Temporal characterization of the requests to Wikipedia

This paper presents an empirical study about the temporal patterns characterizing the requests submitted by users to Wikipedia. The study is based on the analysis of the log lines registered by the Wikimedia Foundation Squid servers after having sent the appropriate content in response to users' requests. The analysis has been conducted regarding the ten most visited editions of Wikipedia and has involved more than 14,000 million log lines corresponding to the traffic of the entire year 2009. The conducted methodology has mainly consisted in the parsing and filtering of users' requests according to the study directives. As a result, relevant information fields have been finally stored in a database for persistence and further characterization. In this way, we, first, assessed, whether the traffic to Wikipedia could serve as a reliable estimator of the overall traffic to all the Wikimedia Foundation projects. Our subsequent analysis of the temporal evolutions corresponding to the different types of requests to Wikipedia revealed interesting differences and similarities among them that can be related to the users' attention to the Encyclopedia. In addition, we have performed separated characterizations of each Wikipedia edition to compare their respective evolutions over time

A quantitative examination of the impact of featured articles in Wikipedia

This paper presents a quantitative examination of the impact of the presentation of featured articles as quality content in the main page of several Wikipedia editions. Moreover, the paper also presents the analysis performed to determine the number of visits received by the articles promoted to the featured status. We have analyzed the visits not only in the month when articles awarded the promotion or were included in the main page, but also in the previous and following ones. The main aim for this is to assess the attention attracted by the featured content and the different dynamics exhibited by each community of users in respect to the promotion process. The main results of this paper are twofold: it shows how to extract relevant information related to the use of Wikipedia, which is an emerging research topic, and it analyzes whether the featured articles mechanism achieve to attract more attention

Characterization of the Wikipedia Traffic

Since its inception, Wikipedia has grown to a solid and stable project and turned into a mass collaboration tool that allows the sharing and distribution of knowledge. The wiki approach that basis this initiative promotes the participation and collaboration of users. In addition to visits for browsing its contents, Wikipedia also receives the contributions of users to improve them. In the past, researchers paid attention to different aspects concerning authoring and quality of contents. However, little effort has been made to study the nature of the visits that Wikipedia receives. We conduct such an study using a sample of users' requests provided by the Wikimedia Foundation in the form of Squid log lines. Our sample contains more that 14,000 million requests from users all around the world and directed to all the projects maintained by the Wikimedia Foundation, including different editions of Wikipedia. This papers describes the work made to characterize the traffic directed to Wikipedia and consisting of the requests sent by its users. Our main aim is to obtain a detailed description of its composition in terms of the percentages corresponding to the different types of requests making part of it. The benefits from our work may range from the prediction of traffic peaks to the determination of the kind of resources most often requested, which can be useful for scalability considerations

Mobilizing the Trump Train: Understanding Collective Action in a Political Trolling Community

Political trolls initiate online discord not only for the lulz (laughs) but also for ideological reasons, such as promoting their desired political candidates. Political troll groups recently gained spotlight because they were considered central in helping Donald Trump win the 2016 US presidential election, which involved difficult mass mobilizations. Political trolls face unique challenges as they must build their own communities while simultaneously disrupting others. However, little is known about how political trolls mobilize sufficient participation to suddenly become problems for others. We performed a quantitative longitudinal analysis of more than 16 million comments from one of the most popular and disruptive political trolling communities, the subreddit /r/The\_Donald (T\D). We use T_D as a lens to understand participation and collective action within these deviant spaces. In specific, we first study the characteristics of the most active participants to uncover what might drive their sustained participation. Next, we investigate how these active individuals mobilize their community to action. Through our analysis, we uncover that the most active employed distinct discursive strategies to mobilize participation, and deployed technical tools like bots to create a shared identity and sustain engagement. We conclude by providing data-backed design implications for designers of civic media

Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.Comment: 13 pages, Including Supporting Information, 7 Figures, Download the dataset from: http://wwm.phy.bme.hu/SupplementaryDataS1.zi

The Informatics Audit - A Collaborative Process

The paper present issues regarding the audit in informatics field, the audit seen as a collaborative process and how the collaborative banking systems are audited. In this paper, the methodology and techniques for an effective audit process are described. There are highlighted some aspects regarding the assessment of collaborative systems and specific flows of informatics audit.Informatics Audit, Collaborative Process, Collaborative System, Methodology, Banking

Linking Data Across Universities: An Integrated Video Lectures Dataset

This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scraped and structured according to well-known vocabularies, like FOAF 1, or the W3C Ontology for Media Resources 2. To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project 3. An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14,000 video lectures from 27 different institutions and categorized under a common classification scheme