Al encontrarnos en entorno cuyos gustos y preferencias de las personas tienen a ser diversos, existe una amplia oferta de productos en el mercado que satisfacen necesidades específicas de los consumidores. Ante ello, los accesorios sexuales conforman productos con crecimiento en su demanda, ya que cada vez más personas se encuentran dispuestas a interactuar con artículos sexuales, los cuales permitan evitar la monotonía y que, a su vez, otorguen un mayor nivel de satisfacción. El objetivo de la presente investigación es atender la demanda de artículos sexuales en el mercado de Lima Metropolitana, a partir de una plataforma virtual que permita que los consumidores puedan acceder a diferentes productos de su agrado, de una forma interactiva y confiable, en la cual se puedan sentir libres de elegir y experimentar diversos productos existentes en el mercado. La propuesta de negocio de la presente investigación no solo se basa en la oferta de artículos sexuales para un segmento determinado, sino que busca como principal objetivo el poder crear una plataforma que, además de ofrecer productos de interés, logre también interactuar con el cliente, de modo que este pueda encontrar en nuestro negocio un espacio en el cual mantenerse informado acerca del uso de los productos, tips de ayuda y todo tipo de información que permita romper tabúes acerca del uso de ciertos artículos. A continuación, se presentan los detalles acerca de nuestro modelo de negocio aplicable en Lima Metropolitana. / Being in a society whose tastes and preferences of people have to be diverse, there is a wide range of products on the market that meet the specific needs of consumers. Given this, sexual accessories are products with growth in demand, as more and more people are willing to interact with sexual items, which avoid monotony and, in turn, provide a higher level of satisfaction. The aim of this research is to meet the demand for sexual items in the Metropolitan Lima market, based on a virtual platform that allows consumers to access different products of their liking, in an interactive and reliable way, in which They can feel free to choose and experience various products existing in the market. The business proposal of this research is not only based on the offer of sexual items for a specific segment, but seeks as the main objective to create a platform that, in addition to offering products of interest, also interact with the customer, so that he can find in our business a space in which to keep informed about the use of products, help tips and all kinds of information that allows breaking taboos about the use of certain items. Below, we present the details about our applicable business model in Metropolitan Lima. / Trabajo de investigación

The amount of published news articles is steadily increasing, and readers are shifting toward online platforms because of the convenience and affordable technology costs (Shearer, 2021). Users have become more engaged with online news articles. This engagement creates a rich corpus, which makes it a powerful means to understand public opinion, emerging events, and their evolvement. Therefore, many organizations invest in mining this large-scale user-generated content to improve their products, services, and, more importantly, their decision-making process. Studying users’ reactions to online news is essential for social scientists, policymakers, and journalists. This type of engagement is an area of study introduced previously. In the statistical and machine learning community, many survey-based studies tried to understand the users’ behavior by characterizing and categorizing comments in online news. Some studies focus on mining user opinions from social media and online news comments. Other works look into bias in the news and its influence on user-generated content. At the same time, the social network community addresses the problem of mining large-scale online news from different angles. Some work focuses on constructing knowledge graphs from the text. Others focus on building high-level graphs, where nodes are users and posts or documents, and links represent the relationship between nodes. Another line of work looked into the word level of the text. They extracted entities and topics by combining Natural Language Processing and graph techniques. From a Machine Learning perspective, there are three main challenges in all these studies 1) jointly mining massive user-generated data, 2) from multiple sources and platforms, and 3) the unpredictable quality of user-generated content. To address these issues, we tackle the problem of jointly learning and mining valuable information from online news articles and user-generated content. We start by studying and understating the relationship between users’ comments and articles in online news. Where the focus is to understand the level of relevancy between articles and their comments, we labeled a few article-comment pairs in this work. We proposed BERTAC (Alshehri et al.,2021), a BERT-based model that jointly learns article-comment embeddings and infers the relevance class of comment. However, we found that the disagreement among annotators as a part of a human (expert) labeling process produces noisy labels, which affect the performance of supervised learning algorithms. On the other hand, working only with high agreement annotations introduces another challenge: the data imbalance problem (Alshehri et al., 2022). As in many machine learning problems, labeling a sufficient number of examples is costly and time-consuming. Therefore, we propose a framework for aligning comments and news articles under a constrained budget(Alshehri et al., 2023a). The proposed model considers the data imbalanced, where we have only a few examples from one class, in addition, it considers the degrees of annotator disagreement. Within the framework, we consider two solutions, 1) semi-automatic labeling based on human-AI collaboration and 2) synthetic data augmentation. Another critical aspect of mining news articles and user-generated content is understanding emerging events and their associated entities. However, this is challenging, especially with the massive growth of online articles and user-generated content across different platforms. Therefore, we proposed MultiLayerET (Alshehri et al., 2023b), a unified representation of online news articles and comments. This work highlights the relationship between entities and topics in news articles and user-generated content. It projects entities and topics as a multi-layer graph, which gives a high-level understanding of the story behind the large pile of the corpus. We showed that such graphs enrich the textual representation and enhance the model learning performance in many downstream applications, such as media bias classification and fake news detection. / Computer and Information Science

This dissertation explores the point and process by which American colonists transformed from subjects to citizens. Upon learning of Boston radicals’ destruction of East India tea, Parliament passed the Coercive Acts, a collection of punitive measures designed to rein in that seaport town. In response, American communities from Massachusetts to Georgia drafted resistance resolutions calling on colonists to refrain from importing British merchandise, exporting American resources, and partaking in frivolous pastimes. Boston’s suffering, these communities declared, presented a threat to every colonist. Grassroots activists next called for a Continental Congress to coordinate and enforce a pan-colonial resistance movement to pressure Parliament’s repeal of the Coercive Acts. Once convened, delegates of the First Continental Congress drafted the Articles of Association which incorporated many directives already circulating in the town and county resolutions. Traditionally presented as a colonial boycott of British manufactures, the Association regulated cultural as well as commercial practices. It advised colonists to avoid waste and extravagance and singled out horse racing, cockfighting, theatergoing, and other displays of leisure as examples of moral decay. Echoing the grassroots resolutions, Congress also urged colonists to commit to nonimportation and non-consumption of British wares and nonexportation of American goods. Through these directives, Congress sought to achieve imperial reconciliation and colonial moral regeneration, yet its commitment to self-preservation reveals it focused more on restoring American virtue than returning harmony to the empire. To enforce the Articles of Association, Congress recommended towns and counties to create Committees of Inspection and Observation. Composed of locally elected men, these committees regulated their neighbors’ behavior and condemned violators of the Association as enemies of America. Using colonial newspapers, private letters, pamphlets, Congress’s official journals, Peter Force’s American Archives, and a wealth of other primary and secondary literature, this dissertation reveals how the Continental Association organized local communities of suffering. Members of these communities voluntarily suspended cultural and commercial practices to protect political identities they felt were in danger. In the process, those sacrificing in the common cause separated from the broader imperial community and formed an American political community. / History

This thesis aims to conduct a critical discourse analysis (CDA) of news articles and tweets discussing the climate protest that occurred on October 14th, 2022, where activists threw tomato soup on Van Gogh’s “Sunflower” at the London National Museum. The purpose is to investigate the media logic and the underlying social and cultural factors of iconoclastic actions that shaped the media discourse surrounding the event. The research questions this thesis aims to investigate are: RQ1: How are news values emphasized in media coverage of the tomato soup incident, as reflected in both news articles and Twitter posts? RQ2: Through the lens of media logic, how do media discourses shape perceptions of the climate action? RQ3: ​​How do affiliations with culturally significant artifacts and the climate shape the discursive representations of the protest in news articles and tweets? The sample consists of 34 tweets and 15 news articles. It is analyzed according to Van Dijk’s and Faircough’s CDA frameworks and the concepts of mediatization, media logic, iconoclasm, affiliation (as defined by Stoler (2022)) and news values. The results of this study suggest that the event had significant news value due to the iconoclastic tactics. Further, the media logic is seemingly involved in shaping the news into a sensationalistic story with little focus on the cause of the action. Lastly, the discourses surrounding the event on the platforms can be discussed in light of affiliation to provide an understanding of the discourse.

Content analysis is a systematic research method for examining symbolical content in communication by recording or transcribing these messages into categories. Reliability is one of the most distinctive attributes of content analysis methodology comparing to other techniques in communication. A content analysis was conducted by analyzing the method sections of published journal articles in Communication Abstracts from January 2006 through January 2011 by searching "advertising" and "content analysis". Results suggested that television is still the most focused medium in advertising content analysis research. Most of the content analysis studies employed 2 coders for coding reliability assessment data and final data. Moreover, content analysis researchers had improved in reporting reliability and reliability coefficients. However, there was a low percentage of studies that reported specific reliability for each variable as well as the lowest acceptable level for the reliability coefficients.

[pt] A partir de dezembro de 2019, o mundo tomou conhecimento da existência da Covid-19 (FIOCRUZ, 2020). As peculiaridades da doença, exigiram a adoção de medidas de distanciamento social (OMS, 2020). Na cidade do Rio de Janeiro, a partir de 16 de março de 2020, foi determinado o fechamento de todas as empresas que não prestassem serviços essenciais (ESTADO DO RIO DE JANEIRO, 2020). Nesse contexto, as empresas se viram compelidas a adaptar as rotinas organizacionais para manter suas operações. Emergiu, então, a relevância de investigar como profissionais experienciaram suas rotinas organizacionais durante o período de isolamento social imposto pela pandemia da Covid-19. A partir de uma abordagem Fenomenográfica, foram entrevistados 30 profissionais. Das análises, retornaram três categorias descritivas: i) a preservação das rotinas organizacionais; ii) a (re)organização da dimensão tempo-espaço e iii) a capacidade de inovação. Para relacioná-las, foram identificadas quatro dimensões explicativas: i) o dinamismo nos componentes operativos das rotinas; ii) os aspectos técnicos-comportamentais dos atores envolvidos; iii) os aspectos gerenciais sobre as rotinas e iv percepção de segurança. Os achados sugerem que as concepções dos profissionais evoluíram da operação mecânica da rotina para uma percepção de engajamento coletivo para manutenção da própria empresa; do objetivo de manter os padrões preestabelecidos, para uma percepção de oportunidade para inovação e diferenciação. Além disso, o estudo reforça a indissociável relação entre os aspectos ostensivo e performativos das rotinas e os artefatos. / [en] As of December 2019, the world became aware of the existence of Covid-19 (FIOCRUZ, 2020). The peculiarities of the disease required the adoption of measures of social distancing (WHO, 2020). In the city of Rio de Janeiro, as of March 16, 2020, all companies that did not provide essential services were closed (ESTADO DO RIO DE JANEIRO, 2020). In this context, companies found themselves compelled to adapt organizational routines to maintain their operations. The relevance of investigating how professionals experienced their organizational routines during the period of social isolation imposed by the Covid-19 pandemic emerged. From a Phenomenographic approach, 30 professionals were interviewed. From the analyses, three descriptive categories returned: i) the preservation of organizational routines; ii) the (re)organization of the time-space dimension and iii) the capacity for innovation. To relate them, four explanatory dimensions were identified: i) the dynamism in the operative components of the routines; ii) the technical-behavioral aspects of the actors involved; iii) managerial aspects of routines and iv perception of safety. The findings suggest that the professionals conceptions evolved from the mechanical operation of the routine to a perception of collective engagement to maintain the company itself; from the objective of maintaining pre-established standards, to a perception of opportunity for innovation and differentiation. Furthermore, the study reinforces the inseparable relationship between the ostensive and performative aspects of routines and artifacts.

Researchers have made significant progress in information extraction from short documents in the last few years, including social media interaction, news articles, and email excerpts. This research aims to extract technical entities like hardware resources, computing platforms, compute time, programming language, and libraries from scholarly research articles. Research articles are generally long documents having both salient as well as non-salient entities. Analyzing the cross-sectional relation, filtering the relevant information, measuring the saliency of mentioned entities, and extracting novel entities are some of the technical challenges involved in this research. This work presents a detailed study about the performance, effectiveness, and scalability of rule-based weakly supervised algorithms. We also develop an automated end-to-end Research Entity and Relationship Extractor (E2R Extractor). Additionally, we perform a comprehensive study about the effectiveness of existing deep learning-based information extraction tools like Dygie, Dygie++, SciREX. The research also contributes a dataset containing novel entities annotated in BILUO format and represents the baseline results using the E2R extractor on the proposed dataset. The results indicate that the E2R extractor successfully extracts salient entities from research articles. / Master of Science / Information extraction is a process of automatically extracting meaningful information from unstructured text such as articles, news feeds and presenting it in a structured format. Researchers have made significant progress in this domain over the past few years. However, their work primarily focuses on short documents such as social media interactions, news articles, email excerpts, and not on long documents such as scholarly articles and research papers. Long documents contain a lot of redundant data, so filtering and extracting meaningful information is quite challenging. This work focuses on extracting entities such as hardware resources, compute platforms, and programming languages used in scholarly articles. We present a deep learning-based model to extract such entities from research articles and research papers. We evaluate the performance of our deep learning model against simple rule-based algorithms and other state-of-the-art models for extracting the desired entities. Our work also contributes a labeled dataset containing the entities mentioned above and results obtained on this dataset using our deep learning model.

Background: In the last few years, there has been rapid growth in the use of cryptocurrency, as it is a form of digital currency and was developed using blockchain technology, so it is almost impossible to counterfeit cryptocurrency. Due to these features, it has attracted a lot of popularity and attention in the market. There has been a research gap in predicting accurate cryptocurrency prices by using sentiment analysis. This study will use Artificial Intelligence-based methods and sentiment analysis to develop a model for predicting cryptocurrency prices. By using the mentioned methods in this thesis, the developed model will provide precise results. Objectives: The objective of the thesis is to compare artificial intelligence models for cryptocurrency price prediction and analyze the importance of sentiment analysis by understanding the public pulse in cryptocurrencies and how it affects price fluctuations, analyzing the correlation within news articles, social media posts, and price fluctuations, as well as evaluating the model performance by employing metrics like RSME, MSE, MAE, and R2 error. Methods: The thesis follows the use of a systematic literature review along with an experimental model for comparing artificial intelligence models. Sentiment analysis played a crucial role in understanding market dynamics. By using linear regression, random forest, and gradient boosting algorithms artificial intelligence models are built to predict cryptocurrency prices using sentiment analysis. The developed models are then compared using performance metrics. This research has analyzed and evaluated each model's performance in predicting cryptocurrency prices. Results: The results of the systematic literature review indicated that market sentiment affects cryptocurrency prices. Prices have increased when market sentiment has been positive, whereas prices dropped when sentiment has been negative. The correlation between cryptocurrency values and market mood, however, is complicated as it depends on a variety of factors. Based on the evaluation measures, the random forest artificial intelligence model is the most accurate in predicting cryptocurrency prices after evaluating the three artificial intelligence models. Conclusions: This study utilized sentiment analysis and artificial intelligence to forecast cryptocurrency prices. It highlighted the significance of sentiment analysis as a tool for predicting the short-term price of cryptocurrencies by demonstrating how negative sentiment is correlated with decreases in price compared to positive sentiment with price increases. However, it recognized that it was necessary to take into consideration the complexity and broad range of effects on cryptocurrency markets. Research in the future will examine comprehensive sentiment analysis methods and broadening data sources.

Since the 1990s, many countries have become more ethnically diverse. The structure of the people, the demos, is changing. According to Robert Dahl’s fourth criterion of what a democratic process is, the people should have control over the political agenda. Previous research has shown a significantly lower political participation and representation of immigrants in the political and media sphere in Sweden. Do the people really control the agenda? This thesis examines the representation of people with a foreign background from outside Europe on Swedish debate pages by collecting a sample of writers' names from three established Swedish newspapers; Dagens Nyheter, Svenska Dagbladet, and Aftonbladet, during the time period 1992–2022. Using a name algorithm to determine country of origin, the results show an increase in representation to the point of 2002. From 2002 to today, the representation of non-European foreign background on Swedish debate pages has decreased.

Story-focused news articles are a different type of news articles, containing more visual and interactive elements, developed in order to engage a younger audience for online newspapers. User engagement has been defined as the “emotional, cognitive and behavioral connection between a user and a resource”, and different metrics are used to track the user engagement of the readers on these articles. However, there is no prior research on which of these metrics describe user engagement in the most accurate way. This study therefore aims to find out what metrics to use when measuring user engagement on story-focused articles through interviewing readers of three different story-focused articles and compare their engagement levels with actual metric values tracked. The results show that two out of the three articles can be considered engaging according to the definition, and the metrics they both have in common is high values of scroll depth, low values of bounce rate and high values of page views. The study therefore concludes that a combination of these three metrics describes user engagement in the most accurate way possible. Furthermore, both the engaging articles have a large number of images, galleries and videos compared to the non-engaging article, which indicates that visual elements in different forms are a winning concept for story-focused articles. / Nyhetsartiklar som fokuserar på berättande är en ny typ av nyhetsartiklar som innehåller fler visuella och interaktiva element, utvecklade för att engagera en yngre publik för digitala nyhetssidor. Användarengagemang har tidigare definierats som det “emotionella, kognitiva och beteendemässiga kontakten mellan användaren och resursen”, och olika mätetal används för att mäta användarengagemanget hos läsarna av nyhetsartiklar som fokuserar på berättande. Däremot finns det ingen tidigare forskning på vilka av dessa mätetal som beskriver användarengagemang på bäst sätt. Den här studien har därför som mål att ta reda på vilka mätetal som borde användas vid mätning av användarengagemang för nyhetsartiklar som fokuserar på berättande, genom att intervjua läsare av tre olika artiklar och jämföra deras engagemangsnivå med uppmätta mätetal. Resultaten visar att 2 av de 3 artiklarna kan anses engagerande enligt definitionen, och mätetalen som de båda har gemensamt är ett högt genomsnittligt scrolldjup, låg nivå av studsar och höga siffror för sidvisningar. Studien drar därför slutsatsen att en kombination av dessa tre mätetal beskriver användarengagemang på bästa möjliga sätt. Dessutom har båda de engagerande artiklarna ett stort antal bilder, gallerier och videor jämfört med den icke engagerande artikeln, vilket indikerar att visuella element av olika slag är ett vinnande koncept för historieberättande artiklar.