Yazar "Coban, Onder" seçeneğine göre listele

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    Deep Learning-based Sentiment Analysis of Facebook Data: The Case of Turkish Users
    (Oxford Univ Press, 2021) Coban, Onder; Ozel, Selma Ayse; Inan, Ali
    Sentiment analysis (SA) is an essential task for many domains where it is crucial to know users' public opinion about events, products, brands, politicians and so on. Existing works on SA have concentrated on English texts including Twitter feeds and user reviews on hotels, movies and products. On the other hand, Facebook, as an online social network (OSN), has attracted quite limited attention from the research community. Among these, SA work on Turkish text obtained from OSNs are extremely scarce. In this paper, our aim is to perform SA on public Facebook data collected from Turkish user accounts. Our study differs from existing studies in terms of the data set scale, the natural language of the texts in the data set and the extent of experimental analyses that include both machine learning and deep learning techniques. We extensively report not only the results of different learning models involving SA but also statistical distribution of metadata of user activities across various user attributes (e.g. gender and age). Our experimental results indicate that recurrent neural networks achieve the best accuracy (i.e. 0.916) with word embeddings. To the best of our knowledge, this is the best result for SA on Facebook data in the context of the Turkish language.
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    Detection and Cross-domain Evaluation of Cyberbullying in Facebook Activity Contents for Turkish
    (Assoc Computing Machinery, 2023) Coban, Onder; Ozel, Selma Ayse; Inan, Ali
    Cyberbullying refers to bullying and harassment of defenseless or vulnerable people such as children, teenagers, and women through any means of communication (e.g., e-mail, text messages, wall posts, tweets) over any online medium (e.g., social media, blogs, online games, virtual reality environments). The effect of cyberbullying may be severe and irreversible and it has become one of the major problems of cyber-societies in today's electronic world. Prevention of cyberbullying activities as well as the development of timely response mechanisms require automated and accurate detection of cyberbullying acts. This study focuses on the problem of cyberbullying detection over Facebook activity content written in Turkish. Through extensive experiments with the various machine and deep learning algorithms, the best estimator for the task is chosen and then employed for both cross-domain evaluation and profiling of cyber-aggressive users. The results obtained with fivefold cross-validation are evaluated with an average-macro F1 score. These results show that BERT is the best estimator with an average macro F1 of 0.928, and employing it on various datasets collected from different OSN domains produces highly satisfying results. This article also reports detailed profiling of cyber-aggressive users by providing even more information than what is visible to the naked eye.
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    Facebook Tells Me Your Gender: An Exploratory Study of Gender Prediction for Turkish Facebook Users
    (Assoc Computing Machinery, 2021) Coban, Onder; Inan, Ali; Ozel, Selma Ayse
    Online Social Networks (OSNs) are very popular platforms for social interaction. Data posted publicly over OSNs pose various threats against the individual privacy of OSN users. Adversaries can try to predict private attribute values, such as gender, as well as links/connections. Quantifying an adversary's capacity in inferring the gender of an OSN user is an important first step towards privacy protection. Numerous studies have been made on the problem of predicting the gender of an author/user, especially in the context of the English language. Conversely, studies in this field are quite limited for the Turkish language and specifically in the domain of OSNs. Previous studies for gender prediction of Turkish OSN users have mostly been performed by using the content of tweets and Facebook comments. In this article, we propose using various features, not just user comments, for the gender prediction problem over the Facebook OSN. Unlike existing studies, we exploited features extracted from profile, wall content, and network structure, as well as wall interactions of the user. Therefore, our study differs from the existing work in the broadness of the features considered, machine learning and deep learning methods applied, and the size of the OSN dataset used in the experimental evaluation. Our results indicate that basic profile information provides better results; moreover, using this information together with wall interactions improves prediction quality. We measured the best accuracy value as 0.982, which was obtained by combining profile data and wall interactions of Turkish OSN users. In the wall interactions model, we introduced 34 different features that provide better results than the existing content-based studies for Turkish.
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    Inverse document frequency-based sensitivity scoring for privacy analysis
    (Springer London Ltd, 2022) Coban, Onder; Inan, Ali; Ozel, Selma Ayse
    Privacy risk analysis of online social network (OSN) users aims at generating a risk score for each OSN user such that higher scores potentially imply a greater risk of privacy violation. Privacy risk analysis is typically carried out over a response matrix (R) where any matrix element r(ij) indicates the portion of the OSN that the user i shares his/her attribute j. Most of the existing work relies on the mathematical framework of item response theory to derive sensitivity and visibility components from R. In this study, we propose interpreting R to be a term-document matrix and consequently suggest using the inverse document frequency (IDF) method as the sensitivity component. Experiments performed on both synthetic and real-world datasets show that the proposed IDF-based method can be used as a sensitivity component.
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    Privacy Risk Analysis for Facebook Users
    (IEEE, 2020) Coban, Onder; Inan, Ali; Ozel, Selma Ayse
    Disclosing personal information over Online Social Networks (OSNs) poses security and privacy risks. The privacy risk imposed on an OSN account can be measured through the privacy preferences and the network position of the corresponding account. In this study, two state-of-the-art methods are used to measure privacy risk of Turkish users of the Facebook OSN. Experimental results show that male users and users at age range 21-40 are at greater risk.
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    Turkish Named Entity Discovery Based on Termsets
    (IEEE, 2019) Coban, Onder; Ozel, Selma Ayse; Iean, Ali
    Named Entity Recognition (NER) is a subtask of the information extraction process and aims to discover named entities in unstructured texts. Previous studies on NER mostly use statistical machine learning models instead of using classifiers since solving this problem as a classification task requires to deal with quite high dimensional and sparse vector spaces. In this paper, we take NER as a classical text classification problem and extract nominal features from each token in the unstructured text sequence. We convert each token to a document transaction and then, we use frequent termset mining to extract termset features and apply termset weighting to classify named entities. Therefore we deal with lower dimensional feature spaces. Our experimental results obtained on a large Turkish dataset show that frequent termsets and their weighting scheme can be used in NER task.