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Mental health-related conversations on social media and crisis episodes: a time-series regression analysis

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Mental health-related conversations on social media and crisis episodes: a time-series regression analysis. / Kolliakou, Anna; Bakolis, Ioannis; Chandran, David; Derczynski, Leon; Werbeloff, Nomi; Osborn, David PJ; Bontcheva, Kalina; Rob, Stewart.

In: Scientific Reports, Vol. 10, 1342, 02.2020.

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

Harvard

APA

Kolliakou, A., Bakolis, I., Chandran, D., Derczynski, L., Werbeloff, N., Osborn, D. PJ., Bontcheva, K., & Rob, S. (2020). Mental health-related conversations on social media and crisis episodes: a time-series regression analysis. Scientific Reports, 10, [1342]. https://doi.org/10.1038/s41598-020-57835-9

Vancouver

Author

Kolliakou, Anna ; Bakolis, Ioannis ; Chandran, David ; Derczynski, Leon ; Werbeloff, Nomi ; Osborn, David PJ ; Bontcheva, Kalina ; Rob, Stewart. / Mental health-related conversations on social media and crisis episodes: a time-series regression analysis. In: Scientific Reports. 2020 ; Vol. 10.

Bibtex

@article{e03797954d3e487fa300a2e5ea3edb7d,
title = "Mental health-related conversations on social media and crisis episodes: a time-series regression analysis",
abstract = "We aimed to investigate whether daily fluctuations in mental health-relevant Twitter posts are associated with daily fluctuations in mental health crisis episodes. We conducted a primary and replicated time-series analysis of retrospectively collected data from Twitter and two London mental healthcare providers. Daily numbers of {\textquoteleft}crisis episodes{\textquoteright} were defined as incident inpatient, home treatment team and crisis house referrals between 2010 and 2014. Higher volumes of depression and schizophrenia tweets were associated with higher numbers of same-day crisis episodes for both sites. After adjusting for temporal trends, seven-day lagged analyses showed significant positive associations on day 1, changing to negative associations by day 4 and reverting to positive associations by day 7. There was a 15% increase in crisis episodes on days with above-median schizophrenia-related Twitter posts. A temporal association was thus found between Twitter-wide mental health-related social media content and crisis episodes in mental healthcare replicated across two services. Seven-day associations are consistent with both precipitating and longer-term risk associations. Sizes of effects were large enough to have potential local and national relevance and further research is needed to evaluate how services might better anticipate times of higher risk and identify the most vulnerable groups.",
author = "Anna Kolliakou and Ioannis Bakolis and David Chandran and Leon Derczynski and Nomi Werbeloff and Osborn, {David PJ} and Kalina Bontcheva and Stewart Rob",
year = "2020",
month = feb,
doi = "10.1038/s41598-020-57835-9",
language = "English",
volume = "10",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",

}

RIS

TY - JOUR

T1 - Mental health-related conversations on social media and crisis episodes: a time-series regression analysis

AU - Kolliakou, Anna

AU - Bakolis, Ioannis

AU - Chandran, David

AU - Derczynski, Leon

AU - Werbeloff, Nomi

AU - Osborn, David PJ

AU - Bontcheva, Kalina

AU - Rob, Stewart

PY - 2020/2

Y1 - 2020/2

N2 - We aimed to investigate whether daily fluctuations in mental health-relevant Twitter posts are associated with daily fluctuations in mental health crisis episodes. We conducted a primary and replicated time-series analysis of retrospectively collected data from Twitter and two London mental healthcare providers. Daily numbers of ‘crisis episodes’ were defined as incident inpatient, home treatment team and crisis house referrals between 2010 and 2014. Higher volumes of depression and schizophrenia tweets were associated with higher numbers of same-day crisis episodes for both sites. After adjusting for temporal trends, seven-day lagged analyses showed significant positive associations on day 1, changing to negative associations by day 4 and reverting to positive associations by day 7. There was a 15% increase in crisis episodes on days with above-median schizophrenia-related Twitter posts. A temporal association was thus found between Twitter-wide mental health-related social media content and crisis episodes in mental healthcare replicated across two services. Seven-day associations are consistent with both precipitating and longer-term risk associations. Sizes of effects were large enough to have potential local and national relevance and further research is needed to evaluate how services might better anticipate times of higher risk and identify the most vulnerable groups.

AB - We aimed to investigate whether daily fluctuations in mental health-relevant Twitter posts are associated with daily fluctuations in mental health crisis episodes. We conducted a primary and replicated time-series analysis of retrospectively collected data from Twitter and two London mental healthcare providers. Daily numbers of ‘crisis episodes’ were defined as incident inpatient, home treatment team and crisis house referrals between 2010 and 2014. Higher volumes of depression and schizophrenia tweets were associated with higher numbers of same-day crisis episodes for both sites. After adjusting for temporal trends, seven-day lagged analyses showed significant positive associations on day 1, changing to negative associations by day 4 and reverting to positive associations by day 7. There was a 15% increase in crisis episodes on days with above-median schizophrenia-related Twitter posts. A temporal association was thus found between Twitter-wide mental health-related social media content and crisis episodes in mental healthcare replicated across two services. Seven-day associations are consistent with both precipitating and longer-term risk associations. Sizes of effects were large enough to have potential local and national relevance and further research is needed to evaluate how services might better anticipate times of higher risk and identify the most vulnerable groups.

U2 - 10.1038/s41598-020-57835-9

DO - 10.1038/s41598-020-57835-9

M3 - Journal article

VL - 10

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 1342

ER -

ID: 84743770