Paper Info
Title
Belief Temporal Analysis of Expert Users: Case Study Stack Overflow.
Abstract
Question Answering communities have known a large expansion over the last few years. Reliable people sharing their knowledge are not that numerous. Thus, detecting experts since their first contributions can be considered as a challenge. We are interested in studying the activity of these platforms' users during a defined period of time. As the data collected is not always reliable, imperfections can occur. In order to manage these imperfections, we choose to use the mathematical background offered by the theory of belief functions. People say that the more time they spend within a community, the more knowledge they acquire. We investigate this assumption in this paper by studying the behavior of users without taking into consideration the reputation system proposed by Stack Overflow. Experiments with real data from Stack Overflow demonstrate that this model can be applied to any expertise detection problem. Moreover, it allows to identify potential future experts. The analysis allows us to study the behavior of experts and non expert users over time spent in the community. We can see that some users keep on being reliable while others do gain knowledge and improve their expertise measure.
Year
DOI
Venue
2017
10.1007/978-3-319-64283-3_27
Lecture Notes in Computer Science
Keywords
Field
DocType
Question answering community,Theory of belief functions,Expertise measure,Classification
Data mining,Question answering,Reputation system,Computer science,Artificial intelligence,Stack overflow,Machine learning
Conference
Volume
ISSN
Citations 
10440
0302-9743
0
PageRank 
References 
Authors
0.34
10
3
Name
Order
Citations
PageRank
Dorra Attiaoui141.42
Arnaud Martin215818.26
Boutheina Ben Yaghlane318933.49