Whose Advantage? Measuring Attention Dynamics across YouTube and Twitter on Controversial Topics

The ideological asymmetries have been recently observed in contested online spaces, where conservative voices seem to be relatively more pronounced even though liberals are known to have the population advantage on digital platforms. Most prior research, however, focused on either one single platform or one single political topic. Whether an ideological group garners more attention across platforms and/or topics, and how the attention dynamics evolve over time, have not been explored. In this work, we present a quantitative study that links collective attention across two social platforms -- YouTube and Twitter, centered on online activities surrounding popular videos of three controversial political topics including Abortion, Gun control, and Black Lives Matter over 16 months. We propose several sets of video-centric metrics to characterize how online attention is accumulated for different ideological groups. We find that neither side is on a winning streak: left-leaning videos are overall more viewed, more engaging, but less tweeted than right-leaning videos. The attention time series unfold quicker for left-leaning videos, but span a longer time for right-leaning videos. Network analysis on the early adopters and tweet cascades show that the information diffusion for left-leaning videos tends to involve centralized actors; while that for right-leaning videos starts earlier in the attention lifecycle. In sum, our findings go beyond the static picture of ideological asymmetries in digital spaces and provide a set of methods to quantify attention dynamics across different social platforms.Comment: Accepted into ICWSM 2022. 11-page main paper and 11-page appendi

Student Engagement in Aviation Moocs: Identifying Subgroups and Their Differences

The purpose of this study was to expand the current understanding of learner engagement in aviation-related Massive Open Online Courses (MOOCs) through cluster analysis. MOOCs, regarded for their low- or no-cost educational content, often attract thousands of students who are free to engage with the provided content to the extent of their choosing. As online training for pilots, flight attendants, mechanics, and small unmanned aerial system operators continues to expand, understanding how learners engage in optional aviation-focused, online course material may help inform course design and instruction in the aviation industry. In this study, Moore’s theory of transactional distance, which posits psychological or communicative distance can impede learning and success, was used as a descriptive framework for analysis. Archived learning analytics datasets from two 2018 iterations of the same small unmanned aerial systems MOOC were cluster-analyzed (N = 1,032 and N = 4,037). The enrolled students included individuals worldwide; some were affiliated with the host institution, but most were not. The data sets were cluster analyzed separately to categorize participants into common subpopulations based on discussion post pages viewed and posts written, video pages viewed, and quiz grades. Subgroup differences were examined in days of activity and record of completion. Pre- and postcourse survey data provided additional variables for analysis of subgroup differences in demographics (age, geographic location, education level, employment in the aviation industry) and learning goals. Analysis of engagement variables revealed three significantly different subgroups for each MOOC. Engagement patterns were similar between MOOCs for the most and least engaged groups, but differences were noted in the middle groups; MOOC 1’s middle group had a broader interest in optional content (both in discussions and videos); whereas MOOC 2’s middle group had a narrower interest in optional discussions. Mandatory items (Mandatory Discussion or Quizzes) were the best predictors in classifying subgroups for both MOOCs. Significant associations were found between subgroups and education levels, days of activity, and total quiz scores. This study addressed two known problems: a lack of information on student engagement in aviation-related MOOCs, and more broadly, a growing imperative to examine learners who utilize MOOCs but do not complete them. This study served as an important first step for course developers and instructors who aim to meet the diverse needs of the aviation-education community

Inferring User Needs and Tasks from User Interactions

The need for search often arises from a broad range of complex information needs or tasks (such as booking travel, buying a house, etc.) which lead to lengthy search processes characterised by distinct stages and goals. While existing search systems are adept at handling simple information needs, they offer limited support for tackling complex tasks. Accurate task representations could be useful in aptly placing users in the task-subtask space and enable systems to contextually target the user, provide them better query suggestions, personalization and recommendations and help in gauging satisfaction. The major focus of this thesis is to work towards task based information retrieval systems - search systems which are adept at understanding, identifying and extracting tasks as well as supporting user’s complex search task missions. This thesis focuses on two major themes: (i) developing efficient algorithms for understanding and extracting search tasks from log user and (ii) leveraging the extracted task information to better serve the user via different applications. Based on log analysis on a tera-byte scale data from a real-world search engine, detailed analysis is provided on user interactions with search engines. On the task extraction side, two bayesian non-parametric methods are proposed to extract subtasks from a complex task and to recursively extract hierarchies of tasks and subtasks. A novel coupled matrix-tensor factorization model is proposed that represents user based on their topical interests and task behaviours. Beyond personalization, the thesis demonstrates that task information provides better context to learn from and proposes a novel neural task context embedding architecture to learn query representations. Finally, the thesis examines implicit signals of user interactions and considers the problem of predicting user’s satisfaction when engaged in complex search tasks. A unified multi-view deep sequential model is proposed to make query and task level satisfaction prediction

The Democratization of News - Analysis and Behavior Modeling of Users in the Context of Online News Consumption

Die Erfindung des Internets ebnete den Weg für die Demokratisierung von Information. Die Tatsache, dass Nachrichten für die breite Öffentlichkeit zugänglicher wurden, barg wichtige politische Versprechen, wie zum Beispiel das Erreichen von zuvor uninformierten und daher oft inaktiven Bürgern. Diese konnten sich nun dank des Internets tagesaktuell über das politische Geschehen informieren und selbst politisch engagieren. Während viele Politiker und Journalisten ein Jahrzehnt lang mit dieser Entwicklung zufrieden waren, änderte sich die Situation mit dem Aufkommen der sozialen Online-Netzwerke (OSN). Diese OSNs sind heute nahezu allgegenwärtig – so beziehen inzwischen $67\%$ der Amerikaner zumindest einen Teil ihrer Nachrichten über die sozialen Medien. Dieser Trend hat die Kosten für die Veröffentlichung von Inhalten weiter gesenkt. Dies sah zunächst nach einer positiven Entwicklung aus, stellt inzwischen jedoch ein ernsthaftes Problem für Demokratien dar. Anstatt dass eine schier unendliche Menge an leicht zugänglichen Informationen uns klüger machen, wird die Menge an Inhalten zu einer Belastung. Eine ausgewogene Nachrichtenauswahl muss einer Flut an Beiträgen und Themen weichen, die durch das digitale soziale Umfeld des Nutzers gefiltert werden. Dies fördert die politische Polarisierung und ideologische Segregation. Mehr als die Hälfte der OSN-Nutzer trauen zudem den Nachrichten, die sie lesen, nicht mehr ($54\%$ machen sich Sorgen wegen Falschnachrichten). In dieses Bild passt, dass Studien berichten, dass Nutzer von OSNs dem Populismus extrem linker und rechter politischer Akteure stärker ausgesetzt sind, als Personen ohne Zugang zu sozialen Medien. Um die negativen Effekt dieser Entwicklung abzumildern, trägt meine Arbeit zum einen zum Verständnis des Problems bei und befasst sich mit Grundlagenforschung im Bereich der Verhaltensmodellierung. Abschließend beschäftigen wir uns mit der Gefahr der Beeinflussung der Internetnutzer durch soziale Bots und präsentieren eine auf Verhaltensmodellierung basierende Lösung. Zum besseren Verständnis des Nachrichtenkonsums deutschsprachiger Nutzer in OSNs, haben wir deren Verhalten auf Twitter analysiert und die Reaktionen auf kontroverse - teils verfassungsfeindliche - und nicht kontroverse Inhalte verglichen. Zusätzlich untersuchten wir die Existenz von Echokammern und ähnlichen Phänomenen. Hinsichtlich des Nutzerverhaltens haben wir uns auf Netzwerke konzentriert, die ein komplexeres Nutzerverhalten zulassen. Wir entwickelten probabilistische Verhaltensmodellierungslösungen für das Clustering und die Segmentierung von Zeitserien. Neben den Beiträgen zum Verständnis des Problems haben wir Lösungen zur Erkennung automatisierter Konten entwickelt. Diese Bots nehmen eine wichtige Rolle in der frühen Phase der Verbreitung von Fake News ein. Unser Expertenmodell - basierend auf aktuellen Deep-Learning-Lösungen - identifiziert, z. B., automatisierte Accounts anhand ihres Verhaltens. Meine Arbeit sensibilisiert für diese negative Entwicklung und befasst sich mit der Grundlagenforschung im Bereich der Verhaltensmodellierung. Auch wird auf die Gefahr der Beeinflussung durch soziale Bots eingegangen und eine auf Verhaltensmodellierung basierende Lösung präsentiert

Factors influencing the popularity of YouTube videos and users’ decisions to watch them

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.YouTube has substantial impact on modern society as the second most popular website in the world. Despite its sustained popularity, little is known about which types of video are most viewed and the reasons why people choose to watch them. This research critically analyses the sample of videos provided by the YouTube API, then uses the metrics associated with these videos to help assess which types of YouTube video are popular. It also harnesses a questionnaire of mainly UK teacher education graduate YouTube users to investigate which factors influence decisions to watch YouTube videos. This was a convenience sample selected to achieve a high response rate, which it achieved (81%), minimising non-response bias. The video lists provided by the YouTube API were not random samples but contained a wide range of types of video (including both popular and unpopular), except that older videos were avoided. There were substantial differences between categories in the average properties of the videos returned and the proportion of videos returned on multiple days. The most popular categories from the YouTube metadata collected based on average view counts are varied: From TV, Best of, Animation and How-to. Cause-based video categories tended to be unpopular. Video popularity did not seem to be affected by video duration, on average. Users are more likely to interact with (comment, like, dislike) videos that are useful or supporting in some way. Videos that are interacted with more are not always more popular, with subject content affecting this relationship. In addition, high view counts associated with fewer likes, dislikes and comments per view, suggesting that indicators of popularity may not attract new viewers. The most popular categories with survey respondents were slightly different, partly reflecting their educational background (e.g., Education videos), and there were some (stereotypical) gender differences in the most popular categories. Respondents rarely believed that they were influenced by a video’s popularity or evidence of other users’ reactions to it when deciding to watch the video. Instead, they were most likely to be influenced by content-related factors, such as a video’s title and thumbnail picture. Despite previous research showing that people can be influenced by the opinions and watching habits of others, respondents claimed to be little influenced by this. Nevertheless, they frequently reported watching videos posted to Facebook, possibly trusting the person that posted the video. Thus, despite extensive discussion of various forms of viral information spreading, content, rather than popularity, is king in YouTube, although online word-of-mouth sharing through trusted relationships is also important. The main limitations of this research are that the data used may not be representative of YouTube and all UK YouTube users overall, so the conclusions should be interpreted cautiously

Metaverse. Old urban issues in new virtual cities

Recent years have seen the arise of some early attempts to build virtual cities, utopias or affective dystopias in an embodied Internet, which in some respects appear to be the ultimate expression of the neoliberal city paradigma (even if virtual). Although there is an extensive disciplinary literature on the relationship between planning and virtual or augmented reality linked mainly to the gaming industry, this often avoids design and value issues. The observation of some of these early experiences - Decentraland, Minecraft, Liberland Metaverse, to name a few - poses important questions and problems that are gradually becoming inescapable for designers and urban planners, and allows us to make some partial considerations on the risks and potentialities of these early virtual cities

Root, Tuber and Banana Food System Innovations

This open access book describes recent innovations in food systems based on root, tuber and banana crops in developing countries. These innovations respond to many of the challenges facing these vital crops, linked to their vegetative seed and bulky and perishable produce. The innovations create value, food, jobs and new sources of income while improving the wellbeing and quality of life of their users. Women are often key players in the production, processing and marketing of roots, tubers and bananas, so successful innovation needs to consider gender. These crops and their value chains have long been neglected by research and development, hence this book contributes to filling in the gap. The book features many outcomes of the CGIAR Research Program in Roots, Tubers and Banana (RTB), which operated from 2012-21, encompassing many tropical countries, academic and industry partners, multiple crops, and major initiatives. It describes the successful innovation model developed by RTB that brings together diverse partners and organizations, to create value for the end users and to generate positive economic and social outcomes. RTB has accelerated the scaling of innovations to reach many end users cost effectively. Though most of the book’s examples and insights are from Africa, they can be applied worldwide. The book will be useful for decision makers designing policies to scale up agricultural solutions, for researchers and extension specialists seeking practical ideas, and for scholars of innovation

An aesthetic for sustainable interactions in product-service systems?

Copyright @ 2012 Greenleaf PublishingEco-efficient Product-Service System (PSS) innovations represent a promising approach to sustainability. However the application of this concept is still very limited because its implementation and diffusion is hindered by several barriers (cultural, corporate and regulative ones). The paper investigates the barriers that affect the attractiveness and acceptation of eco-efficient PSS alternatives, and opens the debate on the aesthetic of eco-efficient PSS, and the way in which aesthetic could enhance some specific inner qualities of this kinds of innovations. Integrating insights from semiotics, the paper outlines some first research hypothesis on how the aesthetic elements of an eco-efficient PSS could facilitate user attraction, acceptation and satisfaction