Seasonality in Seeking Mental Health Information on Google

Ayers, John W.; Althouse, Benjamin M.; Allem, Jon Patrick; Rosenquist, J. Niels; Ford, Daniel E.

doi:10.1016/j.amepre.2013.01.012

Cited by 188 publications

(161 citation statements)

References 53 publications

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“…Thus, social media seems like a perfect fit for studying mental health in both individual and overall trends in the population. Such topics have already been the focus of several studies (Coppersmith et al, 2014;De Choudhury et al, 2014;De Choudhury et al, 2013d;De Choudhury et al, 2013b;De Choudhury et al, 2013c;Ayers et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Quantifying Mental Health Signals in Twitter

Coppersmith¹,

Dredze²,

Harman³

2014

Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

525

469

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The ubiquity of social media provides a rich opportunity to enhance the data available to mental health clinicians and researchers, enabling a better-informed and better-equipped mental health field. We present analysis of mental health phenomena in publicly available Twitter data, demonstrating how rigorous application of simple natural language processing methods can yield insight into specific disorders as well as mental health writ large, along with evidence that as-of-yet undiscovered linguistic signals relevant to mental health exist in social media. We present a novel method for gathering data for a range of mental illnesses quickly and cheaply, then focus on analysis of four in particular: post-traumatic stress disorder (PTSD), depression, bipolar disorder, and seasonal affective disorder (SAD). We intend for these proof-of-concept results to inform the necessary ethical discussion regarding the balance between the utility of such data and the privacy of mental health related information.

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Section: Introductionmentioning

confidence: 99%

Quantifying Mental Health Signals in Twitter

Coppersmith¹,

Dredze²,

Harman³

2014

Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

525

469

View full text Add to dashboard Cite

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“…We have not found systematic studies of seasonality of mood via Twitter, with only one study addressing the topic of mental health using the incidence of Google searches as an indicator for information-seeking about such topics [24].…”

Section: Introductionmentioning

confidence: 99%

“…Various studies have attempted to address these problems and collect information that relates to mental health from online activities [20]- [24]. While this may relate to a specific sub-population, it has the advantage of allowing constant monitoring of a very large number of subjects and of bypassing the aforementioned issues of self-reporting.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Fluctuations in Collective Mood Revealed by Wikipedia Searches and Twitter Posts

Dzogang

Lansdall-Welfare

Cristianini

2016

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

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“…We also focus on work that estimates biologically-rooted metrics. For example, we exclude metrics based on seasonality [47,48] and over-the-counter drug sales volume, itself a proxy [49].…”

Section: Author Summarymentioning

confidence: 99%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

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Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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Seasonality in Seeking Mental Health Information on Google

Cited by 188 publications

References 53 publications

Quantifying Mental Health Signals in Twitter

Quantifying Mental Health Signals in Twitter

Seasonal Fluctuations in Collective Mood Revealed by Wikipedia Searches and Twitter Posts

Global Disease Monitoring and Forecasting with Wikipedia

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