Casual uploading has been tried before both for mobile and through the cross-uploader in VE, and caused outrage from the Commons community each and every time. Have you reviewed the previous discussions? What would your implementation bring to the table that hasn't been tried before? One idea that could be tested would be not to allow the user to upload a previous picture, but only an image taken by the app accessing the camera, but that would not really solve copyright issues in non-FoP friendly countries..
Topic on Talk:Reading/Readers contributions via Android
Hi @Strainu . Thank you! We are familiar with the fact that there has been outrage about content and copywrite violations in the past, but I have not been able to find the specific discussions. You seem to have a strong grasp, however. Can you send links or convey your understanding of the specifics.
You can start with phab:T120867 and related bugs and read the links given there. Not all of them will be relevant, of course, but you will most likely get a good picture of previous attempts and why they are considered as failed by the Wikimedia Community.
Thanks!!
One of the best ways to obtain new uploads for articles would be based on wikidata. For example, if someone reads an article about a Tree in Brazil that doesn't have an image, the interface itself could urge the reader to help out by sharing an image of the tree if they are close to it (geoip).
One way to reduce the copyright issues is for example to allow even anonymous editors to suggest images from public domain / free resources for a particular article, e.g. pixabay, flicker, etc.
Instead of immediately adding them to an article, they'd be placed in a queue that editors can choose to use, if deemed useful.
@197.218.88.3 - Interesting idea. We hadn't thought of connecting to other public domain or other free resource API's as a tool for pulling in individual images. Something to look into for sure.
Hey there, the ability to look for a more suitable image from Wikimedia Commons rather than uploading from the user's own gallery is something we have incorporated in the mocks as an option (see wireframe: https://wikimedia.invisionapp.com/share/8H9YRVDZF) but we could probably flesh out this use case further given it is potentially as/more likely that an image may exist already in Commons for an article.
@197.218.88.3 – agree with your point as well regarding Moderation. The proposal for both the image and audio contribution ideas is to indeed add a 'moderation queue' to the mobile app first for acceptance/rejection rather than allowing immediate contribution. I've updated the page to show the specific wireframes detailing how the queue could work.
There is one interesting finding of this academic study of video uploads :
It shows that media popularity is a good signal to detect copyright infringement. Illegal media, or even just a basic movie, music will likely be shared somewhere, and if it becomes popular there will be an an odd surge of views and downloads.
Currently contributors have no way to even filter any related to media (e.g. movies, images or audio) metrics that will facilitate detection of copyright. So one possibility is simply to surface and prioritize such popular media, so readers and editors can quickly help evaluate it. Wikimedia seems to have this data, yet it is not surfaced anywhere.
Exposing such data (e.g. through an API or special page) will help both even desktop editors prioritize their efforts. In this specific case it would help prioritize audio for review because there is a reasonable chance that a sudden surge of popular audio (downloaded by users) is related to some copyrighted music.
The link doesn't work, but its a cool idea for videos and music--it probably doesn't apply to the random image that happens to have a coca-cola logo in the background. I wonder @Halfak (WMF) and his team of learned machines would be interested in tackling this?
That's odd, it can still be downloaded from here. The study looked into various indicators of copyright including the video metadata and search results. It is certainly an interesting study on automatically detecting video copyright violations:
Swati Agrawal and Ashish Sureka. 2013. Copyright Infringement Detection of Music Videos on YouTube by Mining Video and Uploader Meta-data. In Proceedings of the Second International Conference on Big Data Analytics - Volume 8302 (BDA 2013), Vasudha Bhatnagar and Srinath Srinivasa (Eds.), Vol. 8302. Springer-Verlag New York, Inc., New York, NY, USA, 48-67. DOI=http://dx.doi.org/10.1007/978-3-319-03689-2_4
https://link.springer.com/chapter/10.1007/978-3-319-03689-2_4
https://www.iiitd.edu.in/~ashish/SWATI-MTECH-THESIS.pdf
This can also apply to images though. If someone shares a copyrighted image of maybe some nude famous individual and shares this somewhere it may quickly increase views. It can even be used to fight more sophisticated copyright infringers who embed computer games or other content inside images or other seemingly unpopular media (see T12847).
Some practical uses include detecting seemingly innocuous files that were uploaded ages ago, and then are suddenly downloaded at a very high rate, a new filtering tool to facilitate editor moderation (on the desktop), and scoring search results.
Specifically, for reader contributions this would fit in rather well by prioritizing some images or audio with very high views in the moderation queue, instead of adding all files to the queue randomly or by upload date.
There are many practical uses of this data.