Information quality corresponds to the degree of excellence in communicating knowledge or intelligence and encompasses aspects of validity, accuracy, reliability, bias, transparency, and comprehensiveness among others. Professional news, public relations, and user generated content alike all have their own subtly different information quality concerns. With so much recent growth in online video, it is also apparent that more and more consumers will be getting their information from online videos and that understanding the information quality of video becomes paramount for a consumer wanting to make decisions based on it. This dissertation explores the design and evaluation of collaborative video annotation and presentation interfaces as motivated by the desire for better information quality in online video. We designed, built, and evaluated three systems: (1) Audio Puzzler, a puzzle game which as a by-product of play produces highly accurate time-stamped transcripts of video, (2) Videolyzer, a video annotation system designed to aid bloggers and journalists collect, aggregate, and share analyses of information quality of video, and (3) Videolyzer CE, a simplified video annotation presentation which syndicates the knowledge collected using Videolyzer to a wider range of users in order to modulate their perceptions of video information. We contribute to knowledge of different interface methods for collaborative video annotation and to mechanisms for enhancing accuracy of objective metadata such as transcripts as well as subjective notions of information quality of the video itself.

In today’s society media constitutes an arena with the opportunity for social communication. The arena is made up of subjective opinions that often leads to free debates about the subjects written about. The recent refugee situation has been highlighted in Swedish media during 2015 and has received widespread attention from various media outlets. The purpose of this paper is to examine how different Swedish newspapers chooses to portray the arriving asylumseekers. The material used is a selection of editorial pages in five daily newspapers. The theories used to evaluate how the newspapers are portraying the asylum seekers is the framing theory and agenda-setting theory. This research uses a qualitative case study approach that is complemented with a media analysis. The results of the research indicate that editorial writers do not differentiate in their description between different ethnic or cultural groups that are seeking asylum. Rather, there is a strong focus in writing about how the refugees are contributing negatively in the development of the Swedish society. The results also showed an indication that the content differs depending on the political standing of the newspaper.

This paper set out to find out how actors have framed Sweden's tax on aviation, and compared the debates around the two times the tax has been enacted, 2006 and 2018. Connecting the specific issue of the aviation tax to the broader narrative on climate change, the framing process was formulated as a two-level game with both issue-specific frames and general master frames that have wider cultural resonance. The framing analysis was conducted observing the debates as a framing contest between frame sponsors (focusing on framing efforts from the aviation industry and the Green Party) in interaction with news journalists and opinion writers. The best way to describe the 2006 debate is that it was dominated by discussion of the tax from an economic standpoint, latching on to a master frame of economic consequences with regional impact. The 2018 debate focused on the environmental aspects of the tax, mostly disregarding the explicit effects of the tax and focusing on the harm of flying, connected with a moral frame together with a responsibility frame towards the individual. As I interpret the debates, the actor who effectively connected their issue frame to a master frame had control of the narrative, which meant for example that the Green Party did not get to discuss environmental aspects in 2006, and that they did not need to discuss economic aspects in 2018.

abstract: ABSTRACT This thesis analyzes the discourse surrounding proposed solutions to the immigration phenomenon in the United States. I conducted two qualitative media analyses on the rhetoric and conceptual frames found in mass media newscasts reporting on the immigration debate. The first analysis covered the general immigration debate and the second covered the appearance of American southwest ranchers. Specifically the analyses contrasted the media's coverage of root economic causes to the immigration phenomenon in comparison to reactionary solutions as proposed by leading immigrant attrition organizations such as the immigration think tank, Federation for American Immigration Reform (FAIR) and Republican linguist, Frank Luntz. The main argument of this thesis is based on an analysis of how the media has used southwestern ranchers as expert witnesses for reactionary solutions on a national level. An acute qualitative media analysis was used to compare the rhetoric found in the media coverage of southwestern ranchers versus the rhetoric found in 12 in-depth interviews I conducted with ranchers in the American southwest. This thesis contends that the media has successfully turned southwestern ranchers into spokespersons for border security rhetoric, furthering the binary debates on border security and immigration reform and thus obscuring the conditions which force migrants to leave their home countries. The grounding theoretical framework for this thesis is based on David Altheide's qualitative media analysis which identifies how certain frames and common narratives ultimately construct a way of discussing the problem or the kind of discourse that will follow. This was structured on Atheide's qualitative media analysis protocols to dissect mass media newscasts covering the immigration debate and more specifically the mass media's coverage of southwestern ranchers. The qualitative media analyses were employed to determine whether the discourse found in nightly newscasts falls in line with root causes of immigration or FAIR's concern with reactionary solutions. To further assess the media's ability to shape discourse, and ultimately policy, these qualitative analyses were compared with in-depth interviews of the ranchers. / Dissertation/Thesis / M.A. Social Justice and Human Rights 2010

The term medicalization refers to the process through which a previously nonmedical issue newly becomes conceived of as a medical one. The aim of this thesis is to show how the utilization of this concept can help lead to a better understanding of the contemporary phenomenon of mass antidepressant consumption. I attempt to demonstrate here that the rise in antidepressant use, which has been occurring worldwide in the last few decades, must be understood as the result of a host of factors, among which a key role is played by changes in the conception of certain emotional states, previously understood as normal, as symptoms of mental illness. Aside from a general analysis of the factors that contribute to the consumption of these medications, the thesis also contains my own case study of one of them, namely the ways in which antidepressants are portrayed by the media. I focus on articles, published between the years 1996 and 2016 in Czech national newspapers and attempt to show how discussions about antidepressants, despite their relative neutrality, by accenting a medicalized frame of understanding mental problems help to create an environment, which supports their consumption.

Bacterial resistance to antibiotics threatens modern healthcare on a global scale. Several actors in society, including the general public, must become more involved if this development is to be countered. The conveyance of relevant information provided through education and media reports is therefore of high concern. Antibiotic resistance evolves through the mechanisms of natural selection; in this way, a sound understanding of these mechanisms underlies explanations of causes and the development of effective risk-reduction measures. In addition to natural selection functioning as an explanatory framework to antibiotic resistance, bacterial resistance as a context seems to possess a number of qualities that make it suitable for teaching natural selection – a subject that has been proven notoriously hard to teach and learn. A recently suggested approach for learning natural selection involves so-called threshold concepts, which encompass abstract and integrative ideas. The threshold concepts associated with natural selection include, among others, the notions of randomness as well as vast spatial and temporal scales. Illustrating complex relationships between concepts on different levels of organization is one, of several, areas where visualizations are efficient. Given the often-imperceptible nature of threshold concepts as well as the fact that natural selection processes occur on different organizational levels, visual accounts of natural selection have many potential benefits for learning. Against this background, the present dissertation explores information conveyed to the public regarding antibiotic resistance and natural selection, as well as investigates how these topics are presented together, by scrutinizing media including news reports, websites, educational textbooks and online videos. The principal method employed in the media studies was content analysis, which was complemented with various other analytical procedures. Moreover, a classroom study was performed, in which novice pupils worked with a series of animations explaining the evolution of antibiotic resistance. Data from individual written assignments, group questions and video-recorded discussions were collected and analyzed to empirically explore the potential of antibiotic resistance as a context for learning about evolution through natural selection. Among the findings are that certain information, that is crucial for the public to know, about antibiotic resistance was conveyed to a low extent through wide-reaching news reporting. Moreover, explanations based on natural selection were rarely included in accounts of antibiotic resistance in any of the examined media. Thus, it is highly likely that a large proportion of the population is never exposed to explanations for resistance development during education or through newspapers. Furthermore, the few examples that were encountered in newspapers or textbooks were hardly ever visualized, but presented only in textual form. With regard to videos purporting to explain natural selection, it was found that a majority lacked accounts of central key concepts. Additionally, explanations of how variation originates on the DNA-level were especially scarce. These and other findings coming from the content analyses are discussed through the lens of scientific literacy and could be used to inform and strengthen teaching and scientific curricula with regards to both antibiotic resistance and evolution. Furthermore, several factors of interest for using antibiotic resistance in the teaching of evolution were identified from the classroom study. These involve, among others, how learners’ perception of threshold concepts such as randomness and levels of organization in space and time are affected by the bacterial context

Computational approaches to opinion mining have mostly focused on polarity detection of product reviews by classifying the given text as positive, negative or neutral. While, there is less effort in the direction of socio-political opinion mining to determine favorability towards given targets of interest, particularly for social media data like news comments and tweets. In this research, we explore the task of automatically determining from the text whether the author of the text is in favor of, against, or neutral towards a proposition or target. The target may be a person, an organization, a government policy, a movement, a product, etc. Moreover, we are interested in detecting the reasons behind authors’ positions. This thesis is organized into three main parts: the first part on Twitter stance detection and interaction of stance and sentiment labels, the second part on detecting stance and the reasons behind it in online news comments, and the third part on multi-target stance classification. One may express favor (or disfavor) towards a target by using positive or negative language. Here, for the first time, we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets, as well as for sentiment. These targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. We develop a simple stance detection system that outperforms all 19 teams that participated in a recent shared task competition on the same dataset (SemEval-2016 Task #6). Additionally, access to both stance and sentiment annotations allows us to conduct several experiments to tease out their interactions. Next, we proposed a novel framework for joint learning of stance and reasons behind it. This framework relies on topic modeling. Unlike other machine learning approaches for argument tagging which often require a large set of labeled data, our approach is minimally supervised. The extracted arguments are subsequently employed for stance classification. Furthermore, we create and make available the first dataset of online news comments manually annotated for stance and arguments. Experiments on this dataset demonstrate the benefits of using topic modeling, particularly Non-Negative Matrix Factorization, for argument detection. Previous models for stance classification often treat each target independently, ignoring the potential (sometimes very strong) dependency that could exist among targets. However, in many applications, there exist natural dependencies among targets. In this research, we relieve such independence assumptions in order to jointly model the stance expressed towards multiple targets. We present a new dataset that we built for this task and make it publicly available. Next, we show that an attention-based encoder-decoder framework is very effective for this problem, outperforming several alternatives that jointly learn dependent subjectivity through cascading classification or multi-task learning.

The Master´s thesis aims to assess the overall CSR strategy of Tesco by using KORP analysis. It also evaluates the CSR projects of the company through media analysis. Research has shown that the company is targeting its activities to social and environmental pillar. They are both ranked above average . The economic pillar has the potential to be improved, therefore there are some recommendation how to achieve it . The work points to effectively set CSR strategy of the company

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The diploma thesis is about the elections to the European Parliament in Poland and Germany 2019 and their reflection in the local media, which are the periodicals Junge Freiheit, WSieci, Frankfurter Allgemeine Zeitung, Rzeczpospolita, Süddeutsche Zeitung, Gazeta Wyborcza, Jungle World and Kultura Liberalna. The thesis presents the historical development of the European Parliament, its competencies, and the electoral system. It also acquaints the reader with its powers and composition in 2019. In the thesis, the author presents the first direct elections to the European Parliament in Germany in 1979 and in Poland in 2004. In order to create an objective overview of the elections for the reader, the author presents the elections in Germany and in Poland in 2009 and in 2014. Subsequently, the 2019 elections themselves are the main theme, along with party campaigns and election polls in the two selected countries. Quantitative and qualitative research of the selected media was conducted to find answers to the research questions, which it adequately did. These answers are thoroughly commented after presentation. The final part of the thesis deals with the comparison of periodicals, summarization of the qualitative analysis and the conclusion of the whole diploma thesis.