suitable user to receive a question on Twitter. We rank expert candidates using the Weight Product Model. One of the main differentials of our model is the use of multi-criteria and that it was built for a social network context. In the evaluation, using as metrics the Normalized Discounted Cumulative Gain and the Spearman Rank Correlation Coefficient, our technique achieved promising results in predicting the usefulness of real responders answer.

A Meta-Information Extractor for Interrogative Sentences

The development of tools for Information Retrieval Systems or Expertise Finding Systems has a common task: the understanding of the information need. Since the information need is usually expressed through natural language, the computational processing of the information need involves several NLP techniques. Fortunately, there is a vast set of tools for English, but others languages have been marginalized. Thus, in this paper, we present a Web Service that offers to clients NLP treatment for interrogative sentences written in Brazilian Portuguese. The Web Service receives the question as input e returns its meta-information. The main differential of our proposal is that we offer a full analysis of the question text using a single function. We evaluate a feature of the Web Service named Category labeler, responsible for automatic discover the subject of the question, and we found that it has a true positive rate higher than 50% (\(\alpha \)=10%).

Integrating educational repositories to improve the reuse of learning objects

Over the last decade, research in the area of e-learning has employed efforts to propose solutions for sharing and reuse of educational resources. Such initiative has increased with the rise of the so-called Open Educational Resources (OER). OER are freely accessible documents and media that are useful for learning purposes. Hence, the Web today offers a comprehensive amount of OER repositories. However, these efforts led to a fragmented landscape of concurrent metadata schemas or interface mechanisms that were not designed to offer mechanisms to enable the exchange of resources between these repositories. Recent studies show that this scenario makes it harder the reuse of resources located in OER Repositories. This paper conduct a study using a questionnaire to analyze how students and professors search for educational resources on the Web, in order to better characterize the problem. Questionnaire results confirm the low reuse of educational resources. To alleviate this problem, our conclusions indicate the need for an integrated approach to search for Educational Resources.