WHAD: Wikipedia historical attributes data

Alfonseca, Enrique; Garrido, Guillermo; Delort, Jean-Yves; Peñas, Anselmo

doi:10.1007/s10579-013-9232-5

Cited by 15 publications

(6 citation statements)

References 28 publications

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“…According to the literature, the classification of wiki edits encompasses the detection of paid [20], puffery [21], reverted [22], [23], [24], toxic [25], [26] and vandal [9], [10], [12], [13], [17], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38] reviews. Similarly, prediction focuses on review quality [39], [40], [41] as well as on editor and article quality [18], [42], [43], [44], [45].…”

Section: B Analysis Of Reviewsmentioning

confidence: 99%

Interpretable Classification of Wiki-Review Streams

García-Méndez,

Leal,

Malheiro

et al. 2023

IEEE Access

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Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90 % values for all evaluation metrics (accuracy, precision, recall, and F-measure).INDEX TERMS Data reliability and fairness, data-stream processing and classification, synthetic data, transparency, vandalism, wikis.

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Section: B Analysis Of Reviewsmentioning

confidence: 99%

Interpretable Classification of Wiki-Review Streams

García-Méndez,

Leal,

Malheiro

et al. 2023

IEEE Access

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“…Some of these errors are a repercussion of vandalism. Authors in [8], propose to the technique for historical structured data extraction and vandalism detectionfrom the Wikipedia edit history.Vandalbots and computer programs can be designed to detect and revert vandalism. ClueBot and VoABot II are some of the efforts in the direction.…”

Section:  Vandalismmentioning

confidence: 99%

Using Big Data Technique for Building Edit Alert System for Wikipedia Infoboxes Based on Map-Reduce Method

Bhatia¹,

Halder²,

Yadav³

et al. 2018

IJIRCST

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Wikipedia is an online encyclopedia and has become a vital information resource for users as well as for many knowledge bases derived from it. This information requires manual editing for update. Wikipedia provides an infobox on the right hand side of many articles. An infobox of a Wikipedia article generally contains key facts in thearticle and is organized as attribute-value pairs. All the Wikipedia's content is manually updated or maintained by contributors. This leads to the fact that its information is not updated regularly and completely. In this paper, we present a novel system that focuses onprediction of data items that are most likely to be updated, based on the category of page, record key, last time updated, etc. for alerting Wikipedia editors, about the data items that might need update soon, using Time series modeling. Concept of Bipartite graph is used to perform user based collaborative filtering to find similar editors who might be interested in editing the infobox. The update alert is sent to editors found using Bipartite graph along with the past editors of a particular infobox. The technique to deal with vandalic and erroneous edits is also discussed and its analysis is given. We have also presented various tasks that can be carried out on infoboxes.

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“…A number of information science studies relied on Wikipedia's so-called \revision metadata" that documents who made a particular revision to the Wikipedia article and when as well as \rollbacks" -the process of restoring a database or program to a previously de¯ned state -to detect vandalism (e.g. Alfonseca et al, 2013;West et al, 2010). Similarly, Yan and McLane (2012) discussed metadata management process for \revision metadata", including the edits, history, and tracking, made to spatial data and Geographic Information System (GIS) map¯g-ures.…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

Building a Framework of Metadata Change to Support Knowledge Management

Zavalina

Kizhakkethil

Alemneh

et al. 2015

J. Info. Know. Mgmt.

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Abstract. Evolving user needs and relevance require continuous change and reform. A good digital collection has mechanisms to accommodate the di®ering uses being made of the digital library system. In a metadata management context, change could mean to transform, substitute, or make the content of a metadata record di®erent from what it is or from what it would be if left alone. In light of the evolving compliance requirements, this paper analyses the three most common types of change within metadata records as well as their subcategories and discusses the possible implications of such changes within and beyond the metadata records.

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WHAD: Wikipedia historical attributes data

Cited by 15 publications

References 28 publications

Interpretable Classification of Wiki-Review Streams

Interpretable Classification of Wiki-Review Streams

Using Big Data Technique for Building Edit Alert System for Wikipedia Infoboxes Based on Map-Reduce Method

Building a Framework of Metadata Change to Support Knowledge Management

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