High-resolution Temporal Representations of Alcohol and Tobacco Behaviors from Social Media Data.
High-resolution Temporal Representations of Alcohol and Tobacco Behaviors from Social Media Data.
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社交媒体数据中酒精和烟草行为的高分辨率时间表示。
DOI：
10.1145/3134689
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发表时间：
2017
影响因子：
--
通讯作者：
Chunara,Rumi
中科院分区：
文献类型：
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作者：
Huang,Tom;Elghafari,Anas;Relia,Kunal;Chunara,Rumi
了解与烟草和酒精相关的行为模式对于发现风险因素和潜在地设计有针对性的社会计算干预系统至关重要。鉴于我们每天要做多次选择，每小时和每天的模式对于更好地理解行为至关重要。在这里，我们结合联合收割机自然语言处理，机器学习和时间序列分析，以评估Twitter的活动，特别是与酒精和烟草消费及其子日，日和周周期。Twitter的酒精和烟草使用的自我报告与其他数据流在类似的时间分辨率进行了比较。我们评估是否可以使用这些时间模式区分推断的未成年人与法律的年龄的人或使用不同类型的烟草产品的讨论饮酒。我们发现，社交媒体上的行为的时域和频域表示可以提供有意义的和独特的见解，我们讨论的行为类型的方法可能是最有用的。
Understanding tobacco- and alcohol-related behavioral patterns is critical for uncovering risk factors and potentially designing targeted social computing intervention systems. Given that we make choices multiple times per day, hourly and daily patterns are critical for better understanding behaviors. Here, we combine natural language processing, machine learning and time series analyses to assess Twitter activity specifically related to alcohol and tobacco consumption and their sub-daily, daily and weekly cycles. Twitter self-reports of alcohol and tobacco use are compared to other data streams available at similar temporal resolution. We assess if discussion of drinking by inferred underage versus legal age people or discussion of use of different types of tobacco products can be differentiated using these temporal patterns. We find that time and frequency domain representations of behaviors on social media can provide meaningful and unique insights, and we discuss the types of behaviors for which the approach may be most useful.