Exploring Self-Identified Counseling Expertise in Online Support Forums

This repository contains data information and experimental code for our ACL 2021 Findings paper Exploring Self-Identified Counseling Expertise in Online Support Forums.

Data

• task_data.csv: contains the post ids, comment ids, subreddit names, and author type (mhp or non-mhp) of each data instance used in this study.

• subreddit_topics.csv: Contains the health topics for the subreddits.

  To create these topics, we began with Sharma & Munmun (2018)'s subreddit categorization, which includes the categories Trauma & Abuse, Psychosis & Anxiety, Compulsive Disorders, Coping & Therapy, and Mood Disorders. Then, we used World Health Organization's ICD-10 classification system of mental and behavioural disorders as a basis for categorizing the additional subreddits in our study, and to adjust and add to the original categories.

Distinguishing MHPs and Peers

Unigram Features

• 1-distinguishing-mhps-and-peers.ipynb: code for the experiments with unigram features.

LIWC & LIWC Perspective features

• A license is required for LIWC (see http://liwc.wpengine.com/) so we haven't posted our code, however, we have published the results_df's and fold_vector_df's if you would like to compare a replication to our results.
  • results with all LIWC classes: liwc.results_df.pickle & liwc.fold_vector_df.pickle
  • results with perspective LIWC classes: perspective.results_df.pickle & perspective.fold_vector_df.pickle
• The experimental setup is the same as for unigram features, however instead of counting unigrams, we count the LIWC classes of each unigram.
• The classes used for the perspective features are the following:

  liwc_perspective_classes = ['FOCUSFUTURE', 'FOCUSPAST', 'FOCUSPRESENT', 'I', 'IPRON', 'NEGEMO', 'POSEMO', 'PPRON', 'PRONOUN', 'SHEHE', 'THEY', 'WE', 'YOU']

Linguistic and Dialogue Analysis

LIWC.ipynb: Code for plotting Figure 1, LIWC usage. We provide the precomputed dominance scores for making the plots, since a license is required for LIWC (see http://liwc.wpengine.com/).

Wordnet-Affect.ipynb: Code for plotting Figure 2, WordNet affect usage. We provide the precomputed dominance scores for making the plots. To obtain WordNet domains, please see here: https://wndomains.fbk.eu/download.html.

Prompts-Support-Seeker.ipynb: Code for plotting Figure 3, LIWC categories based on posts that prompt support-seeker replies. We provide percent usage of each LIWC category for each grouping, since a license is required for LIWC (see http://liwc.wpengine.com/).

LSM.ipynb: Code for plotting Figure 4, linguistic style matching. We provide the precomputed LSM scores for the function word categories for each post-reply pair, since a license is required for LIWC (see http://liwc.wpengine.com/).

Language Modeling

See the README in the language_model folder.

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Citation

Please cite the following paper if you find this resource useful in your research:

@inproceedings{lahnala-etal-2021-exploring,
    title = "Exploring Self-Identified Counseling Expertise in Online Support Forums",
    author = "Lahnala, Allison and Zhao, Yuntian and, Welch, Charles and Kummerfeld, Jonathan K. and An, Lawrence C and Resnicow, Kenneth and Mihalcea, Rada and P{\'e}rez-Rosas, Ver{\'o}nica",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.392",
    doi = "10.18653/v1/2021.findings-acl.392",
    pages = "4467--4480",
}

References

*Eva Sharma and Munmun De Choudhury. 2018. Mental health support and its relationship to linguistic accommodation in online communities. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI ’18, page 1–13, New York, NY, USA. Association for Computing Machinery.