Wiki-research-l June 2019

wiki-research-l@lists.wikimedia.org

28 participants
18 discussions

Wikipedia Research policy
by song＠cs.umn.edu 14 Jul '23

14 Jul '23

Pursuant to prior discussions about the need for a research policy on Wikipedia, WikiProject Research is drafting a policy regarding the recruitment of Wikipedia users to participate in studies. At this time, we have a proposed policy, and an accompanying group that would facilitate recruitment of subjects in much the same way that the Bot Approvals Group approves bots. The policy proposal can be found at: http://en.wikipedia.org/wiki/Wikipedia:Research The Subject Recruitment Approvals Group mentioned in the proposal is being described at: http://en.wikipedia.org/wiki/Wikipedia:Subject_Recruitment_Approvals_Group Before we move forward with seeking approval from the Wikipedia community, we would like additional input about the proposal, and would welcome additional help improving it. Also, please consider participating in WikiProject Research at: http://en.wikipedia.org/wiki/Wikipedia:WikiProject_Research -- Bryan Song GroupLens Research University of Minnesota

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[Wikimedia Research Showcase] June 26, 2019 at 11:30 AM PST, 19:30 UTC
by Janna Layton 27 Jun '19

27 Jun '19

Hi all, The next Research Showcase will be live-streamed this Wednesday, June 26, at 11:30 AM PST/19:30 UTC. We will have three presentations this showcase, all relating to Wikipedia blocks. YouTube stream: https://www.youtube.com/watch?v=WiUfpmeJG7E As usual, you can join the conversation on IRC at #wikimedia-research. You can also watch our past research showcases here: https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase This month's presentations: Trajectories of Blocked Community Members: Redemption, Recidivism and Departure By Jonathan Chang, Cornell University Community norm violations can impair constructive communication and collaboration online. As a defense mechanism, community moderators often address such transgressions by temporarily blocking the perpetrator. Such actions, however, come with the cost of potentially alienating community members. Given this tradeoff, it is essential to understand to what extent, and in which situations, this common moderation practice is effective in reinforcing community rules. In this work, we introduce a computational framework for studying the future behavior of blocked users on Wikipedia. After their block expires, they can take several distinct paths: they can reform and adhere to the rules, but they can also recidivate, or straight-out abandon the community. We reveal that these trajectories are tied to factors rooted both in the characteristics of the blocked individual and in whether they perceived the block to be fair and justified. Based on these insights, we formulate a series of prediction tasks aiming to determine which of these paths a user is likely to take after being blocked for their first offense, and demonstrate the feasibility of these new tasks. Overall, this work builds towards a more nuanced approach to moderation by highlighting the tradeoffs that are in play. Automatic Detection of Online Abuse in Wikipedia By Lane Rasberry, University of Virginia Researchers analyzed all English Wikipedia blocks prior to 2018 using machine learning. With insights gained, the researchers examined all English Wikipedia users who are not blocked against the identified characteristics of blocked users. The results were a ranked set of predictions of users who are not blocked, but who have a history of conduct similar to that of blocked users. This research and process models a system for the use of computing to aid human moderators in identifying conduct on English Wikipedia which merits a block. Project page: https://meta.wikimedia.org/wiki/University_of_Virginia/Automatic_Detection_… Video: https://www.youtube.com/watch?v=AIhdb4-hKBo First Insights from Partial Blocks in Wikimedia Wikis By Morten Warncke-Wang, Wikimedia Foundation The Anti-Harassment Tools team at the Wikimedia Foundation released the partial block feature in early 2019. Where previously blocks on Wikimedia wikis were sitewide (users were blocked from editing an entire wiki), partial blocks makes it possible to block users from editing specific pages and/or namespaces. The Italian Wikipedia was the first wiki to start using this feature, and it has since been rolled out to other wikis as well. In this presentation, we will look at how this feature has been used in the first few months since release. -- Janna Layton (she, her) Administrative Assistant - Audiences & Technology Wikimedia Foundation <https://wikimediafoundation.org/>

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Re: [Wiki-research-l] [Analytics] [Wikimedia Research Showcase] June 26, 2019 at 11:30 AM PST, 19:30 UTC
by James Salsman 27 Jun '19

27 Jun '19

> For those that couldn't make it, Is there are summary of what was said? Full recording: https://www.youtube.com/watch?v=WiUfpmeJG7E Slides: https://www.mediawiki.org/wiki/File:Trajectories_of_Blocked_Community_Membe… https://meta.wikimedia.org/wiki/University_of_Virginia/Automatic_Detection_…

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Questions about SuggestBot
by Haifeng Zhang 27 Jun '19

27 Jun '19

Hi all, Is the SuggestBot still in use in Wikipedia? Are there similar task routing tools that have been deployed in Wikipedia? Where in Wikipedia the use of such tools or bots was documented? Thanks, Haifeng Zhang

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[CfP] 1st International Workshop on Approaches for Making Data Interoperable (AMAR 2019)
by Lucie-Aimée Kaffee 26 Jun '19

26 Jun '19

Call For Papers 1st International Workshop on Approaches for Making Data Interoperable (AMAR 2019) https://events.tib.eu/amar2019/ co-located with SEMANTiCS 2019 September 09 – 12, 2019 Karlsruhe, Germany Submission deadline: July 9, 2019 ------------------------------------------------------------------------------------------------------------------------ Overview ------------------------------------------------------------------------------------------------------------------------ Recently, there has been a rapid growth in the amount of data available on the Web. Data is produced by different communities working in a wide range of domains, using several techniques. This way a large volume of data in different formats and languages is generated. Accessibility of such heterogeneous and multilingual data becomes an obstacle for reuse due to the incompatibility of data formats and the language gap. This incompatibility of data formats impedes the accessibility of data sources to the right community. For instance, most of open domain question answering systems are developed to be effective when data is represented in RDF. They can not operate with data in the very common CSV files or presented in unstructured formats. Usually, the data they draw from is in English rendering them unable to answer questions e.g. in Spanish. On the other hand, NLP applications in Spanish cannot make use of a knowledge graph in English. Different communities have different requirements in terms of data representation and modeling. It is crucial to make the data interoperable to make it accessible for a variety of applications. ------------------------------------------------------------------------------------------------------------------------ Topics of Interest ------------------------------------------------------------------------------------------------------------------------ We invite paper submissions from two communities: (i) data consumers and (ii) data providers. This includes practitioners, such as data scientists, that have experience in fitting the data available to their use case; Semantic Web researchers, that have been investigating data reuse from heterogeneous data in tools; researchers in the field of data linking and translation; and other researchers working on the general field of data integration. We invite submissions from the following communities: - Data Integration - Multilingual Data - Data Linking - Ontology and Knowledge Engineering We welcome original contributions about all topics related to data interoperability, including but not limited to: - Approaches to convert data between formats, languages, and schema - Best practices for processing heterogeneous data - Translation of different language data - Cross-lingual applications - Recommendations for language modeling in linked data - Labeling of data with natural language information - Datasets for different communities’ data needs - Tools reusing different data formats - Converting datasets between different formats - Applications in different domains, e.g., Life Sciences, Scholarly, Industry 4.0, Humanities ------------------------------------------------------------------------------------------------------------------------ Author Instructions ------------------------------------------------------------------------------------------------------------------------ Paper submission this workshop will be via EasyChair ( https://easychair.org/conferences/?conf=amar2019). The papers should follow the Springer LNCS format, and be submitted in PDF on or before July 9, 2019 (midnight Hawaii time). We accept papers of the following formats: - Full research papers (8 - 12 pages) - Short research papers (3 - 5 pages) - Position papers (6 - 8 pages) - Resource papers (8 - 12 pages, including the publication of the dataset) - In-Use papers (6 - 8 pages) Accepted papers will be published as CEUR workshop proceedings. We target the creation of a special issue including the best papers of the workshop. ------------------------------------------------------------------------------------------------------------------------ Important Dates ------------------------------------------------------------------------------------------------------------------------ Submission: July 9, 2019 Notification: July 30, 2019 Workshop: September 9, 2019 ------------------------------------------------------------------------------------------------------------------------ Workshop Organizers ------------------------------------------------------------------------------------------------------------------------ Lucie-Aimée Kaffee, University of Southampton, UK & TIB Leibniz Information Centre for Science and Technology, Hannover, Germany Kemele M. Endris, TIB Leibniz Information Centre for Science and Technology and L3S Research Centre University of Hannover, Germany Maria-Esther Vidal, TIB Leibniz Information Centre for Science and Technology and and L3S Research Centre University of Hannover, Germany Please contact us, if you have any questions ------------------------------------------------------------------------------------------------------------------------ Program Committee ------------------------------------------------------------------------------------------------------------------------ Jeremy Debattista, Trinity College Dublin Irlan Grangel, Bosch Corporate Research Lydia Pintscher, Wikimedia Deutschland (Wikidata) Alokkumar Jha, Insight Centre for Data Analytics Amrapali Zaveri, Maastricht University Maribel Acosta, Karlsruhe Institute of Technology Manolis Koubarakis, National and Kapodistrian University of Athens Elena Montiel Ponsoda, Universidad Politécnica de Madrid Javier D. Fernández, Vienna University of Economics and Business Diego Collarana, Enterprise Information System (EIS) Elena Demidova, L3S Research Center David Chaves Fraga, Universidad Politécnica de Madrid Jose M. Gimenez Garcia, Universite Jean-Monnet Julia Bosque Gil, Universidad Politécnica de Madrid -- Lucie-Aimée Kaffee Web and Internet Science Group School of Electronics and Computer Science University of Southampton

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Difference between vandal fighting with vs. without tools
by Haifeng Zhang 25 Jun '19

25 Jun '19

Hi all, This might be a known fact already. Does it take less time (on average) for an editor to identify a vandalistic edit when using counter-vandalism tools, e.g., Huggle or STiki? If so, what features of these tools support such decision? Thanks for your time, Haifeng Zhang

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Monthly/Yearly Edits per country
by Adam Ferris 25 Jun '19

25 Jun '19

Hello all, I am new to this mailing list, and a new researcher so apologies if this is not the right mailing list for this question, but I hope you might be able to help me. I am trying to recreate the map included below, with Wikipedia edits per 10,000 internet users but with newer data, I was hoping year 2018 data that I can then average per month, although it doesn’t have to be a Calendar year I suppose, it could be Feb-2018 to Feb-2019. The key is that I would like newer data. I have looked at the sources of these maps, and they all seem to end in 2013 or 2014. This source got me close, but has no Edit data, only Viewing data. https://stats.wikimedia.org/wikimedia/squids/SquidReportPageViewsPerCountry… Could anyone help me in finding the right dataset to recreate this map? (Image too heavy to be sent in email) Like this image: https://i0.wp.com/geonet.oii.ox.ac.uk/wp-content/uploads/sites/46/2016/09/W… From this article: https://www.oii.ox.ac.uk/blog/the-geography-of-wikipedia-edits/ I’m very grateful for any help you can offer me. Very best, Adam

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Hive and Oozie unavailable for maintenance on Wed 26th 9 AM CEST
by Luca Toscano 25 Jun '19

25 Jun '19

Hi everybody, as part of https://phabricator.wikimedia.org/T225306 I need to reboot the an-coord1001 host, that runs the Hive server/metastore and Oozie. Tomorrow June 26th I'll reboot the host at around 9 AM CEST, the maintenance window should last 10/15 minutes more or less. This means that hive jobs might fail during that timeframe, please let me know if it is a problem. Thanks in advance, Luca (on behalf of the Analytics team)

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Upcoming Research Newsletter: New Papers Open For Review
by Mohammed Sadat Abdulai 23 Jun '19

23 Jun '19

Hi, We’re preparing for the June 2019 research newsletter and looking for contributors. Please take a look at https://etherpad.wikimedia.org/p/WRN201906 and add your name next to any paper you are interested in covering. Our target publication date is on 29 June 23:59 UTC. If you can't make this deadline but would like to cover a particular paper in the subsequent issue, leave a note next to the paper's entry below. As usual, short notes and one-paragraph reviews are most welcome. Highlights from this month: - Assessing The Factual Accuracy of Generated Text - Automatic Detection of Online Abuse and Analysis of Problematic Users in Wikipedia - Gender and deletion on Wikipedia - Improving Knowledge Base Construction from Robust Infobox Extraction - Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics - Neural Based Statement Classification for Biased Language - People Who Can Take It: How Women Wikipedians Negotiate and Navigate Safety - Predicting Economic Development using Geolocated Wikipedia Articles - SEDTWik: Segmentation-based Event Detection from Tweets Using Wikipedia - StRE: Self Attentive Edit Quality Prediction in Wikipedia - TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables - Training and hackathon on building biodiversity knowledge graphs - Uncertainty During New Disease Outbreaks in Wikipedia - Using Wiktionary as a resource for WSD : the case of French verbs - Wikidata and the biodiversity knowledge graph - Wikidata: Recruiting the Crowd to Power Access to Digital Archives - WikiDataSets : Standardized sub-graphs from WikiData Mohammed S. Abdulai and Tilman Bayer [1] http://meta.wikimedia.org/wiki/Research:Newsletter[2] https://twitter.com/WikiResearch

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Research on Edit Size
by Haifeng Zhang 22 Jun '19

22 Jun '19

Dear folks, Are there studies that have examined what might affect edit size (e.g., # of words add/delete/modify in each revision). I am especially interested in the impact of editor's tenure/experience. Thanks, Haifeng Zhang

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Wiki-research-l June 2019