In electoral contexts, mental health topics have recently attracted sociopolitical relevance, influenced by policy developments, election-related psychopathology and popular discourse about individual candidates. Yet, whether this reflects generalised trends or is contingent on election-specific and contextual factors remains difficult to ascertain. This study sought to examine correlations between Google Trends (GT) data on mental health and four national elections in the US and the UK from 2008 to 2020. This was intended to yield preliminary insights into the relevance of mental health topics amongst voters and the potential impact of electoral cycles on patterns of online engagement with these issues. Monthly and daily Search Volume Indexes (SVI) were gathered from the 'Mental Health' category on GT in the US and UK from 2008 to 2023. SVI were evaluated around the past four national ballots, comparing election-year intervals and baseline data from the preceding year. Statistical tests were conducted to assess SVI and short- and long-term dynamics. The results showed heterogeneous SVI patterns related to mental health during elections in the US and the UK. In the US, there were statistically significant SVI increases proximal to the majority of elections as compared to data in the same calendar year, but these did not typically exceed baseline SVI. However, interestingly, divisive US contests showed correlations with several elevated SVI measures in the same and previous years. Conversely, there was a lack of consistent longitudinal trends in UK elections, perhaps informed by its disparate sociopolitical landscape. These findings underline the composite relationship between electoral politics and popular engagement with mental health topics around national votes, suggesting that this is driven more by situational factors rather than a recurrent electoral effect or signs of burgeoning engagement. Detailed research is required to understand the nuances and causality behind these dynamics and their wider implications.
Read full abstract