Measuring self-focus bias in community-maintained knowledge repositories

Hecht, Brent; Gergle, Darren

doi:10.1145/1556460.1556463

Cited by 87 publications

(97 citation statements)

References 12 publications

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“…In particular, as our choice of sampling strategy, we focused on those that represent urban elements commonly interpreted as leisure POIs, such as cafes, restaurants, pubs and bars. These are the categories that are most common to mobile applications such as MyCityWay, 6 Google HotPot 7 and Foursquare, 8 which are used by city dwellers to navigate the urban landscape. To ensure we are considering genuine crowd-sourcing contributions, and not those made by bots (i.e., mass imports), we have eliminated from the dataset those users who performed an excessive number of edits in a very short time (i.e., those who edited more than 40 POIs in a single changeset session in OSM, with the threshold of 40 chosen after manual inspection of the per-user edit distribution).…”

Section: Research Methodology Dataset Descriptionmentioning

confidence: 99%

“…For example, [10] studied gender imbalance in Wikipedia, and reported on how topics of particular interest to females were substantially less covered than topics of specific interest to males. In [8], the indegree summation (i.e., number of inlinks per article in the Wikipedia Article Graph) on 15 different language editions of Wikipedia was analysed; their findings suggest that population is not the most important factor to be considered, and other factors such as fluency in languages are more strongly correlated with indegree instead. They conclude that, when developing technologies to rely upon community maintained repositories, contextual factors of the contributors, such as language and culture, must be carefully examined.…”

Section: Background and Related Workmentioning

confidence: 99%

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Putting ubiquitous crowd-sourcing into context

Mashhadi¹,

Quattrone

Capra

2013

Proceedings of the 2013 Conference on Computer Supported Cooperative Work

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Abstract:Ubiquitous crowd-sourcing has become a popular mechanism to harvest knowledge from the masses. OpenStreetMap (OSM) is a successful example of ubiquitous crowd-sourcing, where citizens volunteer geographic information in order to build and maintain an accurate map of the changing world. Research has shown that OSM information is accurate, by comparing it with centrally maintained spatial information such as Ordnance Survey. However, we find that coverage is low and non uniformly distributed, thus challenging the suitability of ubiquitous crowd-sourcing as a mechanism to map the whole world. In this paper, we investigate what contextual factors correlate with coverage of OSM information in urban settings. We find that, although there is a direct correlation between population density and information coverage, other socio-economic factors also play an important role. We discuss the implications of these findings with respect to the design of urban crowd-sourcing applications. UCL DEPARTMENT OF COMPUTER SCIENCE Putting Ubiquitous Crowd-sourcing into Context ABSTRACTUbiquitous crowd-sourcing has become a popular mechanism to harvest knowledge from the masses. OpenStreetMap (OSM) is a successful example of ubiquitous crowd-sourcing, where citizens volunteer geographic information in order to build and maintain an accurate map of the changing world. Research has shown that OSM information is accurate, by comparing it with centrally maintained spatial information such as Ordnance Survey. However, we find that coverage is low and non uniformly distributed, thus challenging the suitability of ubiquitous crowd-sourcing as a mechanism to map the whole world. In this paper, we investigate what contextual factors correlate with coverage of OSM information in urban settings. We find that, although there is a direct correlation between population density and information coverage, other socio-economic factors also play an important role. We discuss the implications of these findings with respect to the design of urban crowd-sourcing applications.

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Section: Research Methodology Dataset Descriptionmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Putting ubiquitous crowd-sourcing into context

Mashhadi¹,

Quattrone

Capra

2013

Proceedings of the 2013 Conference on Computer Supported Cooperative Work

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“…Moreover it has been found that each language Wikipedia exhibits a self-focus bias towards articles about regions where that language is largely spoken [12].…”

Section: Language Wikipedia Communities and Their Points Of Viewmentioning

confidence: 99%

Manypedia: Comparing language points of view of Wikipedia communities

Massa

Scrinzi

2013

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The 4 million articles of the English Wikipedia have been written in a collaborative fashion by more than 16 million volunteer editors. On each article, the community of editors strive to reach a neutral point of view, representing all significant views fairly, proportionately, and without biases. However, beside the English one, there are more than 280 editions of Wikipedia in different languages and their relatively isolated communities of editors are not forced by the platform to discuss and negotiate their points of view. So the empirical question is: do communities on different language Wikipedias develop their own diverse Linguistic Points of View (LPOV)? To answer this question we created and released as open source Manypedia, a web tool whose aim is to facilitate cross-cultural analysis of Wikipedia language communities by providing an easy way to compare automatically translated versions of their different representations of the same topic.

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“…Previous research on inter-language analysis of Wikipedia articles mainly studied the geographic focus [2,3], famous and prominent people [4][5][6], historical figures [7], editing behavior [1], user interaction [8], and article structure [9] of Wikipedia editions in different languages. Additionally, concept overlap [10], inter-language links [11] between Wikipedia versions in multiple languages have been examined.…”

Section: Introductionmentioning

confidence: 99%