Like! Feelings and Friendship In The Age of Algorithms

This dissertation frames the Like button as a locus through which to analyze the politics of social media. The Like button is more than a simple signal of approval or support; instead, the button makes human reactions machine-readable so that pressing Like communicates simultaneously with social media users and with the social media platform. Rendering feelings as information is an act of de-contextualization that enables feelings, or their approximations, to operate at new scales. As a result of this transformation, the Like button gets bound up in some very big problems, including addiction, competition, sensationalism, and manipulation. I treat social media as infrastructure, marking out the ways that social media is part of the built environment and provides the grounds for social interaction, work, commerce, networking, political organizing, and more. Chapter 1 introduces the concept of affective infrastructure, or infrastructure that is affectively responsive, capable of sensing people&rsquo;s feelings and acting upon that information. Chapter 2 traces the origins of the idea of affective infrastructure back to mid-century radio research and re-interprets the media effects tradition of mass communication research in the United States as centrally concerned with media affects, or the ability of communication technologies to shape people&rsquo;s feelings, beliefs, and subsequent actions. Chapter 3 comparatively analyzes the history of interaction design on Facebook, Instagram, and Twitter and shows how engagement emerged as the organizing paradigm of social media. I argue that the focus on individual engagement comes into conflict with other values, such as well-being, the public good, and democracy. Chapter 4 investigates everyday experiences of affective infrastructure through interviews with artists using Instagram. I argue that social media creates infrastructural feeling rules, or styles of emotional expression reinforced with access to visibility in the form of algorithmic recommendations. Infrastructural feeling rules not only shape what people share on social media but also their sense of self. The concluding chapter reflects on the role of engagement in structuring social interactions and shaping subjectivity, in addition to laying out four provocations for social research in the age of algorithms.</p

Destination image online analyzed through user generated content: a systematic literature review

Destination Image is a concept that has been studied for a long time in tourism research. The question of how a destination is perceived by tourists and potential new guests is an important insight, especially for local tourism managers, in order to evaluate the implemented strategies and to plan further tactics. Since the last two decades, due to a drastic digitalization, tourism research is now increasingly examining the Destination Image online. This creates new challenges in the selection of sources, methods, and in data collection. The aim of the present study was to systematically capture the approach to analyze the online Destination Image through User Generated Content using studies from the last ten years. Therefore, a Systematic Literature Review on primary research from academic databases was conducted. As a summary of the findings, a conceptual model was developed, based on the insights of the studies in the dataset, to contribute a guidance for the preparation phase of future online Destination Image research. In short, the main findings are: TripAdvisor.com is the main source for online Destination Image analysis. Researchers recommend using the help of software and programming languages to collect and analyzed the data. Equally to earlier Destination Image studies, the main methods applied in online Destination Image analysis are quantitative content analysis, qualitative content analysis and sentiment analysis. In combination with the examination of cognitive and affective factors, co-occurrence analysis, and correlation analysis. The present study has several limitations, which are: the loss of detail information due to reducing the studies to comparable key parameters, the absence of Anglo-American studies, due to the database selection as well as the lack of quality testing of the studies included.A Destination Image é um conceito que tem sido estudado há muito tempo na investigação turística. A questão de como o destino é visto pelos turistas e pelos potenciais novos hóspedes é uma perspectiva importante, especialmente para os gestores de turismo da região, a fim de avaliar as estratégias implementadas e de planear novas tácticas. Desde as últimas duas décadas, ocorreu uma digitalização drástica, a investigação turística adaptou-se a este fenómeno e está agora a estudar cada vez mais a imagem do destino online. Esta alteração criou novos desafios na selecção de fontes, métodos, e na recolha de dados. O objetivo do presente trabalho foi o de captar, de forma sistemática, as abordagens consideradas para analisar a imagem do destino online utilizando estudos dos últimos dez anos. Para este efeito, os estudos primários dos anos 2010-2020 das bases de dados académicos Web of Science, ProQuest e b-on, foram recolhidos utilizando palavras-chave de pesquisa pré-definidas. O grupo de artigos obtidos como resultado foram subsequentemente sujeitos a avaliação de eligibilidade, como recomendado por Moher et al. (2009). Isto significa que os estudos que não cumpriam os critérios pré-definidos foram excluídos. Os critérios de inclusão foram: O trabalho académico tinha de ser uma referência primária de uma revista científica, escrita em inglês e a amostra analisada tinha de ter uma origem associada à comunicação nas social media online. Posteriormente, os restantes 35 artigos foram transferidos para uma base de dados utilizando uma matriz de codificação. A matriz de codificação foi concebida para capturar os parâmetros-chave de cada estudo primário de uma forma padronizada e, portanto, comparável. Foi considerada informação geral, como o ano, localização e revista publicada, bem como informação temática específica, como o campo do turismo pesquisado e os meios analisados, juntamente com as categorias referentes à metodologia considerada, as ferramentas utilizadas e os resultados obtidos. A base de dados resultante foi então utilizada para obter declarações sobre a abordagem metodológica utilizada na análise da imagem de destinos online. Como resumo dos resultados, foi desenvolvido um modelo conceptual, baseado nos conhecimentos obtidos a partir do grupo de artigos, que constituiu o conjunto de dados para análise, para contribuir com um guião para a fase de preparação de uma futura investigação sobre imagem dos destinos online. Em resumo, as principais conclusões são: TripAdvisor.com é a principal fonte para a análise da imagem de destinos online. Os investigadores recomendam a utilização da ajuda de software e linguagens de programação para a recolha e análise dos dados. À semelhança de estudos anteriores de Destination Image, os principais métodos aplicados na análise imagem dos destinos online são a análise quantitativa do conteúdo, a análise qualitativa do conteúdo e a análise dos sentimentos. Em combinação com a análise dos fatores cognitivos e afectivos, análise de co-ocorrência, e análise de correlação. O presente estudo tem várias limitações. Que são: a perda de informação detalhada devido à redução dos estudos a parâmetros-chave comparáveis, a ausência de estudos anglo-americanos, devido à selecção do banco de dados, bem como a falta de testes de qualidade dos estudos incluídos.(TurExperience - Tourist experiences' impacts on the destination image: searching for new opportunities to the Algarve”)

Assessing a Collaborative Online Environment for Music Composition

The current pilot study tested the effectiveness of an e-learning environment built to enable students to compose music collaboratively. The participants interacted online by using synchronous and asynchronous resources to develop a project in which they composed a new music piece in collaboration. After the learning sessions, individual semi-structured interviews with the participants were conducted to analyze the participants\u2019 perspectives regarding the e-learning environment\u2019s functionality, the resources of the e-learning platform, and their overall experience with the e-learning process. Qualitative analyses of forum discussions with respect to metacognitive dimensions, and semi-structured interview transcriptions were performed. The findings showed that the participants successfully completed the composition task in the virtual environment, and that they demonstrated the use of metacognitive processes. Moreover, four themes were apparent in the semi-structured interview transcriptions: Teamwork, the platform, face-to-face/online differences, and strengths/weaknesses. Overall, the participants exhibited an awareness of the potential of the online tools, and the task performed. The results are discussed in consideration of metacognitive processes, and the following aspects that rendered virtual activity effective for learning: The learning environment, the platform, the technological resources, the level of challenge, and the nature of the activity. The possible implications of the findings for research on online collaborative composition are also considered

Attachment Styles and the Impact of Extradyadic Behaviors in Polyamorous Relationships

This study examines the emotional experience of extradyadic behavior (EDB) in polyamorous relationships through an attachment lens. Estimated prevalence rates suggest that one in nine people in the United States have engaged in polyamory at some point in their life (Moors et al., 2021). Attachment theory addresses with anxiety and separation in relationships, feelings likely aroused by extradyadic behavior, yet it has been minimally applied to this population (Moors et al., 2015, 2019). The current study utilized a phenomenological approach where eight participants were interviewed, examining the emotional experience of EDB in polyamorous relationships through an attachment lens. The study assessed each person’s adult attachment styles, by administering an Experiences in Close Relationships-Short Form (ECR-S) measure, and conducting a semi-structed interview of the participants’ experiences of EDB. The results suggested that those with an anxious-preoccupied attachment style expressed more avoidance when discussing their romantic relationships compared to other attachment styles. Additionally, individuals with fearful-avoidant attachment styles may have a decreased tolerance for ambivalence as compared to other attachment styles. The results also suggest that individuals in polyamorous relationships have increased capacity for increased open communication, tolerating ambivalence within relationships, and for developing a differentiated sense of self. Finally, results suggested there is a large role of society and internalized monogamous views that influence individuals’ experiences of polyamory. This research could be a reference for future research with more participants, and further inform clinical work with polyamorous clients

Computing point-of-view : modeling and simulating judgments of taste

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 153-163).People have rich points-of-view that afford them the ability to judge the aesthetics of people, things, and everyday happenstance; yet viewpoint has an ineffable quality that is hard to articulate in words, let alone capture in computer models. Inspired by cultural theories of taste and identity, this thesis explores end-to-end computational modeling of people's tastes-from model acquisition, to generalization, to application- under various realms. Five aesthetical realms are considered-cultural taste, attitudes, ways of perceiving, taste for food, and sense-of-humor. A person's model is acquired by reading her personal texts, such as a weblog diary, a social network profile, or emails. To generalize a person model, methods such as spreading activation, analogy, and imprimer supplementation are applied to semantic resources and search spaces mined from cultural corpora. Once a generalized model is achieved, a person's tastes are brought to life through perspective-based applications, which afford the exploration of someone else's perspective through interactivity and play. The thesis describes model acquisition systems implemented for each of the five aesthetical realms.(cont.) The techniques of 'reading for affective themes' (RATE), and 'culture mining' are described, along with their enabling technologies, which are commonsense reasoning and textual affect analysis. Finally, six perspective-based applications were implemented to illuminate a range of real-world beneficiaries to person modeling-virtual mentoring, self-reflection, and deep customization.by Xinyu Hugo Liu.Ph.D

The Digital Badge Initiative and its Implications for First-Year Writing

College students seek degrees to obtain employment in their field of interest, however, as the 21st century progresses, employers are often requiring specific skills in addition to degrees and transcripts. As students graduate with their Associates, Bachelors, and Graduate degrees, they plan to present these degrees as sufficient evidence of their qualifications. However, there is recent criticism of college degrees as evidence of qualifications. A beneficial alternative for students would be digital badges. A digital badge is a visual representation that signifies a specific achievement with detailed metadata attached. Digital badges in first-year writing courses would benefit students as they develop specific writing and critical thinking skills as prompted by the curriculum. First-year writing digital badges can include: researching, synthesizing, writing process, constructing authority, etc. as deemed appropriate by the instructor. Ultimately, students will display their badges on their resumes, CVs, or any other document verifying their achievements

Depression Detection Using Stacked Autoencoder from Facial Features and NLP

Depression has become one of the most common mental illnesses in the past decade, affecting millions of patients and their families. However, the methods of diagnosing depression almost exclusively rely on questionnaire-based interviews and clinical judgments of symptom severity, which are highly dependent on doctors’ experience and makes it a labor-intensive work. This research work aims to develop an objective and convenient method to assist depression detection using facial features as well as textual features. Most of the people conceal their depression from everyone. So, an automated system is required that will pick out them who are dealing with depression. In this research, different research work focused for detecting depression are discussed and a hybrid approach is developed for detecting depression using facial as well as textual features. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Natural Language Processing, Stacked Deep Auto Encoder with Random forest (RF) classifier and fuzzy logic based on multi-feature depression detection system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. The result analysis shows that the developed technique significantly advantages over existing methods

How to Relax in Stressful Situations: A Smart Stress Reduction System

Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual's health, well-being and socio-economic situation. Stress management application development for wearable smart devices is a growing market. The use of wearable smart devices and biofeedback for individualized real-life stress reduction interventions has received less attention. By using our unobtrusive automatic stress detection system for use with consumer-grade smart bands, we first detected stress levels. When a high stress level is detected, our system suggests the most appropriate relaxation method by analyzing the physical activity-based contextual information. In more restricted contexts, physical activity is lower and mobile relaxation methods might be more appropriate, whereas in free contexts traditional methods might be useful. We further compared traditional and mobile relaxation methods by using our stress level detection system during an eight day EU project training event involving 15 early stage researchers (mean age 28; gender 9 Male, 6 Female). Participants' daily stress levels were monitored and a range of traditional and mobile stress management techniques was applied. On day eight, participants were exposed to a 'stressful' event by being required to give an oral presentation. Insights about the success of both traditional and mobile relaxation methods by using the physiological signals and collected self-reports were provided