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After Fomo: five more feelings of angst in the social media age". The Guardian. 2016-01-18 . Retrieved 2021-11-04. The phenomenon was first identified in 1996 by marketing strategist Dr. Dan Herman, who conducted research and published the first academic paper on the topic in 2000 in The Journal of Brand Management. [22] Herman also believes the concept has evolved to become more wide spread through mobile phone usage, texting, and social media and have helped flesh out the concept of the fear of missing out to the masses. [11] Before the Internet, a related phenomenon, " keeping up with the Joneses", was widely experienced. FOMO generalized and intensified this experience because so much more of people's lives became publicly documented and easily accessed. Further, a common tendency is to post about positive experiences (such as a great restaurant) rather than negative ones (such as a bad first date). Studies have found that the likelihood of experiencing fear of missing out has been linked to anxiety or depression. [2] [23]
The classifiers utilized, while by no means comprehensive, embody a range of disparate approaches that make it possible to draw reasonable conclusions as to how well FoMO items can predict each of the behavior domains. The three metrics–accuracy, F1-score and ROC AUC (denoted as ROC in the table header)–reveal different aspects of the skillfulness of the classifier. The range of the values for the metrics is [0, 1], where 0 indicates a no-skill model, and 1 represents perfect skill. The question of which metric is most informative is to a great extent dependent on the goal of the modeling scenario and the characteristics of the dataset. For instance, as discussed earlier, accuracy, being the percentage of instances that the classifier predicts correctly, is not very informative if false negatives or false positives carry different weights, or the dataset is imbalanced. In such cases, the F1-score is a much more appropriate metric. On the other hand, while ROC AUC is a widely adopted general metric for evaluating algorithm performance, it may not be an informative metric for evaluating a model that is calibrated using a given decision threshold. Hence, we include all three metrics in Tables 3 and 4 to allow for a broad assessment of the performance of the techniques employed.
Measures
Deci, E.L. & Ryan, R.M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press. ISBN 9780306420221.
When you say “no,” you may even regret your decision at first. On the other hand, if you are meant to experience the opportunity at all, it will come available to you at a later time. 5. Reassure YourselfBeyens I, Frison E, Eggermont S. “I don’t want to miss a thing”: Adolescents’ fear of missing out and its relationship to adolescents’ social needs, Facebook use, and Facebook related stress. Computers in Human Behavior. 2016;64: 1-8. Plus, TrustPulse lets you customize your notifications without requiring any technical knowledge. Everything can be done by filling in text field boxes or selecting options from drop-down menus:
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