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Total Mobile Text Contributors to en-Wikipedia

Formerly Active

Currently Active (5 or more edits/month)

Potentially Active

Have contributed

Are occasionally contributing

Have never contributed

Communicating with Foundation

Not communicating with Foundation

English Wikipedia

Actively Contributing Elsewhere

Not actively contributing elsewhere

AND

We know a lot about this group

Individual / Institutional force multipliers for Text Contributors: Knowledge Stewards

Individual obstacles for Text Contributors: Vandals

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Total Text Contributors to en-Wikipedia

Formerly Active

Currently Active (5 or more edits/month)

Potentially Active

Have contributed

Are occasionally contributing

Have never contributed

Communicating with Foundation

Not communicating with Foundation

English Wikipedia

Actively Contributing Elsewhere

Not actively contributing elsewhere

AND

We know a lot about this group

29,579 (OCT 16)

Individual / Institutional force multipliers for Text Contributors: Knowledge Stewards

Individual obstacles for Text Contributors: Vandals

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GOAL: To have more active editors involved in decision-making processes and to learn more about their needs.
AUDIENCE: Text Contributors							STATUS: ACTIVE
Sub-Audience: Not communicating with Foundation
Importance of studying this group: Will lead to a better understanding of behavior and motivation of active editors, communication tools, and would help us make product decisions based on input from an active community we serve but are not currently reaching. Questions we have about this group: Where do they live? Do they have preferred communication methods? Are they aware of different ways to become more involved? What platforms do they currently edit on? What motivates them to go from passive user to active user? Does that differ by country? What gets existing “active editors” hooked on Wikipedia? What are the reasons they don’t participate on listservs? IRC? At events? Are there ways we could serve this population that we’re not currently serving? Are they similar or dissimilar to the active editor pool we know about? What motivates them? What are their current behaviors on-site? Who would be force multipliers for this population? Are they more likely to edit for a longer/shorter time than actively communicating editors? How do we reward them?

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Audience: Text Contributors						STATUS: ACTIVE
Sub-Audience: Not communicating with Foundation
What we know about this group (internal) We currently split editors into very active editors (100+/month), active editors (5+/month) , and contributors (1+/month). There is existing research on active editors (defined as people who contribute to Wikipedia 5 or more times in a month) based on surveys and other research methods, but the population of editors that we’ve been able to reach has been relatively small compared to the active editor pool. (An estimate from Editing indicates that our most effective outreach campaign reached 4.6% of active editors during the last Board election. This was 3x greater than any other campaign to reach active editors.) Related research: How Wikipedians Contribute to the Encyclopedia over Time (Research, 2011) Editor Trends Study (Research, 2010) The Rise and Decline of an Open Collaboration Community (Aaron Halfaker, 2013)

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Audience: Text Contributors						STATUS: ACTIVE
Sub-Audience: Not communicating with Foundation
What we know about this group (external) Active text contributors contribute the most to: English, German, Japanese, Spanish, and Russian Wikipedia. (wikistats)

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Audience: Text Contributors						STATUS: ACTIVE
Sub-Audience: Not communicating with Foundation
What are ways we can segment this group? Quantitative By country - we don’t currently segment text editors by country, though this is something the CE team has requested in the past * By device - could we use this as a proxy for countries? Qualitative: By motivation By barriers to access *Emails out to Deb, James, and Neil/Trevor about the following questions: 1. How many active editors do we have on each mobile platform? 2. How many very active editors do we have on each mobile platform? 3. What mobile platforms have had the greatest editor growth in the past 3 years? 4. What mobile platforms have had the most editors (total)? 5. Where in the world are the most mobile editors? 6. On what platforms are the most contributors who have contributed 1-2 times and then stopped?

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Audience: Text Contributors						STATUS: ACTIVE
Sub-Audience: Not communicating with Foundation
Resources we would need to learn more about this group Research: Surface existing research on this group. Identify qualitative and quantitative methods we use to learn more about this group. Analyst: Design methods we can use to identify “active editors” who we are not in touch with. Conduct analysis to identify distinguishing characteristics of this group. Reboot or third party: Interviews with active text editors who we’re not reaching through current communication channels. Market segmentation on contributors and behavior. Learning more about this group would help us answer: How we can learn more about & reach more active editors Whether active editors know about programs, grant opportunities and events Whether our current communication methods are effective methods in reaching active editors Whether we can develop methods for onboarding more active editors



POTENTIAL IMPACT:

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Total Text Contributors to en-Wikipedia

Formerly Active

Currently Active (5 or more edits/month)

Potentially Active

Have contributed

Are occasionally contributing

Have never contributed

Communicating with Foundation

Not communicating with Foundation

English Wikipedia

Actively Contributing Elsewhere

Not actively contributing elsewhere

AND

We know a lot about this group

29,579 (OCT 16)

Individual / Institutional force multipliers for Text Contributors: Knowledge Stewards

Individual obstacles for Text Contributors: Vandals

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GOAL: To learn more about editor retention and methods for retaining editors
AUDIENCE: Text Contributors							STATUS: IN-ACTIVE
Sub-Audience: Formerly active
Importance of studying this group: Questions we have about this group: Where do they live? What platforms are they predominantly on? What were the reasons that they left? Social, technological, harassment, other? Do they ever return? Is the rate of leaving similar or different to other open source projects on the Internet? Are they contributing to the project in other ways (donations, influencers, content maintainers, other)? Would they come back if they could contribute in a different way? Would different editing metrics help us better understand this group? What is their average length of time contributing to the project? If they’re actively contributing elsewhere? Where? How often? What motivates them to contribute? What differentiates it from Wikipedia?

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Audience: Text Contributors						STATUS: IN-ACTIVE
Sub-Audience: Formerly active
What we know about this group (internal) There are 29,800,000~ registered users. (source) There are 127,176 active users in the last 30 days. .42 percent of people who have registered accounts have contributed in the last 30 days. Caveat: This does not include IP edits. Related research: How Wikipedians Contribute to the Encyclopedia over Time (Research, 2011) Editor Trends Study (Research, 2010) The Rise and Decline of an Open Collaboration Community (Aaron Halfaker, 2013) Editor Engagement (2011) What Drives People to Contribute to Wikipedia Editing Dept. Quarterly Review 2015/2016 Q4 July Metrics 2016 2015 Strategy documentation around content contributors - what we don’t know about contributors Editor datasets Is editing more rewarding than discussion?

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Audience: Text Contributors						STATUS: IN-ACTIVE
Sub-Audience: Formerly active
What we know about this group (external) What incentivizes people to contribute to online communities

Using Social Psychology to Motivate Contributions to Online Behavior

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Audience: Text Contributors						STATUS: IN-ACTIVE
Sub-Audience: Formerly active
What are ways we can segment this group? Quantitative (through surveys?) Education Level, Age, Self-efficacy, scales of uses and gratifications and organizational committment Qualitative: Motivation Reasons for editing Reasons for leaving

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Audience: Text Contributors						STATUS: IN-ACTIVE
Sub-Audience: Formerly active
Resources we would need to learn more about this group Research: Surface existing research on this group. Identify qualitative and quantitative methods we use to learn more about this group. Analyst: Design methods we can use to identify “active editors” who are not currently active. Conduct analysis to identify distinguishing characteristics of this group. Reboot or third party: Interviews with formerly active text editors. Market segmentation on former contributors, motivations and behavior. Learning more about this group would help us answer: What barriers exist to retain active editors How to develop better onboarding experiences and retain more active editors More about contributor needs and motivations Are former contributors willing to give in other ways? Microtasks? Donation? Help a friend learn to use the platform?



POTENTIAL IMPACT:

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Total Text Contributors to en-Wikipedia

Formerly Active

Currently Active (5 or more edits/month)

Potentially Active

Have contributed

Are occasionally contributing

Have never contributed

Communicating with Foundation

Not communicating with Foundation

English Wikipedia

Actively Contributing Elsewhere

Not actively contributing elsewhere

AND

We know a lot about this group

29,579 (OCT 16)

Individual / Institutional force multipliers for Text Contributors: Knowledge Stewards

Individual obstacles for Text Contributors: Vandals

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GOAL: To learn more about editor retention and methods for retaining editors
AUDIENCE: Text Contributors							STATUS: IN-ACTIVE
Sub-Audience: Potentially Active
Importance of studying this group: Questions we have about this group: What kinds of people are likely to contribute? Do they have to be readers first? Do they know about Wikipedia? If yes, do they know it’s a non-profit? What motivates them? What devices do they use? What motivates them? Is the reader to editor pathway correct? If not, what is a better mental model? Are there certain types of behaviors on-site that lend themselves to potential editors? If they’re actively contributing elsewhere? Where? How often? What motivates them to contribute? What differentiates it from Wikipedia?

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Audience: Text Contributors						STATUS: IN-ACTIVE
Sub-Audience: Potentially Active
What we know about this group (internal) Related research: Geotargetted editor participation (2013) Wikipedia page views/country Editor growth and contribution program

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Audience: Text Contributors						STATUS: IN-ACTIVE
Sub-Audience: Potentially Active
What we know about this group (external) How can we measure for these qualities in audience members? What incentivizes people to contribute to online communities

Using Social Psychology to Motivate Contributions to Online Behavior

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Audience: Text Contributors						STATUS: IN-ACTIVE
Sub-Audience: Potentially Active
What are ways we can segment this group? Quantitative (through surveys?) Education Level, Age, Self-efficacy, scales of uses and gratifications and organizational committment Qualitative: Motivation Barriers to entry

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Audience: Text Contributors						STATUS: IN-ACTIVE
Sub-Audience: Potentially Active
Resources we would need to learn more about this group Research: Surface existing research on this group. Identify qualitative and quantitative methods we use to learn more about this group. Analyst: Design methods we can use to identify “potential editors” who are not currently active. Conduct analysis to identify distinguishing characteristics of this group. Reboot or third party: Interviews with potential editors. Market segmentation on contributors, motivations and behavior. Learning more about this group would help us answer: What are the most effective intervention methods? Are there behaviors on-site that lead someone into the editing pathway? How do we target groups within this group? (students, parents, teachers, retirees?)



POTENTIAL IMPACT:

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We have to start somewhere.

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Possible way to prioritize text editor audience(s)

Top countries that have Internet Penetration > 50%, Smartphone usage 18-34 > 50%, and were not part of the New Readers initial research shortlist. Highlighted top 10 in terms of pageviews. (source with other metrics we can use to select countries for generative research)

United States
Japan
Russia
Germany
United Kingdom
France
South Korea (no affiliate)
Italy
Spain
Canada
Netherlands
Argentina
Poland
Malaysia (no affiliate)
Australia

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Limitations with this method

Our initial list of current and potential audiences was based on available research and interviews with stakeholders across the Foundation, and may not encompass the full breadth of audiences we currently serve or could serve. There is likely some subjective element to this work as well: as one stakeholder said, “We could spend months thinking about how to select the audience(s) for further generative research, and part of me wants to say ‘Just pick some. Any research on any of these groups will lead us to a better place.’”

We also based our selection on acknowledging the current work being done by the Foundation concerning New Readers. In selecting areas for further study, we chose to not overlap with existing work.

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Ways we could strengthen our methods

Hire a market segmentation firm (or purchase available data) to discover more granular segments that would help us pinpoint segments within countries or geographic regions

Forrester’s Consumer Technographics on behavior, motivation, and usage online
Global Web Index Audience Builder
Nielsen Audience Segments

Partner with like-minded organizations within our ecosystem to share data and create personas

Mozilla’s Behavioral Segmentation Study
In the UK, the Audience Spectrum segments the whole UK population by their attitudes towards culture, and by what they like to see and do. There are 10 different Audience Spectrum profiles that you can use to understand who lives in your local area, what your current audiences are like, and what you could do to build new ones. Audience Spectrum is the most accurate tool the sector has ever had to help target audiences, and include a wider public.

Partner with an internal analyst to place our own data on top of publicly available or purchased data.