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Yayın The use of Facebook by Turkish mothers: its reasons and outcomes(Springer, 2020-03-01) Ögel Balaban, Hale; Altan, ŞebnemObjectives: Parents use social network sites for reasons related to bridging and bonding social capital, and entertainment. The aim of the present study was to examine whether the use of Facebook by Turkish mothers and its reasons are related to mothers’ demographic characteristics, anxiety level and perceived social support. It also examined whether mothers’ Facebook use contributes to their perception of their parental role. Methods: Three hundred thirty-two middle-class Turkish mothers who reported to use Facebook completed the demographic information questionnaire, the use of social media questionnaire, the anxiety inventory, the perceived social support scale and the self-perception of parental role questionnaire. Results: Results indicated that Turkish mothers use Facebook more for reasons related to bridging social capital than reasons related to bonding social capital and entertainment. The frequency of using Facebook and the length of time having an account predicted the use of Facebook for reasons related to bridging and bonding social capital. Anxiety level predicted the use of Facebook for reasons related to entertainment. Mothers’ Facebook use was found not to be related to their self-perceived parental competence. Conclusions: The discussion of these findings in terms of Turkish culture implied the need for cross-cultural studies for a better understanding of parents’ use of social network sites.Yayın Sarcasm detection on news headlines using transformers(Springer, 2025-09-07) Gümüşçekiçci, Gizem; Dehkharghani, RahimSarcasm poses a linguistic challenge due to its figurative nature, where intended meaning contradicts literal interpretation. Sarcasm is prevalent in human communication, affecting interactions in literature, social media, news, e-commerce, etc. Identifying the true intent behind sarcasm is challenging but essential for applications in sentiment analysis. Detecting sarcasm in written text, as a challenging task, has attracted many researchers in recent years. This paper attempts to detect sarcasm in news headlines. Journalists prefer using sarcastic news headlines as they seem much more interesting to the readers. In the proposed methodology, we experimented with Transformers, namely the BERT model, and several Machine and Deep Learning models with different word and sentence embedding methods. The proposed approach inherently requires high-performance resources due to the use of large-scale pre-trained language models such as BERT. We also extended an existing news headlines dataset for sarcasm detection using augmentation techniques and annotating it with hand-crafted features. The proposed methodology could outperform almost all existing sarcasm detection approaches with a 98.86% F1-score when applied to the extended news headlines dataset, which we made publicly available on GitHub.
