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Time-Aware Predictions of Moments of Change in Longitudinal User Posts on Social Media

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Advanced Analytics and Learning on Temporal Data (AALTD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14343))

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Abstract

Capturing changes in an individual’s language is an important aspect of personalised mental health monitoring. A key component is modelling the influence of time, as contextual information both in the recent or distant past/future carries varying semantic weight. We capture and contrast this information by identifying neural, time-sensitive, bi-directional representations of individuals – modelling time-intervals in their social-media posts inspired by the Hawkes process. We demonstrate that our approach helps identify whether an individual’s mood is changing drastically, or smoothly on two social media datasets – yielding superior performance compared to time-insensitive baselines and outperforming the state-of-the-art on the CLPsych 2022 shared task.

https://github.com/Maria-Liakata-NLP-Group/time-aware-predictions-of-mocs.

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Author information

Authors and Affiliations

  1. Queen Mary University of London, London, UK

    Anthony Hills, Adam Tsakalidis & Maria Liakata

  2. The Alan Turing Institute, London, UK

    Adam Tsakalidis & Maria Liakata

  3. University of Warwick, Coventry, UK

    Maria Liakata

Authors
  1. Anthony Hills
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  2. Adam Tsakalidis
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  3. Maria Liakata
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Corresponding author

Correspondence to Anthony Hills .

Editor information

Editors and Affiliations

  1. University College Dublin, Dublin, Ireland

    Georgiana Ifrim

  2. University of Rennes 2, Rennes, France

    Romain Tavenard

  3. University of Southampton, Southampton, UK

    Anthony Bagnall

  4. Humboldt University of Berlin, Berlin, Germany

    Patrick Schaefer

  5. University of Rennes, Rennes, France

    Simon Malinowski

  6. Claude Bernard University Lyon 1, Villeurbanne, France

    Thomas Guyet

  7. Orange Innovation, Lannion, France

    Vincent Lemaire

Ethics declarations

Ethics Statement

Ethics IRB approval was obtained from the IRB Committee of the lead University prior to engaging in this research study. Our work involves ethical considerations around the analysis of user generated content shared on social media (TalkLife and Reddit). A license was obtained to work with the user data from TalkLife and a project proposal was submitted to them in order to embark on the project. Potential risks from the application of our work in being able to identify moments of change in individuals’ timelines are akin to the identification of those in earlier work on personal event identification from social media and the detection of suicidal ideation. Potential mitigation strategies include restricting and regulating access to the code base and annotation labels used for evaluation.

Appendices

A Annotation Guidelines

The two datasets of longitudinal user posts annotated for MoCs that we make use of in this paper were sourced by [33, 34] for TalkLife and Reddit respectively. Both datasets were annotated using the same annotation guidelines and annotation interface proposed [34].

Annotators were provided with timelines to view, containing chronologically ordered posts by users, along with their associated comments and timestamps. They were then asked to label posts for MoCs.

The first type of label, “Switch” was defined in the guidelines as a “drastic change in mood, in comparison with the recent past”. Annotators were also tasked to label how long the Switch in mood persists (i.e. label its beginning and end). The second type of label “Escalation” was defined in their guidelines as a “gradual change in mood, which should last for a few posts”. Similarly, annotators were also instructed to label the associated range of posts for how long this change persists: where a peak of the escalation must be labelled, and the beginning and end of the gradual mood change also provided. Finally, a label of “None” was provided by default where no mood change was identified for that given post.

B Hyper-parameters Searched

We perform a grid-search over the HEAT parameters (Eq. 1): both \(\beta \) (decay rate) and \(\alpha \) (self-excitation) in the range [0.00001, 0.001, 0.1] for both datasets. All models are searched with learning rates in the range [0.0001, 0.001, 0.01] on Reddit and [0.001, 0.01] for TalkLife. All models are trained with 100 epochs with early stopping using a patience of 5 for all models and both datasets. For the BiLSTM module, we perform a grid-search over all layers using output dimensions of [128, 256, 512] and [128, 256] for Reddit and Talklife respectively. All models were implemented with PyTorch, and were trained using K-Fold cross validation over 5 folds using training, validation, and testing sizes of 60%, 20%, 20% respectively.

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Hills, A., Tsakalidis, A., Liakata, M. (2023). Time-Aware Predictions of Moments of Change in Longitudinal User Posts on Social Media. In: Ifrim, G., et al. Advanced Analytics and Learning on Temporal Data. AALTD 2023. Lecture Notes in Computer Science(), vol 14343. Springer, Cham. https://doi.org/10.1007/978-3-031-49896-1_19

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  • DOI: https://doi.org/10.1007/978-3-031-49896-1_19

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