The aesthetic turn: exploring the religious dimensions of digital technology

The arena for developing digital technology has undergone an aesthetic turn, broadening the focus from a functionalist approach producing centralized systems in the 1970s and 1980s to an increased awareness of the aesthetic aspects of the individual user’s interaction with technology in the 1990s and 2000s. Within the academic research fields studying digital technology (e.g. Human-Computer Interaction and Interaction Design) the aesthetic turn has resulted in a shift from a strong emphasis on user behaviour to an increased interest in aesthetic perspectives on the role of the designer, the design process, and the design material. Within these fields, aesthetics has often been interpreted as belonging to the realm of the individual; personal experiences such as pleasure, engagement, and emotions have been emphasized in both technology development and technology research. Aesthetics is not, however, only an individual phenomenon but also has relational and structural components that need to be acknowledged. Structural aspects of aesthetics condition the possibilities for individuals interacting with digital technology. Thus, the tension between individual and relational aspects of aesthetics in digital technology also reflects a tension between freedom and limitation; between change and permanence; between destabilizing and stabilizing forces.Such a broadened understanding of aesthetics offers a model of digital technology that roughly corresponds to Mark C. Taylor’s definition of religion. Taylor argues that religion is constituted by, on the one hand, a figuring moment characterized by structural stability and universality, and, on the other hand, a disfiguring moment characterized by disruption, particularity, and change. The purpose of this paper is to discuss the aesthetic turn and Taylor’s definition of religion to illustrate similarities between the two, suggesting possible religious dimensions of digital technology and how that can inform our understanding of people’s interaction with digital technology.

사진의 메타데이터를 이용한 프라이버시 수준 측정과 온라인 공유 행동 연구

학위논문 (석사)-- 서울대학교 대학원 사회과학대학 언론정보학과, 2017. 8. 이준환.스마트폰 보급이 확산되면서 스마트폰으로 사진을 찍는 사람이 많아졌는데, 누군가가 스마트폰에 저장되어 있는 내 사진을 우연히 보거나 스마트폰 조작 실수로 다른 사진을 온라인에 잘못 공유해 프라이버시를 침해당할 수 있다. 특히 온라인에 공유되는 사진은 누구와 함께 어디에서 무엇을 하고 있는지와 같이 많은 것을 파악할 수 있는 정보를 담고 있기 때문에 사진이 외부로 잘못 유출될 경우 프라이버시 침해는 평소보다 더욱 크게 느껴진다. 따라서 모바일 애플리케이션이 자동으로 스마트폰 갤러리에 있는 사진들의 프라이버시 수준을 측정하고, 프라이버시 수준이 높은 사진은 이용자가 온라인에 공유할 때 프라이버시 수준이 높은 사진임을 한 번 더 확인할 수 있게 해준다면, 사용자 입장에서는 훨씬 편리하고 안전하게 사진을 관리할 수 있을 것이라는 생각에서 이 연구가 시작되었다. 따라서 본 연구에서는 사진의 어떤 요인이 직접적으로 프라이버시 수준을 높게 만드는지, 그리고 온라인 공개를 하지 못하게 만드는지를 밝혔다. 또한 연구용 애플리케이션을 제작하여 실험 참여자 본인의 스마트폰에 저장되어있는 사진으로 실험을 했기 때문에 보다 정확도 높고 신뢰도 있는 연구결과를 도출하였다. 사진 속 인물의 수가 많을수록, 인물의 얼굴 크기가 클수록, 사진에 가족 얼굴, 연인 얼굴, 본인 얼굴이 있으면 프라이버시 수준이 높아지고, 사진을 찍은 시간 역시 프라이버시 수준에 영향을 미치는 것으로 나타났다. 온라인 공개 가능 여부도 프라이버시 수준과 비슷하게 사진 속 인물의 수가 많을수록, 사진 속 인물의 얼굴 크기가 클수록, 그리고 사진 속에 가족의 얼굴이 있거나, 연인의 얼굴이 있거나, 본인의 얼굴이 있으면 온라인 공개를 적게 하는 것으로 나타났다. 또한 오전 3시~9시 그리고 오전 9시~오후 3시에 찍힌 사진은 온라인 공개를 적게 하는 것으로 나타났다. 한 가지 흥미로운 점은 프라이버시 수준이 높으나 온라인 공개 가능하다고 판단한 사진이 몇 장 있었는데, 이러한 사진들은 대부분 무언가를 자랑하기 위한 목적이 많았다. 일반적으로 프라이버시 수준에 따라 온라인 공개 가능 여부가 판단되지만, 특정 몇 개의 사진의 경우 자신을 보여주기 위한 전략적 선택이 적용되어 온라인 공개 가능 여부가 판단되는 것으로 해석된다. 따라서 사진 속에 긍정적인 요인이 있는지를 보기 위해 피사체가 잘 나온 정도나 사진의 전체적인 매력도를 파악할 수 있는 머신러닝(기계학습) 기법을 동원한 추가분석의 가능성을 제시하였다. 또한 본 연구 결과를 토대로 사진의 프라이버시 수준을 판단하여 프라이버시 수준이 높은 사진은 쉽게 유출되지 않도록 보호해주는 기능을 탑재한 애플리케이션 제작이 가능할 것으로 기대된다.1. 문제 제기 1 2. 기존 연구 검토 6 2.1. 프라이버시 개념 6 2.2. 자기 노출과 프라이버시 보호의 전략적 선택 7 2.3. 프라이버시 염려와 보호 행동의 관계 10 2.4. 알고리즘을 이용한 사진 프라이버시 분석 연구 14 2.5. 메타데이터를 이용한 사진 프라이버시 수준 측정 17 2.5.1. 사진 속 인물 분석 20 2.5.2. 사진이 찍힌 장소와 시간 분석 24 2.6. 온라인 사진 공유 행동 26 3. 연구 문제 및 연구 모형 30 4. 사전조사 34 4.1. 조사대상 34 4.2. 변인의 측정 및 분석결과 36 4.3. 사전조사의 의의 및 한계 39 5. 스마트폰 앱을 이용한 실험 40 5.1. 실험 설계 40 5.2. 실험 대상 44 5.3. 주요 개념 정의 및 측정 방법 45 6. 연구 결과 47 6.1. 프라이버시 수준 측정 47 6.2. 온라인 공개 가능 여부 판단 53 6.3. 프라이버시 수준이 높지만 온라인에 공개 가능한 사진의 특징들 59 7. 논의 61 8. 결론 66 참고문헌 70 Abstract 76Maste

Sensing Human Sentiment via Social Media Images: Methodologies and Applications

abstract: Social media refers computer-based technology that allows the sharing of information and building the virtual networks and communities. With the development of internet based services and applications, user can engage with social media via computer and smart mobile devices. In recent years, social media has taken the form of different activities such as social network, business network, text sharing, photo sharing, blogging, etc. With the increasing popularity of social media, it has accumulated a large amount of data which enables understanding the human behavior possible. Compared with traditional survey based methods, the analysis of social media provides us a golden opportunity to understand individuals at scale and in turn allows us to design better services that can tailor to individuals’ needs. From this perspective, we can view social media as sensors, which provides online signals from a virtual world that has no geographical boundaries for the real world individual's activity. One of the key features for social media is social, where social media users actively interact to each via generating content and expressing the opinions, such as post and comment in Facebook. As a result, sentiment analysis, which refers a computational model to identify, extract or characterize subjective information expressed in a given piece of text, has successfully employs user signals and brings many real world applications in different domains such as e-commerce, politics, marketing, etc. The goal of sentiment analysis is to classify a user’s attitude towards various topics into positive, negative or neutral categories based on textual data in social media. However, recently, there is an increasing number of people start to use photos to express their daily life on social media platforms like Flickr and Instagram. Therefore, analyzing the sentiment from visual data is poise to have great improvement for user understanding. In this dissertation, I study the problem of understanding human sentiments from large scale collection of social images based on both image features and contextual social network features. We show that neither visual features nor the textual features are by themselves sufficient for accurate sentiment prediction. Therefore, we provide a way of using both of them, and formulate sentiment prediction problem in two scenarios: supervised and unsupervised. We first show that the proposed framework has flexibility to incorporate multiple modalities of information and has the capability to learn from heterogeneous features jointly with sufficient training data. Secondly, we observe that negative sentiment may related to human mental health issues. Based on this observation, we aim to understand the negative social media posts, especially the post related to depression e.g., self-harm content. Our analysis, the first of its kind, reveals a number of important findings. Thirdly, we extend the proposed sentiment prediction task to a general multi-label visual recognition task to demonstrate the methodology flexibility behind our sentiment analysis model.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Investigating Obfuscation as a Tool to Enhance Photo Privacy on Social Networks Sites

Photos which contain rich visual information can be a source of privacy issues. Some privacy issues associated with photos include identification of people, inference attacks, location disclosure, and sensitive information leakage. However, photo privacy is often hard to achieve because the content in the photos is both what makes them valuable to viewers, and what causes privacy concerns. Photo sharing often occurs via Social Network Sites (SNSs). Photo privacy is difficult to achieve via SNSs due to two main reasons: first, SNSs seldom notify users of the sensitive content in their photos that might cause privacy leakage; second, the recipient control tools available on SNSs are not effective. The only solution that existing SNSs (e.g., Facebook, Flickr) provide is control over who receives a photo. This solution allows users to withhold the entire photo from certain viewers while sharing it with other viewers. The idea is that if viewers cannot see a photo, then privacy risk is minimized. However, withholding or self-censoring photos is not always the solution people want. In some cases, people want to be able to share photos, or parts of photos, even when they have privacy concerns about the photo. To provide better online photo privacy protection options for users, we leverage a behavioral theory of privacy that identifies and focuses on two key elements that influence privacy -- information content and information recipient. This theory provides a vocabulary for discussing key aspects of privacy and helps us organize our research to focus on the two key parameters through a series of studies. In my thesis, I describe five studies I have conducted. First, I focus on the content parameter to identify what portions of an image are considered sensitive and therefore are candidates to be obscured to increase privacy. I provide a taxonomy of content sensitivity that can help designers of photo-privacy mechanisms understand what categories of content users consider sensitive. Then, focusing on the recipient parameter, I describe how elements of the taxonomy are associated with users\u27 sharing preferences for different categories of recipients (e.g., colleagues vs. family members). Second, focusing on controlling photo content disclosure, I invented privacy-enhancing obfuscations and evaluated their effectiveness against human recognition and studied how they affect the viewing experience. Third, after discovering that avatar and inpainting are two promising obfuscation methods, I studied whether they were robust when de-identifying both familiar and unfamiliar people since viewers are likely to know the people in OSN photos. Additionally, I quantified the prevalence of self-reported photo self-censorship and discovered that privacy-preserving obfuscations might be useful for combating photo self-censorship. Gaining sufficient knowledge from the studies above, I proposed a privacy-enhanced photo-sharing interface that helps users identify the potential sensitive content and provides obfuscation options. To evaluate the interface, I compared the proposed obfuscation approach with the other two approaches – a control condition that mimics the current Facebook photo-sharing interface and an interface that provides a privacy warning about potentially sensitive content. The results show that our proposed system performs better over the other two in terms of reducing perceived privacy risks, increasing willingness to share, and enhancing usability. Overall, our research will benefit privacy researchers, online social network designers, policymakers, computer vision researchers, and anyone who has or wants to share photos online