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- Garcia, Danilo, 1973, et al.
(author)
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A Collective Picture of What Makes People Happy: Words Representing Social Relationships, not Money, are Recurrent with the Word ‘Happiness’ in Online Newspapers
- 2016
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In: G. Riva, B. K. Wiederhold, & P. Cipresso (Eds.), The Psychology of Social Networking. Identity and Relationships in Online Communities Vol. 2. - : DeGruyter Open. - 9783110473780 ; , s. 4-16, s. 4-16
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Book chapter (peer-reviewed)abstract
- The Internet allows people to freely navigate through news and use that information to reinforce or support their own beliefs in, for example, different social networks. In this chapter we suggest that the representation of current predominant views in the news can be seen as collective expressions within a society. Seeing that the notion of what makes individuals happy has been of increasing interest in recent decades, we analyze the word happiness in online news. We first present research on the co-occurrence of the word happiness with other words in online newspapers. Among other findings, words representing people (e.g., “mom”, “grandmother”, “you”/”me”, “us”/”them”) often appear with the word happiness. Words like “iPhone”, “millions” and “Google” on the other hand, almost never appear with the word for happiness. Secondly, using words with predefined sets of psycholinguistic characteristics (i.e., word-norms measuring social relationships, money, and material things) we further examine differences between sets of articles including the word happiness (“happy” dataset) and a random set (“neutral” dataset) of articles not including this word. The results revealed that the “happy” dataset was significantly related to social relationships word-norm, while the “neutral” dataset was related to the money word-norm. However, the “happy” dataset was also related to the material things word-norm. In sum, there is a relatively coherent understanding among members of a society concerning what makes us happy: relationship, not money; meanwhile there is a more complex relationship when it comes to material things. The semantic method used here, which is particularly suitable for analyzing big data, seems to be able to quantify collective ideas in online news that might be expressed through different social networks.
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