Wikimedia Apps/Suggested edits/mk

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Предложените уредувања се нов начин да ја уредувате Википедија од Андроид. Би ценеле ако го пробате.

Што се тоа Предложени уредувања?
Suggested edits presents opportunities for small but vital contributions to Wikipedia. We would like to raise awareness that everyone can edit Wikipedia and make contributing easier and more accessible for everyone.

Get started with Suggested edits
The Suggested edits home page consists of the following areas: Profile statistics/contribution history and contribution opportunities. Contribution opportunities are the central element of Suggested edits. Here you can find ways to contribute to Wikipedia. At the moment we offer tasks to add or translate article descriptions and image captions. If you want to know more or learn how to edit like a pro, check out the dedicated sections below:



We are going to add more contribution types soon, stay tuned.

Profile statistics display information about your activity within Suggested edits. They start to populate once you've made your first Suggested edits.

Tapping the card leads to the contribution history page. The contribution history lists all contribution types that are available in Suggested edits. You can filter by edit type and see how many pageviews the articles you’ve contributed to had in the past 30 days. Tapping an item in the contribution history list leads you the edit detail (diff) page, where even more infos about the particular edit are featured.
 * Contributions - displays the number of contributions you’ve made with Suggested edits in the past 30 days.
 * Pageviews - displays the total number of times in the last 30 days that others viewed items you contributed to using Suggested edits.
 * Edit streak - displays how many days without a break you’ve contributed via Suggested edits. If you haven’t contributed in a while, it shows your last contribution date.
 * Edit quality - based on how many times one of your contributions was reverted (that is: undone by another editor). The fewer reverted contributions, the better the edit quality.

What are article descriptions?

 * Short, multilingual descriptions of items (e.g. Wikipedia articles)
 * Article descriptions are not full sentences, but small bits of information.
 * In most cases, the proper length is between two and twelve words.

What are article descriptions used for?
Article descriptions are shown in the apps below Wikipedia article titles to help readers identify the article they're looking for. Article descriptions are stored and maintained on Wikidata and have been designed to disambiguate items with the same or similar labels. Descriptions are also shown outside the Wikipedia site and apps: for example, in Google searches.

Article descriptions summarize an article to help readers understand the subject at a glance. These are known in the Wikimedia community as Wikidata descriptions.

Tips for creating article descriptions
Article descriptions should ideally fit on one line, and be between two to twelve words long. They are capitalized, and do not normally begin with initial articles (a, an, the). For example:


 * Painting by Leonardo da Vinci (title description for an article about the Mona Lisa)
 * Earth’s highest mountain (title description for an article about Mount Everest)

Other tips for writing good article descriptions:

(e.g., ‘current Prime Minister of…’) (e.g., ‘the best…’)
 * Avoid information that is likely to change
 * Avoid opinionated, biased or promotional wording
 * Avoid controversial claims

More information is available on the Wikidata descriptions help page.

More on article descriptions
Article descriptions are stored and maintained on Wikidata, a project of the Wikimedia Foundation which provides a free, collaborative, multilingual, secondary database supporting Wikipedia and other projects.


 * About Wikidata
 * Wikidata help page about article descriptions
 * Guidelines for writing article descriptions in English
 * Guidelines for writing short descriptions on English Wikipedia

What are image captions?

 * Short, multilingual descriptions of image files
 * Limited to 250 characters in length and cannot contain markup

What are image captions used for?
Image captions describe an image to help readers understand the meaning and context of an image. They are also used to provide alternative information for an image if people cannot view it, e.g. because of a slow internet connection or if people use a screen reader.

Tips for creating image captions
Image captions should be short descriptions of what the image shows. They are generally four to twelve words long. They can also contain information about the artist or creator of the image.

Remember to keep your captions neutral. Avoid making value judgements such as "beautiful", "good" or "ugly".

The difference between the image description and the image caption is that the description can have a lot of information about the file. If the image is a scan it can have details about the original photograph or artwork. It can have links. The image caption should ignore all this and just describe what it shows, not the information about the file.

A good image caption should:


 * Aim to briefly describe the contents of an image
 * Describe the image such that those with vision or other impairments can imagine what it looks like
 * Contain some keywords that people are likely to use to search for an image (so a picture of a cat should include the word "cat" somewhere in the caption)

In many cases, the caption will be similar or identical to the description (or even the file title!). For example, File:Fire station Hallstatt - October 2017 - 02.jpg has the English caption "Hallstatt fire station in October 2017".

More on image captions
Image captions are stored and maintained on Wikimedia Commons, a project of the Wikimedia Foundation which provides an online repository of free-use images, sounds, other media, and JSON files supporting Wikipedia and other projects.

Image tags

 *  See also: Commons:Depicts 

What are image tags?
"Image tags" is a shorthand phrase for what the Commons community calls Depicts statements.

What are image tags used for?
By adding image tags, you will help make images easier to search for on Commons, the free license image repository that Wikipedia uses for images in its articles.

Tips for adding image tags
Please add tags conservatively. If there are multiple items clearly and deliberately depicted by the image, all should be added as separate tags, within reason. For example, should be tagged with "Bonnie and Clyde", "Bonnie Parker", and "Clyde Barrow". Identify the most important thing(s) in the image.

Be as specific as you can. Search for the most relevant tags by tapping "+ Add tag". You may see a number of search results, but resist the urge to add a large number of semi-relevant tags. In the example shown below, the picture is of the Williamsburg Bridge, but that tag has not been added. Add it yourself by tapping "+ Add tag" and searching for "Williamsburg Bridge".

Where is the train algorithm task?
Due to the train image algorithm task serving as a MVP for the Android team to learn from and build the full image matching feature, the train image algorithm MVP has been sunset. With the help of Wikipedians like you, we have improved the algorithm and have enough learnings to proceed with the next phase of our work to build the full image matching feature. Our lessons from the experiment will be available on the Add an Image project page. You can watch participate in the next phase of this work at Growth team's project page.

What is the train algorithm task?
Wikipedia articles are written and edited by thousands of volunteers from around the world. Unfortunately, many articles lack images. The Train Algorithm task is a type of Suggested Edits task that will show logged-in Android users articles and images along with its associated information, so that users can determine if the image is a good illustration of the contents of the article displayed.

The images will be suggested to you using an algorithm. The algorithm will pull images from other sources and suggest a match with an article that does not have an image.

Unlike other Suggested Edits tasks, the Train Algorithm task will not save any edits to any Wiki projects and is a temporary task. The purpose of the Train Algorithm task is to gather data, improve our image matching algorithm, and inform our design for future releases of an image matching task on Android and Mobile Web.



Tips for training the image algorithm
To best determine if an image is the right match for an article you should review:
 * Image (zoom in to review more details)
 * Image file name
 * Image description
 * Image suggestion reason
 * Article content

Dialog options
In the task you can select Yes, No or Not Sure, to the question of if you would add the image to an article.


 * Yes indicates the image is a good illustration to help readers understand the topic of the Wikipedia article
 * No indicates the image would not help readers understand the topic of the Wikipedia article. Reasons the image would not help be a good fit for the article includes:
 * Not Relevant, which indicates the image depicts a topic that is not associated with the article that is being shown
 * Not enough Information, which indicates the metadata shared with the image does not provide enough details for you to confidently indicate the image would help readers better understand the article
 * Offensive, which indicates the image is inappropriate
 * Low Quality, which indicates you can not see the image well enough to confidently say it is a good illustration of the article being shown and help readers understand the topic
 * Don't know this subject, which indicates you do not feel you have the needed expertise to determine if the image would help readers understand the topic of the Wikipedia article
 * Cannot read the language, which indicates the words written in the image, or the metadata is in a language you do not understand, and are then unable to confidently say the image would help readers understand the topic of the Wikipedia article
 * Other, which indicates there is another reason the image is not a good match for the article
 * Not Sure indicates you are not certain whether or not the image would help readers understand the topic of the Wikipedia article. Reasons for not being sure could include:
 * Not enough Information, which indicates the metadata shared with the image does not provide enough details for you to confidently indicate the image would help readers better understand the article
 * Cannot see image, which indicates you can not see the image well enough to confidently say it is a good illustration of the article being shown and help readers understand the topic
 * Don't know this subject, which indicates you do not feel you have the needed expertise to determine if the image would help readers understand the topic of the Wikipedia article
 * Don't understand the task, which indicates you do not understand the Train Image Algorithm task
 * Cannot read the language, which indicates the words written in the image, or the metadata is in a language you do not understand, and are then unable to confidently say the image would help readers understand the topic of the Wikipedia article
 * Other, which indicates there is another reason you are not sure if the image would be a good match for the article

Daily goal
The 'Train image algorithm' task consists of a daily goal. To reach the daily goal, you need to evaluate 10 image suggestions. The goal resets to 0 on the next day. You can always evaluate more than 10 image suggestions per day, this should just serve as an indicator of your daily progress.

When will you roll out the full image matching task?
The Android and Growth teams will collaboratively evaluate the outcomes of this MVP, and determine next steps for a task that places images in articles in July 2021. During that time, the teams will update the MVP project page with our findings and share when we will offer the image matching task.

How can I follow the outcome of this MVP when it ends?
You can watch our Add an Image MVP project page for updates about this effort. We would also value your feedback on our talk page.