The main purpose of this workshop is to explore issues of evaluation methodology, effectiveness metrics and other processes related to early risk detection. Early detection technologies can be employed in different areas, particularly those related to health and safety. For instance, early alerts could be sent when a predator starts interacting with a child for sexual purposes, or when a potential offender starts publishing antisocial threats on a blog, forum or social network. Our main goal is to pioneer a new interdisciplinary research area that would be potentially applicable to a wide variety of situations and to many different personal profiles. Examples include potential paedophiles, stalkers, individuals that could fall into the hands of criminal organisations, people with suicidal inclinations, or people susceptible to depression.
This is the first year that this workshop runs and there are two possible ways to participate:
The workshop is open to the submission of papers describing test collections or data sets suitable for early risk prediction, early risk prediction challenges, tasks and evaluation metrics or specific early risk detection solutions. We understand that there are two main classes of early risk prediction:
The two classes of risks described above might interact one to each other. For instance, individuals suffering from major depression might be more inclined to fall prey to criminal networks. From a technological perspective, different types of tools are likely needed to develop early warning systems for these two types of risks.
Essentially, we look at early risk prediction as a process of sequential evidence accumulation where alerts are made when there is enough evidence about a certain type of risk. For the single actor type of risk, the pieces of evidence could come from the chronological sequence of entries written by a tormented subject in the Social Media. For the multiple actor type of risk, the pieces of evidence could come from a series of messages interchanged by an offender and a victim in a chatroom or online forum.
Notice that we refer to early risk in a general way and the workshop is open to contributions along these lines in any possible application domain.
Important dates (Research Papers):
Submission instructions: We solicit papers up to 12 pages in length. The papers must be written in English and should follow the Springer-Verlag LNCS style. For details see the Springer LNCS Author Instructions. Papers (PDF) must be submitted through EasyChair:(eRisk Workshop Papers). The proceedings of this workshop will be published in the online CEUR-WS Proceedings and on the conference website.
The second way of participation consists in performing a pilot task on early risk detection of depression. This is an exploratory task on early risk detection of depression. The challenge consists of sequentially processing pieces of evidence and detect early traces of depression as soon as possible. The task is mainly concerned about evaluating Text Mining solutions and, thus, it concentrates on texts written in Social Media. Texts should be processed in the order they were created. In this way, systems that effectively perform this task could be applied to sequentially monitor user interactions in blogs, social networks, or other types of online media.
The test collection for this pilot task is the collection described in [Losada & Crestani 2016]. It is a collection of writings (posts or comments) from a set of Social Media users. There are two categories of users, depressed and non- depressed, and, for each user, the collection contains a sequence of writings (in chronological order). For each user, his collection of writings has been divided into 10 chunks. The first chunk contains the oldest 10% of the messages, the second chunk contains the second oldest 10%, and so forth.
The task is organized into two different stages:
Evaluation: The evaluation will take into account not only the correctness of the system's output (i.e. whether or not the user is depressed) but also the delay taken to emit its decision. To meet this aim, we will consider the ERDE metric proposed in [Losada & Crestani 2016].
Being a pilot task, we expect it to be useful for instigating discussion on how to create evaluation laboratories for early risk prediction: proper size of the data, adequate early risk evaluation metrics, alternative ways to formulate early detection tasks, other possible application domains, etc.
Nov 30th, 2016: The training data has been released!. More info: here
University of Texas at Austin, USA
Maastricht University, The Netherlands
University of Birmingham, UK