Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect

Abstract

Background The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. Objective Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. Methods Using COVID-19–related news headlines from a database of online news stories in conjunction with mental health–related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. Results Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA β=–.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA β=.221; P<.001) contributing generally to an increase in online mental health search term frequency. Conclusions This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health–related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.

Assessing the Impacts of COVID-19 and Social Isolation on Mental Health in the United States of America

“…Several past studies have unearthed possible links between COVID-19 infection and development of anxious or depressive symptoms [24, 25, 26, 27, 29, 30, 31, 32, 28, 23] and while our study does not address that specific topic, it does imply that an increase in COVID-19 incidence and death may lead to increased anxiety and depression and this can be exacerbated by factors that coincide with increased prevalence of disease such as continuous media coverage and the possibility of increased medical bills and loss of wages due to long-term issues that can arise with COVID-19 infection [33, 34]. Second, variables related to social isolation did have a significant impact on anxiety and depression in the general population (see Figures 3, 4, 7, and 8), but perhaps less so than the impact that variables related to COVID-19 had.…”

The Relationship between Searches for COVID-19 Vaccines and Dynamics of Vaccinated People in Poland: An Infodemiological Study

“…During the COVID-19 pandemic, Google Trends was used to evaluate the behaviours of Internet users in many countries (e.g., India, Italy, the USA, Columbia, Turkey) [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. In one part of the study the analysed period was the SARS-CoV-2 pandemic, and in the other part the words and phrases searched for before and during the pandemic were compared.…”

Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data

“…However, the sudden and widespread move toward remote work during the pandemic has also influenced the provision of care for mental illness—what Shore et al [ 4 ] call “the rapid virtualization of psychiatric care.” Given the requirements of social distancing and pandemic-incited isolation, an increasing number of individuals have turned to technology-mediated tools and resources to find help when in distress, including online support communities [ 5 ], helplines [ 6 ], resources recommended by search engines [ 7 ], teletherapy [ 8 ], and telepsychiatry [ 4 ], among other modalities. Recommendation algorithms that analyze individual language around mental health underlie how these tools suggest resources to people in need [ 9 ], including ways that may be opaque to those engaging with the technology- or algorithmically mediated support system [ 10 ].…”