Researcher:
Baruh, Lemi

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Lemi

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Baruh

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Baruh, Lemi

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2010 - 2019262020 - 20238

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Now showing 1 - 10 of 35
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    Citizen (in)security?: social media, citizen journalism and crisis response
    (The Pennsylvania State University, 2014) Watson, Hayley; Finn, Rachel L.; Scifo, Salvatore; Department of Media and Visual Arts; Baruh, Lemi; Faculty Member; Department of Media and Visual Arts; College of Social Sciences and Humanities; 36113
    The use of social media in a crisis has been applauded, and is witnessing an increase in uptake among those involved in crisis management activities, including citizens. Whilst some challenges have been discussed elsewhere, somewhat lacking is a discussion on the impact of sharing information on the security of those that may have been recorded. Accordingly, this working paper aims to provide preliminary results of an initial mapping task that seeks to examine the impact of the use of social media in a crisis on the social and ethical wellbeing of the security of the citizen. Authors argue that the heightened involvement of citizen journalism results in the filtering of information after its online publication which raises concerns relating to the dissemination of false information and a threat to an individual's privacy. Such issues should be adequately addressed in the encouragement and use of citizen contributions in crisis response.
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    Privacy as cultural choice and resistance in the age of recommender systems
    (Routledge, 2018) Popescu, Mihaela; Department of Media and Visual Arts; Baruh, Lemi; Faculty Member; Department of Media and Visual Arts; College of Social Sciences and Humanities; 36113
    This chapter analyzes the role of recommender systems (RSs) and their surveillance mechanisms in the flow of culture in order to uncover some of the challenges that RSs pose to privacy theory. It also analyzes four roles that RSs perform in the organization, distribution, and management of culture, how practices of surveillance are essential to those roles, and with what consequences. The chapter examines why RSs illuminate conceptual problems with the notion of information privacy and how privacy might be related to cultural identity. It also examines the architecture of RSs, and describes their increasingly significant role in the circulation of culture by emphasizing how this role is possible in, and preconditioned on, the mobilization of an expansive apparatus of surveillance. A recommender system or engine is "any system that guides a user in a personalized way to interesting or useful objects in a large space of possible options or that produces such objects as output".
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    When sharing less means more: how gender moderates the impact of quantity of information shared in a social network profile on profile viewers' intentions about socialization
    (Taylor and Francis, 2014) Chisik, Yoram; Bisson, Christophe; Department of Media and Visual Arts; Department of Media and Visual Arts; Baruh, Lemi; Şenova, Başak; Faculty Member; Teaching Faculty; Department of Media and Visual Arts; College of Social Sciences and Humanities; College of Social Sciences and Humanities; 36113; N/A
    This study summarizes the results from a 2 (low vs. high information) x 2 (female vs. male profile) experiment that investigates the impact of quantity of information shared on a Social Network Site (SNS) profile on viewers' intentions to pursue further interactions with the profile owner. Quantity of information had no statistically significant effect on intentions to further socialize online. The two-way interaction between information quantity and profile gender was such that for male profiles more information increased profile viewers' intentions to further socialize with the profile owner, whereas for female profiles the opposite was the case. The three-way interactions among quantity of information, profile gender, and profile viewer's gender underline a tendency for male profile viewers to respond more positively to higher information shared by profiles from their own gender. For female viewers, this effect, although in the same direction, was smaller.
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    Communicating Turkish-Islamic identity in the aftermath of the Gaza flotilla raid: who is the "us" in "us" versus "them"?
    (Cambridge Univ Press, 2011) Popescu, Mihaela; Department of Media and Visual Arts; Baruh, Lemi; Faculty Member; Department of Media and Visual Arts; College of Social Sciences and Humanities; 36113
    On May 31, 2010, Israeli Defense Forces raided the ship Mavi Marmara, part of a six-vessel flotilla aiming to break the Israeli naval blockade of the Gaza Strip and to deliver supplies to Gaza. Using comments posted on Turkish online discussion forums in the aftermath of the raid that resulted in the death of nine passengers, this article analyzes how the incident was appropriated to negotiate between Turkishness and Islam as two alternative, yet coinciding forms of collective identity. Particularly, the article will compare different discursive strategies that were utilized in "general-interest" and "Islamic-leaning" online discussion groups. A deductive thematic analysis of 585 posts in general-interest and Islamic-leaning forums found significant differences in how metaphors of the body-blood, sacrifice, and martyrdom-as well as in-group/out-group comparisons were used in order to support a territorial-based nationalism versus a religion-based identity. The analysis also discusses the rhetoric that enabled discussants in general-interest forums to negotiate the tensions between the two collective identities.
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    Online anomaly detection with nested trees
    (IEEE-Inst Electrical Electronics Engineers Inc, 2016) Gökçesu, Kaan; Şimşek, Mustafa; Kozat, Süleyman S.; N/A; Department of Media and Visual Arts; Delibalta, İbrahim; Baruh, Lemi; PhD Student; Faculty Member; Department of Media and Visual Arts; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; 36113
    We introduce an online anomaly detection algorithm that processes data in a sequential manner. At each time, the algorithm makes a new observation, produces a decision, and then adaptively updates all its parameters to enhance its performance. The algorithm mainly works in an unsupervised manner since in most real-life applications labeling the data is costly. Even so, whenever there is a feedback, the algorithm uses it for better adaptation. The algorithm has two stages. In the first stage, it constructs a score function similar to a probability density function to model the underlying nominal distribution (if there is one) or to fit to the observed data. In the second state, this score function is used to evaluate the newly observed data to provide the final decision. The decision is given after the well-known thresholding. We construct the score using a highly versatile and completely adaptive nested decision tree. Nested soft decision trees are used to partition the observation space in a hierarchical manner. We adaptively optimize every component of the tree, i.e., decision regions and probabilistic models at each node as well as the overall structure, based on the sequential performance. This extensive in-time adaptation provides strong modeling capabilities; however, it may cause overfitting. To mitigate the overfitting issues, we first use the intermediate nodes of the tree to produce several subtrees, which constitute all the models from coarser to full extend, and then adaptively combine them. By using a real-life dataset, we show that our algorithm significantly outperforms the state of the art.
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    Role of personality traits in first impressions: an investigation of actual and perceived personality similarity effects on interpersonal attraction across communication modalities
    (Academic Press Inc Elsevier Science, 2018) Department of Psychology; Department of Media and Visual Arts; N/A; N/A; N/A; Cemalcılar, Zeynep; Baruh, Lemi; Kezer, Murat; Kamiloğlu, Roza Gizem; Niğdeli, Bihter; Faculty Member; Faculty Member; Master Student; Master Student; Master Student; Department of Psychology; Department of Media and Visual Arts; College of Social Sciences and Humanities; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; Graduate School of Business; 40374; 36113; N/A; N/A; N/A
    We investigate the effects of perceivers' impressions of others' personality traits on their social attraction, after a brief encounter face-to-face or via Facebook. We first examine the main effects of perceived personality traits. Next, we compare and contrast the effects of perceived- and actual-personality similarity through polynomial regressions with response surface analysis (RSA) accounting for dyads' respective levels of personality traits. Results suggest that RSA yield estronger effects of perceived similarity (vs. actual) on attraction. Perceivers are more attracted to targets who are "better versions" of themselves, yet too much discrepancy impede attraction, more so when face-to-face. (C) 2018 Elsevier Inc. All rights reserved.
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    Testing the BFI-2 in a non-weird community sample
    (Elsevier, 2021) Kezer, Murat; Soto, Christopher J.; Sumer, Nebi; John, Oliver P.; Department of Psychology; Department of Media and Visual Arts; Cemalcılar, Zeynep; Baruh, Lemi; Faculty Member; Faculty Member; Department of Psychology; Department of Media and Visual Arts; College of Social Sciences and Humanities; College of Social Sciences and Humanities; 40374; 36113
    We present two studies testing the validity and nomological properties of the Turkish adaptation of the Big Five Inventory-2 (BFI-2) using a university student sample and a nationally representative community sample of young adults aged 18-35. Findings from the university student sample replicate the psychometric properties of the BFI-2. Findings from the community sample replicate the factor structure and majority of the trait-outcome associations obtained from non-community samples in WEIRD populations. However, there were notable differences in terms of the internal consistency reliabilities of the personality domains, and some trait-outcome associations, specifically with outcomes that are germane to the Turkish culture.
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    Biased perceptions against female scientists affect intentions to get vaccinated for COVID-19
    (Sage Publications Ltd, 2022) Kuru, Ozan; Yıldırım, Kerem; N/A; Department of Media and Visual Arts; Department of Psychology; Department of International Relations; Doğan, İsminaz; Baruh, Lemi; Cemalcılar, Zeynep; Çarkoğlu, Ali; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Media and Visual Arts; Department of Psychology; Department of International Relations; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Administrative Sciences and Economics; N/A; 36113; 40374; 125588
    Based on role congruity theory, we investigated how gender bias may influence public attitudes toward the vaccine in Turkey. Using a between-subjects design, we tested whether an emphasis on the female versus the male scientist as the vaccine's inventor in a news story influenced attitudes about the BioNTech vaccine and vaccination intentions. Partly confirming role congruity theory, three-way interaction results from 665 participants demonstrated that among male participants with a stronger belief in traditional gender roles (compared to males with lower belief), the presence of the female inventor, either by herself or together with the male inventor, decreased the perceived efficacy and safety of the vaccine and reduced intentions to be vaccinated by the BioNTech vaccine. We did not observe such differences for women. These findings highlight how gender bias may influence individuals' information processing and decision making in a way that may have negative consequences for public health.
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    Communicating Turkish- Islamic identity in the aftermath of the gaza flotilla raid: who is the "us" in "us" versus "them"
    (Cambridge University Press, 2011) Popescu, Mihaela; Department of Media and Visual Arts; Baruh, Lemi; Faculty Member; Department of Media and Visual Arts; College of Social Sciences and Humanities; 36113
    On May 31, 2010, Israeli Defense Forces raided the ship Mavi Marmara, part of a six-vessel flotilla aiming to break the Israeli naval blockade of the Gaza Strip and to deliver supplies to Gaza. Using comments posted on Turkish online discussion forums in the aftermath of the raid that resulted in the death of nine passengers, this article analyzes how the incident was appropriated to negotiate between Turkishness and Islam as two alternative, yet coinciding forms of collective identity. Particularly, the article will compare different discursive strategies that were utilized in "general-interest" and "Islamic-leaning" online discussion groups. A deductive thematic analysis of 585 posts in general-interest and Islamicleaning forums found significant differences in how metaphors of the body-blood, sacrifice, and martyrdom-as well as in-group/out-group comparisons were used in order to support a territorial-based nationalism versus a religion-based identity. The analysis also discusses the rhetoric that enabled discussants in general-interest forums to negotiate the tensions between the two collective identities.
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    Online text classification for real life tweet analysis
    (Institute of Electrical and Electronics Engineers (IEEE), 2016) Yar, Ersin; Kozat, Süleyman S.; N/A; Department of Media and Visual Arts; Delibalta, İbrahim; Baruh, Lemi; PhD Student; Faculty Member; Department of Media and Visual Arts; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; 36113
    In this paper, we study multi-class classification of tweets, where we introduce highly efficient dimensionality reduction techniques suitable for online processing of high dimensional feature vectors generated from freely-worded text. As for the real life case study, we work on tweets in the Turkish language, however, our methods are generic and can be used for other languages as clearly explained in the paper. Since we work on a real life application and the tweets are freely worded, we introduce text correction, normalization and root finding algorithms. Although text processing and classification are highly important due to many applications such as emotion recognition, advertisement selection, etc., online classification and regression algorithms over text are limited due to need for high dimensional vectors to represent natural text inputs. We overcome such limitations by showing that randomized projections and piecewise linear models can be efficiently leveraged to significantly reduce the computational cost for feature vector extraction from the tweets. Hence, we can perform multi-class tweet classification and regression in real time. We demonstrate our results over tweets collected from a real life case study where the tweets are freely-worded, e.g., with emoticons, shortened words, special characters, etc., and are unstructured. We implement several well-known machine learning algorithms as well as novel regression methods and demonstrate that we can significantly reduce the computational complexity with insignificant change in the classification and regression performance./ Öz: Serbestçe kelimelere dökülmüş metinden üretilen yüksek boyutlu öznitelik vektörlerinin çevrimiçi işlenmesine uygun son derece etkin boyut azaltıcı tekniklerin tanıtıldıgı bu bildiride tweetlerin çok sınıflı sınıflandırması incelenmektedir. Gerçek hayat çalışması olarak, Türk dilindeki tweetler üzerinde çalışılmaktadır. Ancak, kullanılan yöntemler bildiride açıklandığı üzere geneldir ve diğer diller içinde kullanılabilir. Gerçek hayat uygulaması üzerinde çalışıldığından ve tweetlerin serbestçe yazılmış olmasından dolayı, metin düzeltme, düzgeleme ve kök bulma algoritmaları uygulanır. Metin işleme ve sınıflandırma duygu tanıması, reklam seçimi vb. gibi birçok uygulamada yüksek derecede önemli olmasına rağmen çevrimiçi metin sınıflandırma ve regresyon algoritmaları doğal metin girdilerini gösterimlemek için yüksek boyutlu vektörlere olan ihtiyaçtan dolayı sınırlıdır. Bu gibi kısıtlamaların üstesinden özellik vektörü özütlemesi için hesaplama maliyetini ciddi ölçüde azaltan rasgeleleştirilmiş izdüşümler ve parçalı doğrusal modelleri etkin bir biçimde kullanılarak gelinebilir. Bu sayede, gerçek zamanlı çok sınıflı tweet sınıflandırması ve regresyonu yapılabilir. Sonuçlar gerçek bir hayat çalışmasından toplanan serbestçe yazılmış yani ifadeler, kısaltılmış kelimeler, özel karakterler vb. ile ve düzensiz olan tweetler kullanılarak gösterilmektedir. Özgün regresyon yöntemleri ile iyi bilinen makine öğrenimi algoritmaları uygulanır ve sınıflandırma ve regresyon performansında önemli değişiklik olmadan hesaplama karma¸sıklığın önemli ölçüde azaltıldığı gös- terilir.