Emotional Tendency Analysis of Twitter Data Streams

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi

Aging Pipeline Infrastructure in the United States: How do a changing policy mix, issues of energy justice, and social media communication impact future risk analysis?

Over two and a half million miles of pipeline cross the United States today, half of which is over fifty years old and thus was designed, located, and debated without today’s modern environmental policies in place. Aging pipeline infrastructure, such as the (infamous in Michigan) Enbridge Line 5 pipeline underwater crossing at Michigan’s Straits of Mackinac, has undergone increased public scrutiny and risk analysis this past decade. This has led to the potential for policy changes in the historically stable energy services institution associated with pipeline infrastructure regulation. While policy process literature generally describes how policy changes over time, it is missing research on how new goals and new technology, such as energy justice and social media, impact agenda setting and decisions when added to the policy mix. This dissertation first investigates the evolving federal pipeline regime policy goals through an advanced policy mix analysis. Next, it argues that energy justice research can be advanced through deterministic approaches and analyses. Last, this dissertation uses a social network analysis to explain why aging pipelines are on today’s policy agenda through social network analysis. By understanding how the pipeline policy mix has changed over time, including through the addition of modern topics such as energy justice and modern technologies such as social media, policy and decision makers can improve prioritization of risk analysis for aging pipeline infrastructure

IT-Gestützte Kollaborative Kreativität

Companies and organizations must constantly evolve in order to stay competitive in the marketplace. An important role is played by innovations that ensure this continuous corporate success. Producing innovations depends strongly on creativity, which is why active support makes sense and is worthwhile. Creativity can be supported by information technology and is most effective in teams and groups. Collaboration and the consideration of different collaboration mechanisms play an equally important role in this context as the active support by information technology. This dissertation deals with the question of how information systems can be designed in order to use information technology to actively support creativity and that collaborative creativity processes are promoted. With the help of a systematic literature review, current creativity support systems were examined and the necessity of research was explained. A design-oriented approach was then used to develop and evaluate various approaches that address the research question. A total of 25 scientific articles were produced, five of which are included in this dissertation. Various conducted studies show the additional value of active support through information technology and provide design guidelines for better support of collaborative creativity.Unternehmen und Organisationen müssen sich ständig weiterentwickeln, um am Markt beständig zu sein und geschäftsfähig zu bleiben. Eine wichtige Rolle sind Innovationen, die diesen kontinuierlichen Unternehmenserfolg sicherstellen. Innovationen zu produzieren hängt stark von Kreativität ab, weshalb eine aktive Unterstützung sinnvoll und lohnenswert ist. Kreativität kann dabei durch Informationstechnik unterstützt werden und entfaltet vor allem in Teams und Gruppen ihre größte Wirkung. Kollaboration und die Betrachtung unterschiedlicher Kollaborationsmechanismen spielt in diesem Kontext gleichermaßen eine wichtige Rolle, wie die aktive Unterstützung durch Informatikstechnik. Die vorliegende Dissertation beschäftigt sich mit der Fragestellung, wie Informationssysteme gestaltet werden können, um einerseits Informationstechnik so einzusetzen, dass sie aktiv Kreativität unterstützt, andererseits so gestaltet werden sollte, dass kollaborative Kreativitätsprozesse gefördert werden. Mit Hilfe einer systematischen Literaturanalyse wurden dabei aktuelle Kreativitätsunterstützungsysteme untersucht und die Notwendigkeit der Forschung dargelegt. Mit einem gestaltungsorientierten Vorgehen wurden daraufhin unterschiedliche Ansätze entwickelt und evaluiert, die die Fragestellung adressieren. Dabei sind insgesamt 25 wissenschaftliche Artikel entstanden, von welchen fünf in diese Dissertation eingebunden sind. Unterschiedliche durchgeführte Studien zeigen daraufhin den Mehrwert von aktiver Unterstützung durch Informationstechnik auf und geben Gestaltungsrichtlinien zur besseren Unterstützung von kollaborativer Kreativität

Monitoring depressive symptoms using social media data

Social media data contains rich information about one's emotions and daily life experiences. In the recent decade, researchers have found links between people's behavior on social media platforms and their mental health status. However, little effort has been spent on mapping social media behaviors to the psychological processes underlying the psychopathological symptoms. Identifying these links may allow researchers to observe the trajectory of the illness through social media behaviors. The psychological processes examined in this thesis include affective patterns, distorted cognitive thinking and topics relevant to mental health status. In the first part of the thesis, we conducted two studies to explore methods to extract affective patterns from social media text. We demonstrated that mood fluctuations and mood transitions extracted from social media text reflect an individual’s depressive symptom level. In another study, we demonstrated that the affect from content not written by social media users themselves, such as quotes and lyrics, also reflects depressive symptoms, but the implications from these are different from content written by the users themselves. In the second part of the thesis, we identified distorted thinking from social media text. We found that these thinking patterns have a higher association with users' self-reported depressive symptom levels than affect extracted from users' text. In the last part of the thesis, we manually compiled topic dictionaries related to suicidal ideations according to the psychopathology literature. We found that users' suicidal risk levels can be estimated by using these topics. The estimation can be improved by combining these topics with results from a language model. The data-driven empirical studies in this thesis demonstrated that we can characterize the social media signals in a way that impacts our understanding of mental disorder symptoms. We blended data-driven methods such as machine learning, natural language processing and data science with theoretical insights from psychology

Wild Stories on the Internet: Hiker Accounts of Living Among Wildlife on the Appalachian Trail

The Appalachian Trail is the world’s longest hiking-only trail, covering roughly 2,200 miles of forest, mountains, ridges and plains. Each year a few thousand people set out to hike the entire length of the trail, estimated to take between five and seven months to complete. Numerous species of autonomous animals – wildlife – dwell on and around the trail, and it is the encounters that happen between these human and nonhuman animals that are the focus of this thesis. The research presented here is based wholly around narratives posted online as blogs by 166 Appalachian Trail hikers during the years 2015 and 2016. These narratives provide an insight into how hikers related to the self-directed animals that they temporarily shared a home with. Several recurring themes emerged to form the basis of the thesis chapters: many hikers viewed their trek as akin to a pilgrimage, which informed their perception of the animals that they encountered; American Black Bears (Ursus americanus), viewed as emblematic of the trail wilderness, made dwelling on the trail satisfyingly risky; hikers experienced strong feelings about some animals as being cute, and about others as being disgusting; along a densely wooded trail, experience of animals was often primarily auditory; the longer that they spent on the trail, the more hikers themselves experienced a sense of becoming wild. Through an analysis of these themes, it became clear that hikers thought about trail animals as meaning, or representing, something, in the context of their own narrative journey. Yet at the same time, the autonomous animals on the trail were experienced in complex, multifaceted, and sometimes even contradictory ways that succeeded in making them interesting to hikers in ways that they may never have anticipated

The Democratization of Artificial Intelligence

After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms

The Democratization of Artificial Intelligence: Net Politics in the Era of Learning Algorithms

After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms