Creating, destroying, and restoring value in wikipedia

Priedhorsky, Reid; Chen, Jilin; Lam, Shyong K.; Panciera, Katherine; Terveen, Loren; Riedl, John

doi:10.1145/1316624.1316663

Cited by 264 publications

(197 citation statements)

References 16 publications

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“…When epochs=600, RMSE for training data and checking data are shown in (5). Figure 12, shows the output of the system.…”

Section: Methodsmentioning

confidence: 99%

“…Lih [19] measured the quality of Wikipedia articles by the independent variables of linking, number of edits, number of authors, article size, and other metadata from Wikipedia. Priedhorsky et al [5] have examined the impact of editing and editors on the Wikipedia value. Stein and Hess [20] have demonstrated that there is a significant relationship between the featured articles and increasing number of authors.…”

Section: Determining the Quality Of Wikipedia Articlesmentioning

confidence: 99%

“…Free access, constant updates, extensive coverage, and diversity have turned Wikipedia into one of the most unmatched social websites in the cyberspace [3]. Increasing the number of members of a group may usually reduce the effectiveness of teamwork; however, the researchers have assessed wisdom of crowds and collective action as the success factors of Wikipedia [4,5,6]. The users residing in the Europe and North America have mostly contributed in writing the articles of Persian Wikipedia and the Iranian users' contribution is only about 45% [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Qualifying Articles of Persian Wikipedia Encyclopedia Through J48 Algorithm, ANFIS and Subtractive Clustering

Seyedsadr

Afsharkazemi²,

Nikoomaram³

2015

ACIS

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Abstract:Since Wikipedia encyclopedia is one of the most popular web sites on the internet, providing accurate information is of abundant importance. In this research, the effective variables on quality of Persian articles are identified and a system is, then, designed for judging articles in three quality levels: high quality, cleanup needed, and deletion. First, the variables relating to the articles included in the list of featured articles, good articles, cleanup needed, and deletion articles are collected. Then, two methods are used for the analysis of data: First, a decision tree explains the relationships among the collected variables as rules that are implemented by adaptive neuro fuzzy interference system. Second, the data are implemented by subtractive clustering algorithm and the error of both methods is, finally, measured and compared. The results indicate that the average daily hits, total views, page length, total number of edits, total number of authors, and number of templates used are directly related to quality of Persian articles while the number of recent number of authors is inversely related to quality of articles.

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“…When epochs=600, RMSE for training data and checking data are shown in (5). Figure 12, shows the output of the system.…”

Section: Methodsmentioning

confidence: 99%

Section: Determining the Quality Of Wikipedia Articlesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Qualifying Articles of Persian Wikipedia Encyclopedia Through J48 Algorithm, ANFIS and Subtractive Clustering

Seyedsadr

Afsharkazemi²,

Nikoomaram³

2015

ACIS

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show abstract

“…The most common application is detection and measurement of popular news topics or events [13][14][15][16][17]. The data have also been used to study the dynamics of Wikipedia itself [18][19][20]. Social applications include evaluating toponym importance in order to make type size decisions for maps [21], measuring the flow of concepts across the world [22], and estimating the popularity of politicians and political parties [23].…”

Section: Author Summarymentioning

confidence: 99%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

Self Cite

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Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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“…Fortunately, those who damage articles seem poorly motivated. Priedhorsky et al [56] observes that 71% of damaging edits exhibit 'nonsense' or 'offensive' attributes. However, [35] indicates that Wikipedia's growing popularity will invite motivated malicious users, such as spammers, who have financial incentive to evade protections.…”

Section: Cautions For Value Usagementioning

confidence: 99%

Trust in collaborative web applications

West

Chang

Venkatasubramanian

et al. 2012

Future Generation Computer Systems

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Collaborative functionality is increasingly prevalent in web applications. Such functionality permits individuals to add -and sometimes modify -web content, often with minimal barriers to entry. Ideally, large bodies of knowledge can be amassed and shared in this manner. However, such software also provide a medium for nefarious persons to operate. By determining the extent to which participating content/agents can be trusted, one can identify useful contributions. In this work, we define the notion of trust for Collaborative Web Applications and survey the state-of-the-art for calculating, interpreting, and presenting trust values. Though techniques can be applied broadly, Wikipedia's archetypal nature makes it a focal point for discussion. KeywordsCollaborative web applications, trust, reputation, Wikipedia AbstractCollaborative functionality is increasingly prevalent in web applications. Such functionality permits individuals to add -and sometimes modify -web content, often with minimal barriers to entry. Ideally, large bodies of knowledge can be amassed and shared in this manner. However, such software also provide a medium for nefarious persons to operate. By determining the extent to which participating content/agents can be trusted, one can identify useful contributions. In this work, we define the notion of trust for Collaborative Web Applications and survey the state-of-the-art for calculating, interpreting, and presenting trust values. Though techniques can be applied broadly, Wikipedia's archetypal nature makes it a focal point for discussion.

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Creating, destroying, and restoring value in wikipedia

Cited by 264 publications

References 16 publications

Qualifying Articles of Persian Wikipedia Encyclopedia Through J48 Algorithm, ANFIS and Subtractive Clustering

Qualifying Articles of Persian Wikipedia Encyclopedia Through J48 Algorithm, ANFIS and Subtractive Clustering

Global Disease Monitoring and Forecasting with Wikipedia

Trust in collaborative web applications

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