Utilizing Big Data from Google Trends to Map Out Population Depression in the United States: Exploratory Infodemiology Study (Preprint)

BACKGROUND The epidemiology of mental health disorders has important theoretical and practical implications for healthcare service and planning. The recent increase in big data storage and subsequent development of analytical tools suggests that mining search databases may yield important trends on mental health, which can be used to replace or support existing population health studies. OBJECTIVE This study aimed to map out depression search intent in the United States based on internet mental health queries. METHODS Weekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: “feeling sad,” “depressed,” “depression,” “empty,” “insomnia,” “fatigue,” “guilty,” “feeling guilty,” and “suicide”. Multivariable regression models were created based on geographic and environmental factors and normalized to control terms “sports,” “news,” “google,” “youtube,” “facebook,” and “netflix”. Heat maps of population depression were generated based on search intent. RESULTS Depression search intent grew 67% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P < 0.001) and early spring months (adjusted P < 0.001), relative to summer months. Geographic location correlated to depression search intent with states in the Northeast (adjusted P = 0.01) having higher search intent than states in the South. CONCLUSIONS The trends extrapolated from Google Trends successfully correlate with known risk factors for depression, such as seasonality and increasing latitude. These findings suggest that Google Trends may be a valid novel epidemiological tool to map out depression prevalence in the United States.

A Query Understanding Framework for Earth Data Discovery

One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospatial domain has investigated user queries in a systematic way. Here, we propose a query understanding framework and apply it to fill the gap by better interpreting a user’s search intent for Earth data search engines and adopting knowledge that was mined from metadata and user query logs. The proposed query understanding tool contains four components: spatial and temporal parsing; concept recognition; Named Entity Recognition (NER); and, semantic query expansion. Spatial and temporal parsing detects the spatial bounding box and temporal range from a query. Concept recognition isolates clauses from free text and provides the search engine phrases instead of a list of words. Name entity recognition detects entities from the query, which inform the search engine to query the entities detected. The semantic query expansion module expands the original query by adding synonyms and acronyms to phrases in the query that was discovered from Web usage data and metadata. The four modules interact to parse a user’s query from multiple perspectives, with the goal of understanding the consumer’s quest intent for data. As a proof-of-concept, the framework is applied to oceanographic data discovery. It is demonstrated that the proposed framework accurately captures a user’s intent.