[OSM-dev] OSM Project Idea for GSoC 17: Vandalism Detection in Map Edits

Jason Remillard remillard.jason at gmail.com
Tue Dec 20 14:27:18 UTC 2016


There is obviously plenty of data that represents "good" changes. The
data working group reversions could be used to train a classifier on
what a bad edit looks like. After that, looking for change sets that
are logically erased after a short period of time (say 2 weeks), might
also yield some bad change set.


On Sun, Dec 18, 2016 at 6:38 PM, Animesh Sinha
<sinha.animesh34 at gmail.com> wrote:
> Hi,
> I am a first year masters students at Purdue University and would like to
> propose a project idea for GSoC 2017. I have worked on Vandalism Detection
> in Wikipedia in the past and understand how important it is to predict if an
> information is correct or not as it may be misleading to others.
> Hence, I would like to propose this project idea:
> Title: Detect if a user edit made in OSM is a vandal edit or regular.
> Summary: It's a very challenging task to monitor the malicious edits or
> spams manually for a large active user base. I plan to identify the cases of
> vandalism on OSM by classifying edits as either regular or vandal. This is
> clearly a Binary Classification task, but if the distribution of regular and
> vandalism cases in the dataset are skewed, it can also be explored as an
> Anomaly Detection problem.
> Requirements: Lots of data about the edits made, information about the users
> making the edit, information about the people annotating the true labels,
> etc.
> I would appreciate if someone can provide a feedback on the project idea and
> the requirements needed.
> Thanks,
> Animesh Sinha
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