Circadian mood variations in Twitter content.
Circadian mood variations in Twitter content.
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DOI：
10.1177/2398212817744501
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发表时间：
2017-01-01
影响因子：
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通讯作者：
Cristianini, Nello
中科院分区：
文献类型：
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作者：
Dzogang, Fabon;Lightman, Stafford;Cristianini, Nello
背景：睡眠、认知和代谢状态的昼夜节律调节是由中央时钟驱动的，而中央时钟又受到环境信号的影响。了解情绪的昼夜节律调节对于应对日常需求至关重要，需要大量数据集，并且通常采用主观报告。方法：在这项研究中，我们使用了 4 年来在英国收集的超过 8 亿条 Twitter 消息的庞大数据集。我们从情绪和疲劳的集体表达中提取一天中发生的变化的强烈信号。我们使用统计分析和傅立叶分析方法来识别周期性结构、极值、变化点，并比较这些事件在季节和周末的稳定性。结果：我们揭示了积极和消极情绪的强烈但不同的昼夜节律模式。疲劳和愤怒的周期在不同季节和周末/工作日的界限内显得非常稳定。积极情绪和悲伤更多地相互作用以应对这些不断变化的情况。愤怒和较低程度的疲劳表现出一种与已知的血浆皮质醇浓度昼夜节律变化相反的模式。大多数数量在早上表现出强烈的拐点。结论：由于昼夜节律和睡眠障碍在整个情绪障碍中都有报道，我们建议对社交媒体的分析可以为理解精神障碍提供宝贵的资源。
BACKGROUND: Circadian regulation of sleep, cognition, and metabolic state is driven by a central clock, which is in turn entrained by environmental signals. Understanding the circadian regulation of mood, which is vital for coping with day-to-day needs, requires large datasets and has classically utilised subjective reporting.METHODS: In this study, we use a massive dataset of over 800 million Twitter messages collected over 4 years in the United Kingdom. We extract robust signals of the changes that happened during the course of the day in the collective expression of emotions and fatigue. We use methods of statistical analysis and Fourier analysis to identify periodic structures, extrema, change-points, and compare the stability of these events across seasons and weekends.RESULTS: We reveal strong, but different, circadian patterns for positive and negative moods. The cycles of fatigue and anger appear remarkably stable across seasons and weekend/weekday boundaries. Positive mood and sadness interact more in response to these changing conditions. Anger and, to a lower extent, fatigue show a pattern that inversely mirrors the known circadian variation of plasma cortisol concentrations. Most quantities show a strong inflexion in the morning.CONCLUSION: Since circadian rhythm and sleep disorders have been reported across the whole spectrum of mood disorders, we suggest that analysis of social media could provide a valuable resource to the understanding of mental disorder.