Ensuring the integrity of wikipedia: A data science approach

Francesca Spezzano

Ensuring the integrity of wikipedia: A data science approach

Computer Science Department

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, we present our research on the problem of ensuring the integrity of Wikipedia, the world's biggest free encyclopedia. As anyone can edit Wikipedia, many malicious users take advantage of this situation to make edits that compromise pages' content quality. Specifically, we present DePP, the state-of-the-art tool that detects article pages to protect with an accuracy of 93% and we introduce our research on identifying spam users. We show that we are able to classify spammers from benign users with 80.8% of accuracy and 0.88 mean average precision.

Original language	English
Pages	98-105
Number of pages	8
State	Published - 2017
Event	25th Italian Symposium on Advanced Database Systems, SEBD 2017 - Squillace Lido, Catanzaro, Italy Duration: 25 Jun 2017 → 29 Jun 2017

Conference

Conference	25th Italian Symposium on Advanced Database Systems, SEBD 2017
Country/Territory	Italy
City	Squillace Lido, Catanzaro
Period	25/06/17 → 29/06/17

Cite this

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title = "Ensuring the integrity of wikipedia: A data science approach",

abstract = "In this paper, we present our research on the problem of ensuring the integrity of Wikipedia, the world's biggest free encyclopedia. As anyone can edit Wikipedia, many malicious users take advantage of this situation to make edits that compromise pages' content quality. Specifically, we present DePP, the state-of-the-art tool that detects article pages to protect with an accuracy of 93% and we introduce our research on identifying spam users. We show that we are able to classify spammers from benign users with 80.8% of accuracy and 0.88 mean average precision.",

author = "Francesca Spezzano",

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AB - In this paper, we present our research on the problem of ensuring the integrity of Wikipedia, the world's biggest free encyclopedia. As anyone can edit Wikipedia, many malicious users take advantage of this situation to make edits that compromise pages' content quality. Specifically, we present DePP, the state-of-the-art tool that detects article pages to protect with an accuracy of 93% and we introduce our research on identifying spam users. We show that we are able to classify spammers from benign users with 80.8% of accuracy and 0.88 mean average precision.

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M3 - Paper

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Y2 - 25 June 2017 through 29 June 2017

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Ensuring the integrity of wikipedia: A data science approach

Abstract

Conference

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