Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter.
Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter.
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DOI：
10.1371/journal.pone.0026752
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Danforth CM
中科院分区：
文献类型：
--
作者：
Dodds PS;Harris KD;Kloumann IM;Bliss CA;Danforth CM
个人幸福是一个基本的社会指标。幸福通常通过自我报告来衡量，但往往被更容易量化的经济指标（如国内生产总值）间接表征和掩盖。在这里，我们研究了在网上，全球微博和社交网络服务Twitter的表达，揭示和解释的时间尺度从几个小时到几年的幸福和信息水平的时间变化。我们的数据集包含超过460亿个单词，包含在超过6300万独立用户在33个月内发布的近46亿个表情中。在测量幸福感时，我们构建了一个可调的、实时的、遥感的、非侵入性的、基于文本的幸福度计。在构建我们的指标时，我们进行了一项调查，以获得超过10，000个单词的幸福评估，这比现有的类似单词集提高了10倍。而不是特设的，我们的单词列表是完全由使用频率选择，我们展示了如何构建和捍卫一个高度稳健和可调的度量。
Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month span by over 63 million unique users. In measuring happiness, we construct a tunable, real-time, remote-sensing, and non-invasive, text-based hedonometer. In building our metric, made available with this paper, we conducted a survey to obtain happiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing word sets. Rather than being ad hoc, our word list is chosen solely by frequency of usage, and we show how a highly robust and tunable metric can be constructed and defended.
影响因子：
2.6
作者：
Bollen, Johan;Goncalves, Bruno;Mao, Huina
通讯作者：
Mao, Huina