Statewide and Regionalist Parties’ Perspectives in the Long-Term Dynamics of Decentralization

The doctoral dissertation with the title “Statewide and Regionalist Parties' Perspectives in the Long-Term Dynamics of Decentralization” engages with patterns of decentralization over time in comparative perspective. How the multi-level state is organized is fundamental for territorial politics, and therefore why decentralization occurs is an important factor to understand the democratic system and how policy-making difficulties arise. This dissertation embraces a post-functionalist, rational choice assumption about political parties: governing statewide parties decentralize in their own interest. Otherwise, why would they distribute power? Additionally, an innovative neo-institutionalist perspective argues that, over time, arising multi-level institutions influence statewide parties’ calculations endogenously to subsequently decentralize. The establishment of regional democracy through the major reform of political decentralization should empower regional actors, and influence statewide parties’ strategies of decentralization. The dissertation also includes two new methodological procedures. In chapter 2, an analysis of decentralization dynamics over time (1950-2018) and in 19 democracies unveils that regional democracy affects statewide parties’ asymmetric decentralization decisions. Before political decentralization, ideological proximity between the center and regions with decentralization demands seems to predict decentralizing reforms. This pattern disappears after political decentralization, possibly due to statewide governments giving up on ideological considerations vis-à-vis regional executives. Furthermore, party-based explanations of asymmetric decentralization cannot be found in symmetric decentralization, highlighting the latter’s idiosyncrasy. In chapter 3, based on co-authored work with Leonce Röth and Lea Kaftan, we develop a procedure to generate optimized dictionaries to measure attention dynamics to territorial politics based on newspaper texts in Spain (1976-2019) and the UK (1900-2020), two prominent and complex cases of decentralization. We show how to efficiently develop this important text-as-data resource to compare attention patterns across political arenas (mass media and parliament). By measuring salience of the territorial issue and its sub-issue over time, we find that media emphasizes violence-related territorial sub-issue more, whereas parliament focuses on administrative and technical issues such as a fiscal authority decentralization. In chapter 4, also based on a co-authored investigation with Lea Kaftan and Leonce Röth, we argue that party positions conveyed by the media are key to understand the voter-party convergence link in democratic representation. Mediated party positions can help us fill the gap in territorial politics concerning party positions on territorial sub-issues. We develop a procedure to obtain mediated party positions from news text with sentiment analysis and topic models in an automatized manner. Accounting for news outlet differences and comparing our measures with established expert judgments, manifesto positions, and estimates based on parliamentary debates, we find valid mediated positions for statewide and regionalist parties on four territorial sub-issues in Spain

Controversy trend detection in social media

In this research, we focus on the early prediction of whether topics are likely to generate significant controversy (in the form of social media such as comments, blogs, etc.). Controversy trend detection is important to companies, governments, national security agencies, and marketing groups because it can be used to identify which issues the public is having problems with and develop strategies to remedy them. For example, companies can monitor their press release to find out how the public is reacting and to decide if any additional public relations action is required, social media moderators can moderate discussions if the discussions start becoming abusive and getting out of control, and governmental agencies can monitor their public policies and make adjustments to the policies to address any public concerns. An algorithm was developed to predict controversy trends by taking into account sentiment expressed in comments, burstiness of comments, and controversy score. To train and test the algorithm, an annotated corpus was developed consisting of 728 news articles and over 500,000 comments on these articles made by viewers from CNN.com. This study achieved an average F-score of 71.3% across all time spans in detection of controversial versus non-controversial topics. The results suggest that it is possible for early prediction of controversy trends leveraging social media

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

Tackling food marketing to children in a digital world: trans-disciplinary perspectives. Children’s rights, evidence of impact, methodological challenges, regulatory options and policy implications for the WHO European Region

There is unequivocal evidence that childhood obesity is influenced by marketing of foods and non-alcoholic beverages high in saturated fat, salt and/or free sugars (HFSS), and a core recommendation of the WHO Commission on Ending Childhood Obesity is to reduce children’s exposure to all such marketing. As a result, WHO has called on Member States to introduce restrictions on marketing of HFSS foods to children, covering all media, including digital, and to close any regulatory loopholes. This publication provides up-to-date information on the marketing of foods and non-alcoholic beverages to children and the changes that have occurred in recent years, focusing in particular on the major shift to digital marketing. It examines trends in media use among children, marketing methods in the new digital media landscape and children’s engagement with such marketing. It also considers the impact on children and their ability to counter marketing as well as the implications for children’s rights and digital privacy. Finally the report discusses the policy implications and some of the recent policy action by WHO European Member States

Breakingnews: article annotation by image and text processing

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Building upon recent Deep Neural Network architectures, current approaches lying in the intersection of Computer Vision and Natural Language Processing have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these learning methods, though, rely on large training sets of images associated with human annotations that specifically describe the visual content. In this paper we propose to go a step further and explore the more complex cases where textual descriptions are loosely related to the images. We focus on the particular domain of news articles in which the textual content often expresses connotative and ambiguous relations that are only suggested but not directly inferred from images. We introduce an adaptive CNN architecture that shares most of the structure for multiple tasks including source detection, article illustration and geolocation of articles. Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation. Furthermore, we present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (such as GPS coordinates and user comments). We show this dataset to be appropriate to explore all aforementioned problems, for which we provide a baseline performance using various Deep Learning architectures, and different representations of the textual and visual features. We report very promising results and bring to light several limitations of current state-of-the-art in this kind of domain, which we hope will help spur progress in the field.Peer ReviewedPostprint (author's final draft