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    Fine-grained Kinship Detection for Facebook Users based on Wall Contents
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ooban, Onder; Inan, Ali; Ozel, Selma Ayse
    This paper investigates whether it is possible to automatically detect fine-grained kinship (not to detect its existence but to detect the type of kinship like child, father, grandfather, and so on) between two Facebook users or not. To do so, we present and employ a lexicon-based approach that completely depends on the wall contents of users. To the best of our knowledge, this is the first study towards kinship detection for both the type of input data (i.e., free OSN text) and the language (i.e., Turkish). We perform our experiments on a crawled snapshot of public Facebook data collected from accounts of users in Turkey. Our results are promising and show that a content-based approach can be a good starting point for future works even though it has some challenges. © 2021 IEEE.
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    Named Entity Recognition over FBNER: A New Facebook Dataset in Turkish
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ooban, Onder; Ozel, Selma Ayse; Inan, Ali
    In this paper, we introduce a new Named Entity Recognition (NER) dataset of Facebook messages written in the Turkish language. We also employ a Conditional Random Fields based NER system to discover named entities from Facebook messages. Our system achieves an F1 score of 0.713 when training and test sets include Facebook posts. We also obtained an F1 score of 0.599 when the training set is from the news domain. A strength of this research is that it is one of the first studies in this field that focuses on NER over Turkish Facebook messages. This is because performing NER on user-generated content turns into a very challenging task since such informal contents are often noisy texts that have arammatical and spelling errors. © 2021 IEEE.