Clustering U.S. 2016 presidential candidates through linguistic appraisals

Producción CientíficaThe main purpose of this paper is to cluster the United States (U.S.) 2016 presidential candidates taking the linguistic appraisals made by a random representative sample of adults living in the U.S. as our starting point. To do this, we have used the concept of ordinal proximity measure (see García-Lapresta and Pérez-Román), which allows to determine the degree of consensus in a group of agents when a set of alternatives is evaluated through non-necessarily qualitative scales.Ministerio de Economía, Industria y Competitividad (project ECO2016-77900-P

Social Networks, Political Discourse and Polarization during the 2017 Catalan elections

This thesis investigates the political process in Spain and Catalonia during the Catalan election in December 2017. This regional election was unusual because of the independence process in Catalonia and its repression. Two parties, Ciudadanos (anti-independence) and Podemos (ambiguous position) and their leaders’ activity in Twitter was analyzed. It was explored from three perspectives: social networks, lexical and emotional discourse and ideological polarization. Firstly, social networks were used to see the properties of the support communities of both parties. Interestingly unlike Ps, Ciudadanos’ (Cs) metrics of cohesion showed that political communities of this party in Spain and Catalonia were remarkably well integrated. Secondly, using machine learning techniques, discourse cohesiveness of Ps and Cs’ politicians was analyzed regarding the lexical and emotional content of their messages. The results showed that even though Cs’ politicians were more lexically similar, Ps’ were more similar in terms of emotions. Specifically, the study of emotions in the discourse shed light on populist messages from Cs. This party used anger and disgust to take advantage the polarized political scenario. Lastly, with a sample of users (N=2000) in Twitter, the relationship between dispositional emotions and ideological polarization was investigated. Results showed that users predisposed to anger were significantly more polarized and those predisposed to fear were significantly less polarized. Interestingly, even though predisposition to fear decreased polarization, the interaction between fear and anger significantly increased it. These results have interesting implications regarding the increasing opportunities of politicians to target the electorate based on personal characteristics

Adaptive sentiment analysis

Domain dependency is one of the most challenging problems in the field of sentiment analysis. Although most sentiment analysis methods have decent performance if they are targeted at a specific domain and writing style, they do not usually work well with texts that are originated outside of their domain boundaries. Often there is a need to perform sentiment analysis in a domain where no labelled document is available. To address this scenario, researchers have proposed many domain adaptation or unsupervised sentiment analysis methods. However, there is still much room for improvement, as those methods typically cannot match conventional supervised sentiment analysis methods. In this thesis, we propose a novel aspect-level sentiment analysis method that seamlessly integrates lexicon- and learning-based methods. While its performance is comparable to existing approaches, it is less sensitive to domain boundaries and can be applied to cross-domain sentiment analysis when the target domain is similar to the source domain. It also offers more structured and readable results by detecting individual topic aspects and determining their sentiment strengths. Furthermore, we investigate a novel approach to automatically constructing domain-specific sentiment lexicons based on distributed word representations (aka word embeddings). The induced lexicon has quality on a par with a handcrafted one and could be used directly in a lexiconbased algorithm for sentiment analysis, but we find that a two-stage bootstrapping strategy could further boost the sentiment classification performance. Compared to existing methods, such an end-to-end nearly-unsupervised approach to domain-specific sentiment analysis works out of the box for any target domain, requires no handcrafted lexicon or labelled corpus, and achieves sentiment classification accuracy comparable to that of fully supervised approaches. Overall, the contribution of this Ph.D. work to the research field of sentiment analysis is twofold. First, we develop a new sentiment analysis system which can — in a nearlyunsupervised manner—adapt to the domain at hand and perform sentiment analysis with minimal loss of performance. Second, we showcase this system in several areas (including finance, politics, and e-business), and investigate particularly the temporal dynamics of sentiment in such contexts

Policing Power: Essays on Coercion, Corruption, and the State

This dissertation is about how the state influences individuals’ behavior by giving certain citizens the legal and physical means to coerce other citizens. Using field experimentation, participatory observation, and time-series analysis of two large sets of micro-data on crime to study policing in West Africa and Melanesia, the findings challenge conventional wisdom about the relationship between coercion, corruption, and the state. Empowering women by sending police officers to assist them in disputes with men may not necessarily reduce gender-based coercion, because men can preserve their privileges by drawing on alternative authorities. Conferring police officers powers to coerce other people does not necessarily induce corrupt behavior, because conferral of power may cause them to care more about their reputation than the rents they can extract. Competitive elections may not reduce petty police corruption even if they make principals accountable; instead, elections may incentivize corruption by increasing agents’ uncertainty about how principals will act in the future. These findings contribute new insights to the theory of state-building, accountability, and bureaucratic politics

Spreading News: The Coverage Of Epidemics By American Newspapers And Its Effects On Audiences - A Crisis Communication Approach

Launched in 2002 in response to inadequate communications during the anthrax attacks and in preparations to the threats posed by H5N1, the Centers for Disease Control and Prevention (CDC)’s Crisis and Emergency Risk Communication (CERC) framework provides health professionals with trainings, tools, and resources to help them communicate effectively during emergencies and public health crises. Since that time, the framework has been used by the organization during outbreaks of infectious diseases. A core argument of CERC is that lack of certainty, efficacy, and trust serve as barriers to compliance with and support in CDC during an outbreak. According to CERC, providing the public with information about health and social risks, as well as information about ways individuals and organizations may ameliorate threats, could counter these perceptions, improve communications, and eventually save lives. However, the dissemination of the organization’s crisis messages depends largely on the mass media coverage. Understanding the news media’s agenda, priorities and role during outbreaks is essential for improving the cooperation between CDC and journalists. However, CERC provides little information about the actual behavior of journalists during crises, as reflected in news coverage of past outbreaks. This work aims to fill that gap in our understanding of the routinization of news during epidemics and its impact on audiences by systematically analyzing the coverage of epidemics in leading newspapers and using experiments to test its effects. This study analyzed 5,006 articles from leading American newspapers covering three epidemics: H1N1, Ebola, and Zika. Using a mixed method of automated and manual content analysis, it identified three distinct themes used to cover the diseases; pandemic, scientific, and social. Next, manual content analysis was conducted to assess the prevalence of information components theorized by CERC to increase certainty, efficacy and trust- information about medical/health risks, social/economic disruptions, and potential individual and organizational responses to ameliorate risks and reduce harm. Analysis of the themes based on CERC principles demonstrated substantial discrepancies between what CDC aims to communicate during epidemics and what the media actually disseminated to the public. An experiment (n = 321) found that exposure to articles representing the themes affected perceptions of certainty, efficacy, and trust, that in turn were associated with intentions to comply with CDC. The experiment also demonstrated the ability of coverage that follows CERC principles more closely to reduce harmful perceptions that were associated with behavioral intentions in target audiences. Implications for public health organizations and communicators are discussed, including ways to improve cooperation with journalists and the use of alternative direct-channels for filling gaps in news media coverage

Optimising Emotions, Incubating Falsehoods: How to Protect the Global Civic Body from Disinformation and Misinformation

This open access book deconstructs the core features of online misinformation and disinformation. It finds that the optimisation of emotions for commercial and political gain is a primary cause of false information online. The chapters distil societal harms, evaluate solutions, and consider what must be done to strengthen societies as new biometric forms of emotion profiling emerge. Based on a rich, empirical, and interdisciplinary literature that examines multiple countries, the book will be of interest to scholars and students of Communications, Journalism, Politics, Sociology, Science and Technology Studies, and Information Science, as well as global and local policymakers and ordinary citizens interested in how to prevent the spread of false information worldwide, both now and in the future

Optimising Emotions, Incubating Falsehoods

This open access book deconstructs the core features of online misinformation and disinformation. It finds that the optimisation of emotions for commercial and political gain is a primary cause of false information online. The chapters distil societal harms, evaluate solutions, and consider what must be done to strengthen societies as new biometric forms of emotion profiling emerge. Based on a rich, empirical, and interdisciplinary literature that examines multiple countries, the book will be of interest to scholars and students of Communications, Journalism, Politics, Sociology, Science and Technology Studies, and Information Science, as well as global and local policymakers and ordinary citizens interested in how to prevent the spread of false information worldwide, both now and in the future

Politics of the Heart and Mind : Using WhatsApp to Drive Support Amongst Evangelicals in Brazil

Dissertation (MA (Political Sciences))--University of Pretoria, 2022.On the 28 October 2018, Brazilians elected right-wing populist Jair Messias Bolsonaro as the 38th President of Brazil. Bolsonaro defeated his opponent Fernando Haddad from the PT, by a significant margin of 55,13% as against 43,87% (Araujuo & Prior 2020). Even against the backdrop of surging support for right-wing candidates and parties, the election of Bolsonaro still surprised observers and academics. Bolsonaro’s success, as Leticia Cesarino (2019) and Stuart Davis and Joe Straubhaar (2020) found, was due to his campaign’s communication strategy on social media. At the heart of these strategies is the use of emotive messaging on WhatsApp, which micro-targeted Evangelicals through the use of fake news, particularly disinformation, and populist communication derived from crises occurring in Brazil leading up to the 2018 election. Grounded in these arguments, this dissertation studies the effects of fear, anger and enthusiasm/hope in communication shared by the Bolsonaro campaign on WhatsApp. In doing so, the study applies a two-step analysis method. Firstly, the 12 sample WhatsApp messages were analysed using a latent content analysis to identify and explore the topics addressed in these messages. Secondly, these messages were then subjected to an emotional sentiment analysis to measure their emotive elements using the Linguistic-Inquiry and Word Count software. Guided by the Theory of Affective Intelligence, this dissertation found that anger and enthusiasm are evoked by WhatsApp messaging based on political habits, while fear is evoked by messaging grounded in an unknown threat. Moreover, anger and enthusiasm are found to decrease information-seeking, while fear encourages individuals to seek alternative information. The Bolsonaro WhatsApp network acted as both an echo-chamber for individuals experiencing anger/enthusiasm, and an alternative source of information for individuals experiencing fear. This dissertation shows how the Bolsonaro campaign used emotive strategic communication on WhatsApp to appeal to Evangelical Christians, specifically their desire for social recognition.Political SciencesMA (Political Sciences)Unrestricte