Using social media to monitor mental health discussions − evidence from Twitter

TLDR

Mental‑health communication is increasingly important for public health, and social media platforms are becoming a major channel for disseminating information. The study aimed to develop an empirical model that detects periods of heightened interest in mental‑health topics on Twitter. An ARIMA model was applied to 176 million tweets from 2011‑2014 to identify deviations from predicted trends in depression and suicide discussions, distinguishing expected increases from planned events and unexpected spikes. The ARIMA model proved effective, showing that tweet volume after expected events returns to baseline more quickly than after unexpected events, with spikes lasting less than two days, underscoring the need for timely dissemination of credible information.

Abstract

Given the public health importance of communicating about mental illness and the growing use of social media to convey information, our goal was to develop an empirical model to identify periods of heightened interest in mental health topics on Twitter.We collected data on 176 million tweets from 2011 to 2014 with content related to depression or suicide. Using an autoregressive integrated moving average (ARIMA) data analysis, we identified deviations from predicted trends in communication about depression and suicide.Two types of heightened Twitter activity regarding depression or suicide were identified in 2014: expected increases in response to planned behavioral health events, and unexpected increases in response to unanticipated events. Tweet volume following expected increases went back to the predicted level more rapidly than the volume following unexpected events.Although ARIMA models have been used extensively in other fields, they have not been used widely in public health. Our findings indicate that our ARIMA model is valid for identifying periods of heightened activity on Twitter related to behavioral health. The model offers an objective and empirically based measure to identify periods of greater interest for timing the dissemination of credible information related to mental health.Spikes in tweet volume following a behavioral health event often last for less than 2 days. Individuals and organizations that want to disseminate behavioral health messages on Twitter in response to heightened periods of interest need to take this limited time frame into account.