Analytics February 2017

analytics@lists.wikimedia.org

27 participants
29 discussions

Your feedback matters: Last reminder about the global Wikimedia survey

by Edward Galvez

Hello Wikimedia technical contributors & developers: Just sending one last reminder. The Wikimedia Foundation is asking for your feedback in a survey. ***The survey will close on February 15, 2017.*** We want to know how well we are supporting your contributions on and off wiki, and how we can change or improve things in the future.[1] The opinions you share will directly affect the current and future work of the Wikimedia Foundation. To say thank you for your time, we are giving away 10 Wikimedia T-shirts to randomly selected people who take the survey.[2] The survey is available in various languages and will take between 20 and 40 minutes. Use this link to take the survey now: https://wikimedia.qualtrics.com/SE/?SID=SV_6mTVlPf6O06r3mt&Aud=DEV&Src=DEV You can find more information about this project here[3]. This survey is hosted by a third-party service and governed by this privacy statement[4]. Please visit our frequently asked questions page to find more information about this survey[5]. If you need additional help or have questions about this survey, send an email to surveys(a)wikimedia.org. Thank you! Edward Galvez Community Engagement Wikimedia Foundation [1] This survey is primarily meant to get feedback on the Wikimedia Foundation's current work, not long-term strategy. [2]Legal information we have to share: No purchase necessary. Must be the age of majority to participate. Sponsored by the Wikimedia Foundation located at 149 New Montgomery, San Francisco, CA, USA, 94105. Ends February 16, 2017. Void where prohibited. Follow this link for the contest rules: https://meta.wikimedia.org/wiki/Community_Engagement_ Insights/2017_second_contest_rules [3] About this survey: https://meta.wikimedia.org/wiki/Community_Engagement_Insight s/About_CE_Insights [4] Privacy statement: https://wikimediafoundation.org/wiki/ Community_Engagement_Insights_2016_Survey_Privacy_Statement [5] FAQ: https://meta.wikimedia.org/wiki/Community_Engagement_Insight s/Frequently_asked_questions -- Edward Galvez Evaluation Strategist (Survey Specialist), and Affiliations Committee Liaison Learning & Evaluation Community Engagement Wikimedia Foundation

7 years, 2 months

Missing mediacounts for 2016-12-01

by Federico Leva (Nemo)

As far as I can see, mediacounts.2016-12-01.v00.tsv.bz2 is missing: http://dumps.wikimedia.your.org/other/mediacounts/daily/2016/ https://dumps.wikimedia.org/other/mediacounts/daily/2016/ Nemo

7 years, 3 months

Weigh in on whether to finalize "Amendments" section of Code of Conduct

by Matthew Flaschen

Please comment on whether to approve the "Amendments" section of the draft Code of conduct for technical spaces. The draft text is at https://www.mediawiki.org/w/index.php?title=Code_of_Conduct/Draft&oldid=238… . You can comment at https://www.mediawiki.org/wiki/Talk:Code_of_Conduct/Draft#Finalize_.22Amend… . A position and brief comment is fine. You can also send private feedback to conduct-discussion(a)wikimedia.org . Thanks again, Matt Flaschen * The "Creation and renewal of the Committee" section has been approved. Thanks to everyone who helped discuss, edit, and approve this section.

7 years, 3 months

Re: [Analytics] February 15, 2017 Research Showcase

by Sarah R

Just a reminder this will be taking place in one hour! On Tue, Feb 14, 2017 at 2:49 PM, Sarah R <srodlund(a)wikimedia.org> wrote: > Hi Everyone, > > The next Research Showcase will be live-streamed this February 15, 2017 at > 11:30 AM (PST) 18:30 UTC. > > YouTube stream: https://www.youtube.com/watch?v=m6smzMppb-I > > As usual, you can join the conversation on IRC at #wikimedia-research. > And, you can watch our past research showcases here > <https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase#February_2017> > . > > This month's presentations: > > Wikipedia and the Urban-Rural DivideBy *Isaac Johnson*Wikipedia articles > about places, OpenStreetMap features, and other forms of peer-produced > content have become critical sources of geographic knowledge for humans and > intelligent technologies. We explore the effectiveness of the peer > production model across the rural/urban divide, a divide that has been > shown to be an important factor in many online social systems. We find that > in Wikipedia (as well as OpenStreetMap), peer-produced content about rural > areas is of systematically lower quality, less likely to have been produced > by contributors who focus on the local area, and more likely to have been > generated by automated software agents (i.e. “bots”). We continue to > explore and codify the systemic challenges inherent to characterizing rural > phenomena through peer production as well as discuss potential solutions. > > > Wikipedia Navigation VectorsBy *Ellery Wulczyn > <https://www.mediawiki.org/wiki/User:Ewulczyn_(WMF)>*In this project, we > learned embeddings for Wikipedia articles and Wikidata > <https://www.wikidata.org/wiki/Wikidata:Main_Page> items by applying > Word2vec <https://en.wikipedia.org/wiki/Word2vec> models to a corpus of > reading sessions. Although Word2vec models were developed to learn word > embeddings from a corpus of sentences, they can be applied to any kind of > sequential data. The learned embeddings have the property that items with > similar neighbors in the training corpus have similar representations (as > measured by the cosine similarity > <https://en.wikipedia.org/wiki/Cosine_similarity>, for example). > Consequently, applying Wor2vec to reading sessions results in article > embeddings, where articles that tend to be read in close succession have > similar representations. Since people usually generate sequences of > semantically related articles while reading, these embeddings also capture > semantic similarity between articles. > > -- > Sarah R. Rodlund > Senior Project Coordinator-Product & Technology, Wikimedia Foundation > srodlund(a)wikimedia.org > -- Sarah R. Rodlund Senior Project Coordinator-Product & Technology, Wikimedia Foundation srodlund(a)wikimedia.org

7 years, 3 months

Readership metrics for the timespan until February 5, 2017

by Zareen Farooqui

Link to PDF of report in Commons <https://commons.wikimedia.org/wiki/File:Wikimedia_Readership_metrics_for_th…> Hi all, Here is the usual look <https://www.mediawiki.org/wiki/Reading/Readership_metrics_reports> at our most important readership metrics. This time we see an overall rise in pageviews following the seasonal winter slump, examine the recent year-over-year growth in pageviews more closely, and introduce a new day-7 retention metric for the Wikipedia iOS app. As laid out earlier <https://lists.wikimedia.org/pipermail/mobile-l/2015-September/009773.html>, the main purpose is to raise awareness about how these are developing, call out the impact of any unusual events, and facilitate thinking about core metrics in general. As always; feedback and discussion welcome. Week-over-week and month-over-month changes are being recorded on the Product page <https://www.mediawiki.org/wiki/Wikimedia_Product#Reading> at MediaWiki.org. This edition of the report covers a timespan of five weeks. You can also find lots of other traffic and usage data in the quarterly metrics presentation <https://commons.wikimedia.org/wiki/File:Wikimedia_Foundation_Reading_metric…> for Q2 2016-2017 (October - December) that was just published by the WMF Reading team. All numbers below are averages for January 2 - February 5, 2017 unless otherwise noted. Pageviews Total: 582 million/day (+10.14% from the previous report) Context (April 2015-February 2017): See also the Vital Signs dashboard <https://analytics.wikimedia.org/dashboards/vital-signs/#projects=all/metric…> After the seasonal winter slump, we see a rise in desktop pageviews, as expected. Mobile pageviews continue to remain at higher levels than before christmas. The previously mentioned iOS app’s pageview increase is still under investigation, and may turn out to be an anomaly <https://phabricator.wikimedia.org/T154735> inflating mobile pageviews by roughly 5 million views per day. This chart looks at long-term traffic trends from May 2013 - January 2017. This shows that over this timespan, the annual change in overall pageviews was -2%, desktop has been down 15%, and mobile (web + apps) has been trending upwards at a rate of 23% per year. However, the past few months have seen total pageviews increasing year-over-year (chart further below). To facilitate our understanding of which traffic movements are seasonal and which may indicate lasting changes, here is a chart overlaying the total pageview numbers back to May 2013 (the earliest time for which we have data according to the current pageview definition): Total pageviews have continued rising and are now higher than before the winter holidays. The blue line shows that the increase in overall pageviews year-over-year remains (January 2017 is up 5% from January 2016). It is possible that a smaller part of this is due to unidentified bot traffic (e.g. we just updated <https://phabricator.wikimedia.org/T157528> the pageview definition to exclude a recently discovered bot that had been causing up to 0.9% of total pageviews). But overall it is starting to look like a small but sustained rise in real human pageviews. Here we see that the changed trend in recent months can be attributed to desktop pageviews, which have mostly stopped declining year-over-year. This chart also shows that the seasonal christmas dip in pageviews comes predominantly from desktop views. Most recently, January 2017 desktop pageviews are again down 1.8% from January 2016, but that is still much less than the aforementioned -15% yearly trend since 2013. Year-over-year mobile pageviews are still increasing at a notable rate, but slower than in 2013-15. January 2017 mobile pageviews are up 13% from January 2016, less than the yearly mobile growth of 23% in the overall 2013-17 timespan. We can see how mobile pageviews always increase around the winter holidays and maintain higher levels afterwards. Desktop: 50.9% (previous report: 51.5%) Mobile web: 46.7% (previous report: 46.8%) Apps: 2.4% (previous report: 1.7%) Mobile percentage dropped a bit following the christmas spike, but remains high at an average of 49% mobile views. The previously mentioned iOS app’s pageview increase is still under investigation, and may turn out to be an anomaly <https://phabricator.wikimedia.org/T154735> inflating this metric by about 1%. Global North ratio: 77.3% of total pageviews (previous report: 76.8%) Context (January 2016-February 2017): Although Global North pageview percentage fell following the winter spike, but overall this metric grew since the last report. Levels remain in the high 70 percentile range. NB: We are currently rethinking this metric and might replace it with a different country selection constructed as part of the work on the New Readers project Unique devices See the announcement blog post <https://blog.wikimedia.org/2016/03/30/unique-devices-dataset/> from March 2016 for background and details on this metric. These estimated numbers are provided for all Wikimedia language projects (separately for the desktop and mobile web version). Because of the instrumentation method, there is no global metric for all projects and all languages, but it is currently being extended <https://phabricator.wikimedia.org/T138027> to a cross-language global metric per project at least. For now, we track the daily numbers of English Wikipedia in this report. Daily unique devices estimate for English Wikipedia: - Mobile web: 36.3 million - Desktop: 24.3 million - Total: 60.6 million - Average mobile web ratio: 60.0% Context (January 2016- February 2017): Since the last report, both daily unique desktop devices and mobile devices grew. Mobile web daily uniques mirrors the daily pageviews chart - mobile web unique levels have remained higher than before the winter bump. January 2017 was the first month we could compare year-over-year changes in unique devices. While pageviews on English Wikipedia are up from January 2016, the number of monthly unique devices is down 16%. For this report timespan (Jan 2 - Feb 5), daily mobile web uniques are up 2.6%, while daily desktop uniques are down 8.2%. This is currently being investigated. While the number of daily unique desktop devices has grown overall, the ratio of pageviews to devices on desktop has steadily declined back to pre christmas levels. The small, but notable increase in daily views per device for mobile web which happened in November has held up. New app installations Android: 18.0k/day (-5.9% from the previous report) Daily installs per device, from Google Play Context (last nine months): The number of daily installs dropped after the christmas spike, but remains higher than the number of uninstalls (except for January 26). Overall, the number of daily installs has dropped nearly 6% and daily uninstalls dropped about 8% since the last report. iOS: 5.67k/day (+6.5% from the previous report) Download numbers from App Annie Context (last two months): [image: Wikipedia iOS app daily downloads by country, Dec 6, 2016 - Feb 5, 2017 (App Annie).png] Similar to other mobile metrics measured in this report, the iOS app download metric has remained higher than usual following the winter bump. During the timespan of this report, iOS app downloads remain fairly steady besides two large, unusual spikes at the end of January in the United Arab Emirates and the United States (which follow the spike from the Netherlands in December) which are likely anomalies. We are looking into filing a bug report with App Annie to investigate this. App user retention Android: 15.6% (previous report: 16.8%) (Ratio of app installs opened again 7 days after installation for all dates that fall within this report. 1:100 sample) Context (last six months): As remarked in earlier reports, this data is a bit too noisy for drawing conclusions about whether retention changed significantly between different releases. However, we can at least rule out the existence of major shifts during this timespan. There was a small spike in day 7 retention for installs on January 22nd. iOS: 19.2% (previous report: N/A <https://phabricator.wikimedia.org/T126693> ) (Ratio of app installs opened again 7 days after installation for all dates that fall within this report. 100:100 sample since iOS app only sends data when users opt-in so there is a small user base) Context (last two months): After a long break where we refrained from reporting iOS retention here due to data quality issues with the number provided by Apple, we are happy to report iOS app 7 day retention again, based on a new EventLogging instrumentation that parallels the one on Android. There doesn’t seem to have been any significant changes in retention levels during the timespan of this report, even after new releases of the app. Looking back at December though, it appears that there was a period of lower retention which roughly corresponds with stability issues (crashes) the app was having at this time, and which have since been fixed. Unique app users Android: 1.211 million / day (+2.7% from the previous report) Context (last eight months): Android daily average levels have remained higher following the christmas and new year’s bump. This matches the mobile pageviews and unique mobile devices metrics. iOS: N/A <https://phabricator.wikimedia.org/T130432> Zareen Farooqui, Data Analyst Intern, Wikimedia Foundation Tilman Bayer, Senior Analyst, Wikimedia Foundation Data sources For reference, the queries and source links used are listed below (access is needed for each). Unless otherwise noted, all content of this report is © Wikimedia Foundation and released under the CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0/> license. Most of the above charts are available on Commons, too. <https://commons.wikimedia.org/wiki/Category:Wikimedia_readership_metrics_re…> SELECT year, month, CONCAT(year,"-",LPAD(month,2,"0")) as yearmonth, SUM(view_count) AS allhuman, sum(IF(access_method = 'desktop', view_count, null)) AS desktophuman FROM wmf.projectview_hourly WHERE year = 2017 AND agent_type = 'user' GROUP BY year, month ORDER BY year, month LIMIT 1000; SELECT year, month, day, CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) as date, sum(IF(access_method <> 'desktop', view_count, null)) AS mobileviews, SUM(view_count) AS allviews FROM wmf.projectview_hourly WHERE year = 2017 AND agent_type = 'user' GROUP BY year, month, day ORDER BY year, month, day LIMIT 1000; SELECT access_method, SUM(view_count)/(7*5) FROM wmf.projectview_hourly WHERE agent_type = 'user' AND CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) BETWEEN "2017-01-02" AND "2017-02-05" GROUP BY access_method; SELECT year, month, day, CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")), SUM(view_count) AS all, SUM(IF (FIND_IN_SET(country_code, 'AD,AL,AT,AX,BA,BE,BG,CH,CY,CZ,DE,DK,EE,ES,FI,FO,FR,FX,GB,GG,GI,GL,GR,HR,HU,IE,IL,IM,I S,IT,JE,LI,LU,LV,MC,MD,ME,MK,MT,NL,NO,PL,PT,RO,RS,RU,SE,SI,SJ,SK,SM,TR,VA,AU,CA,HK,MO, NZ,JP,SG,KR,TW,US') > 0, view_count, 0)) AS Global_North_views FROM wmf.projectview_hourly WHERE year = 2017 AND agent_type='user' GROUP BY year, month, day ORDER BY year, month, day LIMIT 1000; SELECT year, month, day, CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) as date, SUM(IF(uri_host LIKE 'en.m.wikipedia%', uniques_estimate, 0)) AS enwiki_mobile_web FROM wmf.last_access_uniques_daily WHERE year=2017 GROUP BY year, month, day ORDER BY year, month, day LIMIT 1000; SELECT year, month, day, CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) as date, SUM(IF(uri_host LIKE 'en.wikipedia%', uniques_estimate, 0)) AS enwiki_desktop FROM wmf.last_access_uniques_daily WHERE year=2017 GROUP BY year, month, day ORDER BY year, month, day LIMIT 1000; SELECT year, month, day, CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) AS date, SUM(IF(access_method = 'mobile web', view_count, null)) AS mobilewebviews, SUM(IF(access_method = 'desktop', view_count, null)) AS desktopviews FROM wmf.projectview_hourly WHERE year=2017 AND agent_type = 'user' AND project = 'en.wikipedia' GROUP BY year, month, day ORDER BY year, month, day LIMIT 1000; https://console.cloud.google.com/storage/browser/pubsite_prod_rev_028125227… https://www.appannie.com/dashboard/252257/item/324715238/downloads/ SELECT LEFT(timestamp, 8) AS date, SUM(IF(event_appInstallAgeDays = 0, 1, 0)) AS day0_active, SUM(IF(event_appInstallAgeDays = 7, 1, 0)) AS day7_active FROM log.MobileWikiAppDailyStats_12637385 WHERE timestamp LIKE '2017%' AND userAgent LIKE '%-r-%' AND userAgent NOT LIKE '%Googlebot%' GROUP BY date ORDER BY DATE; SELECT LEFT(timestamp, 8) AS date, SUM(IF(event_appInstallAgeDays = 0, 1, 0)) AS day0_active, SUM(IF(event_appInstallAgeDays = 7, 1, 0)) AS day7_active FROM log.MobileWikiAppDailyStats_12637385 WHERE userAgent LIKE '%iPhone%' OR userAgent LIKE '%iOS%' GROUP BY date ORDER BY DATE; SELECT CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) as date, unique_count AS Android_DAU FROM wmf.mobile_apps_uniques_daily WHERE year = 2017 AND platform = 'Android'; Zareen Farooqui

7 years, 3 months

February 15, 2017 Research Showcase

by Sarah R

Hi Everyone, The next Research Showcase will be live-streamed this February 15, 2017 at 11:30 AM (PST) 18:30 UTC. YouTube stream: https://www.youtube.com/watch?v=m6smzMppb-I As usual, you can join the conversation on IRC at #wikimedia-research. And, you can watch our past research showcases here <https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase#February_2017>. This month's presentations: Wikipedia and the Urban-Rural DivideBy *Isaac Johnson*Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. We explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in Wikipedia (as well as OpenStreetMap), peer-produced content about rural areas is of systematically lower quality, less likely to have been produced by contributors who focus on the local area, and more likely to have been generated by automated software agents (i.e. “bots”). We continue to explore and codify the systemic challenges inherent to characterizing rural phenomena through peer production as well as discuss potential solutions. Wikipedia Navigation VectorsBy *Ellery Wulczyn <https://www.mediawiki.org/wiki/User:Ewulczyn_(WMF)>*In this project, we learned embeddings for Wikipedia articles and Wikidata <https://www.wikidata.org/wiki/Wikidata:Main_Page> items by applying Word2vec <https://en.wikipedia.org/wiki/Word2vec> models to a corpus of reading sessions. Although Word2vec models were developed to learn word embeddings from a corpus of sentences, they can be applied to any kind of sequential data. The learned embeddings have the property that items with similar neighbors in the training corpus have similar representations (as measured by the cosine similarity <https://en.wikipedia.org/wiki/Cosine_similarity>, for example). Consequently, applying Wor2vec to reading sessions results in article embeddings, where articles that tend to be read in close succession have similar representations. Since people usually generate sequences of semantically related articles while reading, these embeddings also capture semantic similarity between articles. -- Sarah R. Rodlund Senior Project Coordinator-Product & Technology, Wikimedia Foundation srodlund(a)wikimedia.org

7 years, 3 months

Re: [Analytics] Research on wikipedia traffic and educational quality

by Erik Zachte

Dear Fabian, Let me relay your question to the WMF Analytics Team. Best regards, Erik Zachte From: Fabian Stephany [mailto:fns27@cam.ac.uk] Sent: Sunday, February 05, 2017 20:48 To: erikzachte(a)infodisiac.com; erikzachte(a)wikimedia.org Cc: Fabian Braesemann Subject: Research on wikipedia traffic and educational quality Dear Erik, please allow me to contact you regarding your work for wikipedia (https://stats.wikimedia.org/ <http://stats.grok.se/> ). My colleague and me (Oxford Internet Institute/University of Cambridge/Wittgenstein Centre Vienna) are about to start a research project on educational quality and wikipedia traffic. Hopefully, you find the time give us some advise on our questions regarding wikipedia access and edit traffic. We want to look at the category and geographical origin of wikipedia article access and editing. A) ACCESS TRAFFIC We are interested in wikipedia clicks per world-wide geographical unit (sub-national, ideally) and per category (https://en.wikipedia.org/wiki/Portal:Contents/Categories). Is there a way to stream (maybe per API) the origin of wikipedia clicks in a specific category? Instead it would already be great for us to find a statistic that shows regional access statistics on wikipedia in general over time. B) EDITING TRAFFIC As far as we have seen, it is possible to access the editing statistics (often with IP addresses given) for the last 30 days (https://en.wikipedia.org/w/index.php?namespace= <https://en.wikipedia.org/w/index.php?namespace=&tagfilter=&days=30&title=Sp ecial:RecentChanges> &tagfilter=&days=30&title=Special%3ARecentChanges). Is there likewise a tool or API to steam the editing process of wikipedia (IP and editor name, if registered)? Thank you very much for your help or likewise for suggesting somebody, who could help us out. Best wishes, Fabian Stephany, PhD MSc, MPhil Cantab fns27(a)cam.ac.uk fabian.stephany(a)wu.ac.at fabianstephany.com UK +44 776 3505 435 AT +43 680 5015 960 DE +49 176 3121 5012

7 years, 3 months

User Agents

by Utsav Sabharwal

Hi Is it possible to get logs of different User Agent (complete string) that were used to browse Wikipedia? Thanks Regards, Utsav Sabharwal

7 years, 3 months

Re: [Analytics] Figures regarding African topics coverage

by Samuel Guebo

Hello Erik, Thank you very much Erik for these valuable details you provided. I will review them thoroughly and see how they can meet my needs. Cheers, ___ Samuel GUEBO, GLAM-WIKI coordinator in Côte d'Ivoire Le 9 févr. 2017 02:13, "Erik Zachte" <ezachte(a)wikimedia.org> a écrit : (Cc'ing Analytics mailing list) Hi Samuel, I can only partially answer your questions, but maybe colleagues can add to this. *==Page views==* Not exactly what you ask, but we do publish monthly update stats on pageviews per country and per language This report shows how many page views we received in Dec 2016 per country (for any Wikipedia language) https://stats.wikimedia.org/wikimedia/squids/SquidReportPageViewsPerCountry Overview.htm There are further breakdowns, One that shows which Wikipedia language received which percentage of views from a given country, in other words which Wikipedia was most visited from Ivory Coast (French 82.4%) https://stats.wikimedia.org/wikimedia/squids/SquidReportPageViewsPerCountry Breakdown.htm One that shows per Wikipedia the distribution per country, in other words how many pageviews to say French Wikipedia came from Cote d'Ivoire (0.5%) https://stats.wikimedia.org/wikimedia/squids/SquidReportPageViewsPerLanguag eBreakdown.htm *==Edits, unique IP==* We don't yet publish edit counts of unique IP addresses per country as far as I know (because of privacy issues on small wikis). Maybe a colleague knows whether we already maintain an overall count for all African countries put together? *==Topics==* I'll think about topics coverage. We could mine the category system, but anomalous cross links make that non trivial. Cheers, Erik *From:* Samuel Guebo [mailto:samuelguebo@gmail.com] *Sent:* Monday, February 06, 2017 1:09 *To:* erikzachte(a)infodisiac.com *Cc:* Isla Haddow-Flood *Subject:* Figures regarding African topics coverage Hello Erik, My name is Samuel Guebo and I am a board member of the Wikimedia User Group of Côte d'Ivoire. I am preparing a blog post and I need recent figures regarding coverage of African topics on Wikipedia. For example, what is the estimated number of articles related to Africa? How many individuals edit with an IP coming from Africa? I already read the WikiProject Africa <https://en.wikipedia.org/wiki/Wikipedia:WikiProject_Africa> figures and also the Information Imbalance: Africa on Wikipedia <http://geography.oii.ox.ac.uk/?page=information-imbalance-africa-on-wikiped…> study but need more acurate and up-to-date figures. I Cc'ed Isla Haddow-Flood from Wikimedia South-Africa as she is both actively contributing to the WikiProject Africa and interested in these figures. Cheers, *_______________________________________________________* *Samuel Guebo * *GLAM-WIKI Coordinator in Côte d'Ivoire Creative Director at E-voir <http://e-voir.net/> * *Social entrepreneur / designer / apps developerChroniqueur culture & technologie * *(225) 77 34 17 21 <77%2034%2017%2021> / 40 27 28 37 <40%2027%2028%2037>Join me on : Facebook <http://facebook.com./samuel.guebo> / Twitter <http://twitter.com/samuelguebo> / My website <http://samuelguebo.com/>*

7 years, 3 months

Re: [Analytics] Figures regarding African topics coverage

by Erik Zachte

(Cc'ing Analytics mailing list) Hi Samuel, I can only partially answer your questions, but maybe colleagues can add to this. ==Page views== Not exactly what you ask, but we do publish monthly update stats on pageviews per country and per language This report shows how many page views we received in Dec 2016 per country (for any Wikipedia language) https://stats.wikimedia.org/wikimedia/squids/SquidReportPageViewsPerCountry… There are further breakdowns, One that shows which Wikipedia language received which percentage of views from a given country, in other words which Wikipedia was most visited from Ivory Coast (French 82.4%) https://stats.wikimedia.org/wikimedia/squids/SquidReportPageViewsPerCountry… One that shows per Wikipedia the distribution per country, in other words how many pageviews to say French Wikipedia came from Cote d'Ivoire (0.5%) https://stats.wikimedia.org/wikimedia/squids/SquidReportPageViewsPerLanguag… ==Edits, unique IP== We don't yet publish edit counts of unique IP addresses per country as far as I know (because of privacy issues on small wikis). Maybe a colleague knows whether we already maintain an overall count for all African countries put together? ==Topics== I'll think about topics coverage. We could mine the category system, but anomalous cross links make that non trivial. Cheers, Erik From: Samuel Guebo [mailto:samuelguebo@gmail.com] Sent: Monday, February 06, 2017 1:09 To: erikzachte(a)infodisiac.com Cc: Isla Haddow-Flood Subject: Figures regarding African topics coverage Hello Erik, My name is Samuel Guebo and I am a board member of the Wikimedia User Group of Côte d'Ivoire. I am preparing a blog post and I need recent figures regarding coverage of African topics on Wikipedia. For example, what is the estimated number of articles related to Africa? How many individuals edit with an IP coming from Africa? I already read the WikiProject Africa <https://en.wikipedia.org/wiki/Wikipedia:WikiProject_Africa> figures and also the Information Imbalance: Africa on Wikipedia <http://geography.oii.ox.ac.uk/?page=information-imbalance-africa-on-wikiped…> study but need more acurate and up-to-date figures. I Cc'ed Isla Haddow-Flood from Wikimedia South-Africa as she is both actively contributing to the WikiProject Africa and interested in these figures. Cheers, _______________________________________________________ Samuel Guebo GLAM-WIKI Coordinator in Côte d'Ivoire Creative Director at <http://e-voir.net/> E-voir Social entrepreneur / designer / apps developer Chroniqueur culture & technologie (225) 77 34 17 21 / 40 27 28 37 Join me on : <http://facebook.com./samuel.guebo> Facebook / <http://twitter.com/samuelguebo> Twitter / <http://samuelguebo.com/> My website

7 years, 3 months

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Analytics February 2017