Skip to content
Home » Shared Task 2022

Shared Task 2022

The Workshop on Computational Linguistics and Clinical Psychology

CLPsych 2022 will be held in conjunction with NAACL 2022

July 15th 2022

CLPsych 2022 Shared Task

All participants to sign the following documents and to send them to NORC:

NORC Non-disclosure

NORC DUA

Capturing changes in mood over time

Increasingly the clinical community are looking for new and better diagnostic measures and mental health condition monitoring tools. Over the past decade, there has been a surge in methods at the intersection of NLP and mental health, showing that signals for the diagnosis of certain conditions can be found in language. However, most research tasks have been defined on the basis of classifying individuals (e.g., on the basis of suicide risk [1] or on the basis of having a mental health condition or not [2]), thus lacking the longitudinal component of monitoring an individual’s mood and well-being in real-time.

The CLPsych 2022 Shared Task

We introduce the problem of assessing changes in a person’s mood over time on the basis of their linguistic content. For the purpose of the task we focus on posting activity in online social media platforms. In particular, given a user’s posts over a certain period in time, we aim: (1) at capturing those sub-periods during which a user’s mood deviates from their baseline mood – a post-level sequential classification task. We then build on this task, by leveraging it to further help us assess: (2) the risk level the user is at – a user-level classification task [1] & a continuation of the 2019 Shared Task [3]. Thus, the task consists of the two subtasks: (1) the main task of identifying mood changes in users’ posts over time and (2) the auxiliary task of showing how (1) helps us assess the risk level of a user.

Data & processing environment

Social media data annotated for the purposes of the above tasks will be made available in a secure environment and all processing and model development will happen in the secure environment. This is because the data pertains to vulnerable individuals and we endeavour to protect them as much as possible. Task participants will be allocated credits in the secure environment and will be provided with the necessary development tools and libraries they request upon registering interest. Finally task participants will need to sign data use agreements and abide by ethical practice. In addition to the shared task, participants will be invited (and strongly encouraged) to participate in an online Hackathon scheduled for March 21-22. The aim of the Hackathon will be to try out the secure environment with a subset of the data prior to the shared task and permit some live interaction with the organising team in terms of their experience around the task. The organising team will also be providing baselines for the primary task (1) based on existing work [4].

Timeline
  • 17 Feb – Call goes out inviting Expression of Interest
  • 28 Feb – Deadline for teams to register interest (link)
  • 7 Mar – Team selection announcement. 
  • 24 & 25 Mar – Hackathon- subset of training data available
  • 1 Apr – Availability of training data
  • 2 May – Availability of test data
  • 10 May 12 May – System submissions due
  • 13 May 16 May – Results announced
  • 17 May 19 May – System description papers due
  • 22 May – Acceptance notification
  • 26 May – Camera ready 
Quick Links

Invitation to Participate – Expression of Interest: Link
Email Organizers: clpsych22-shared-task-organizers@googlegroups.com

Organizers
Adam Tsakalidis (Queen Mary University of London & The Alan Turing Institute)
Federico Nanni (The Alan Turing Institute)
Maria Liakata  (Queen Mary University of London & The Alan Turing Institute)

You can find information for last year’s shared task (CLPsych 2021) here, and papers and presentations from the workshop here.

A Big Thank You to Our Sponsors!

Shared Task Sponsor

Follow us on Twitter

Yaara Shriki and colleagues in “Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis” fine-tune a language model and mask nouns, verbs and some other morphosyntactic categories to detect transcribed speech of patients diagnosed with schizophrenia

Daeun Lee et al. developed the suicidality detection model C-GraphSAGE based on a graph neural network to grasp the dynamic semantic information of social media post by using the suicide vocabulary efficiently.

Check out shared task paper “Multi-Task Learning to Capture Changes in Mood Over Time”. The authors investigate the impact of using MTL to predict mood changes over time of social media users –From: uOttawa-AI

To what extent do people who engage in non-suicidal self-injury conceptualize their behavior as an addiction? Using #reddit data we identify shared language across #NSSI and #SubstanceUse Reddits, showing borrowed #recovery language when describing self-harm

Load More…