Robust language-based mental health assessments in time and space through social media

Code for generating robust spatial and temporal measurements of well-being as described in Mangalik et al., 2024.

This system is currently built around aggregation to the account (user) level and the weekly level and then aggregating those results up to the county and week level. The code is generally flexible to other space and time resolutions but has not been thoroughly tested and may need adjustments. When in doubt, please look to README-advanced.md for the complete command set.

Full pipeline for generating scores and running analyses:

Prerequisites:

• create a symlink between your home directory and your spark directory called spark/ such that spark jobs be run via ~/spark/bin/spark-submit

1. Tweet Gathering

• Collecting tweets
  • Use the TwitterMySQL library found in DLATK, the GitHub repository has information about accessing the Twitter API keys to pull tweets
  • Command to run tweet extraction: python twInterface.py -d twitter -t batch_1 --time_lines --user_list </path/to/listOfAccounts.txt> --authJSON </path/to/twitterAPIkeys.json>
• Mapping twitter users to counties
  • Further details can be found in: Characterizing Geographic Variation in Well-Being Using Tweets

2. Data Preparation

Filters to english, as well as removes retweets, tweets with urls, duplicate tweets.

• Runnable as: ./filterTweets.sh HDFS_INPUT_CSV MESSAGE_FIELD_IDX GROUP_FIELD_IDX
  • Input:
    • HDFS_INPUT_CSV -- tweets in csv
    • MESSAGE_FIELD_IDX -- the column index for the text
    • GROUP_FIELD_IDX -- is the column index for the id.
  • Output:
    • hdfs_intput_csv_english_deduped -- same format as input but with rows filtered out
• Advanced: Run each step independently

3. Account-, time-level, lexical scoring

Extracts word mentions per group_id (group_id is commonly time_unit:user_id), optionally postratifies given weights_csv, and then runs the specified weighted lexicon per group_id.

• Runnable as: lexiconExtractAndScore.sh HDFS_INPUT_CSV MESSAGE_FIELD_IDX GROUP_ID_IDX LEXICON_CSV [WEIGHTS_CSV]
  • Input:
    • HDFS_INPUT_CSV -- social media data (e.g. tweets) with tweet body, group_id containing account number of tweeter and timeunit (like week) tweet was made, mapping for accounts to a location (like county)
    • MESSAGE_FIELD_IDX -- column index where message (text) is contained
    • GROUP_ID_IDX -- column index for the group id (i.e. the column to agrgegate words by).
    • LEXICON_CSV -- weighted lexicon: column 0 is word, column 1 is term, and column 3 is weight, sample lexicon
    • WEIGHTS_CSV -- (optional) maps group_id to weights: column 0 is group_id, column 1 is weight
  • If accounts are going to be reweighted then a mapping between entities and weights must be provided
  • Output
    • feat.lexicon.hdfs_input_csv
    • Format: [timeunit+account], [score_type], [weighted_count], [weighted_score]
• Advanced: Run each step independently

4. Location aggregation

Aggregates lexica to a higher order group by field (e.g. county)

• Runnable as ./locationTimeAggregation.sh HDFS_INPUT_CSV GROUP_ID_IDX AGG_CSV MIN_GROUPS [HDFS_OUTPUT_CSV]
  • HDFS_INPUT_CSV -- csv with group_ids and scores per group_id
  • GROUP_ID_IDX -- column index for the group id (i.e. the column to be mapped to a higher order group)
  • AGG_CSV -- csv with mapping from group_id (column 0) to higher order group (column 1): e.g. mapping from time_unit:user_id to time_unit:county_id.
  • MIN_GROUPS -- Minimum number of groups per higher order group in order to include in aggregated output.
  • HDFS_OUTPUT_CSV -- (optionally) specify the output csv filename (otherwise it will be INPUT_CSV-AGG_CSV)
• Advanced: Run each step independently

You now have the scores per time:unit, community.

5. Analysis (Optional)

• We provide a notebook demonstrating how to use the outputted scores to do all analyses seen in the original manuscript
  • See the data generated for Robust language-based mental health assessments in time and space through social media in wwbp/lbmha_2019-2020