7 research outputs found
Social Interactions vs Revisions, What is important for Promotion in Wikipedia?
In epistemic community, people are said to be selected on their knowledge
contribution to the project (articles, codes, etc.) However, the socialization
process is an important factor for inclusion, sustainability as a contributor,
and promotion. Finally, what does matter to be promoted? being a good
contributor? being a good animator? knowing the boss? We explore this question
looking at the process of election for administrator in the English Wikipedia
community. We modeled the candidates according to their revisions and/or social
attributes. These attributes are used to construct a predictive model of
promotion success, based on the candidates's past behavior, computed thanks to
a random forest algorithm.
Our model combining knowledge contribution variables and social networking
variables successfully explain 78% of the results which is better than the
former models. It also helps to refine the criterion for election. If the
number of knowledge contributions is the most important element, social
interactions come close second to explain the election. But being connected
with the future peers (the admins) can make the difference between success and
failure, making this epistemic community a very social community too
Document controversy classification based on the wikipedia category structure
Dispute and controversy are parts of our culture and cannot be omitted on the Internet (where it becomes more anonymous). There have been many studies on controversy, especially on social networks such as Wikipedia. This free on-line encyclopedia has become a very popular data source among many researchers studying behavior or natural language processing. This paper presents using the category structure of Wikipedia to determine the controversy of a single article. This is the first part of the proposed system for classification of topic controversy score for any given text
Document controversy classification based on the wikipedia category structure
Dispute and controversy are parts of our culture and cannot be omitted on the Internet (where it becomes more anonymous). There have been many studies on controversy, especially on social networks such as Wikipedia. This free on-line encyclopedia has become a very popular data source among many researchers studying behavior or natural language processing. This paper presents using the category structure of Wikipedia to determine the controversy of a single article. This is the first part of the proposed system for classification of topic controversy score for any given text
Automated credibility assessment on twitter
In this paper, we make a practical approach to automated credibility assessment on Twitter. We describe the process behind the design of an automated classifier for information credibility assessment. As an addition, we propose practical implementation of TwitterBOT, a tool which is able to score submitted tweets while working in the native Twitter interface