The Covid-19 pandemic presented unprecedented challenges, with global lockdowns impacting individuals on a profound scale. Many took to social media to cope with feelings of anxiety and isolation. Lockdown conditions and social media carry with them particular challenges, triggers and temptations for those with eating disorders, namely in the form of online communities promoting eating disorders and disordered behaviors as a legitimate lifestyle choice. This study examines pro-eating disorder Twitter communities before, during and after the initial Covid-19 lockdown (Mid-March to May 2020) to examine the influence of confinement on leadership dynamics and content trends. Utilizing data obtained through Twitter’s Academic API, I constructed monthly retweet network time-slices spanning from November 2019 to September 2020. Through social network analysis and analyzing turnover rates of top users, the evolution of influential users was assessed to test whether the circumstances created by Covid-19 restrictions would disrupt the established leadership paradigm or the period would maintain stable leadership based on expectations proposed by the literature of preferential attachment in scale-free networks. Contrary to expectations, influential users exhibited high turnover throughout the period and the network showed no tendency towards preferential attachment or any scale-free behavior in degree distributions. The high rate of leader turnover further increased in May and a higher proportion of new users achieved the highest number of in-degree ties into the latter months, but this hint at a cohort shift did not align with covid lockdown as predicted, instead occurring at the end of lockdown and continuing until the end of the studied period. Ultimately, users’ mostly fleeting popularity was largely based on the current content interests of the group rather than the individual user’s network position. The increase in activity predicted to co-occur with covid restrictions did not materialize until the summer months, therefore cannot be definitively linked to lockdown. The fluctuations in topic popularity detected in the topic model suggest a possible seasonal component to the rhythms of this community that requires further research. This exclusive longitudinal analysis of retweet networks as they were affected by covid-19 lockdown conditions challenges previous research on influence in social networks and online communities with findings of more dynamic leadership. Understanding the influence dynamics of this community can inform efforts to combat the spread of potentially harmful content and provide valuable insights for eating disorder specialists navigating the influences that may affecting their patients.

Bakgrund: Hållbarhetsredovisning har fått stor betydelse och mer uppmärksamhet de senaste åren. Det ökande trycket på hållbarhet har gett upphov till nya lagkrav och regler för företag som redovisar hållbarhet i sina årsredovisningar. Det har bland annat vuxit fram regelverk och organisationer för att stödja företag att vara transparenta och skapa en tydlig gemensam struktur för företagens hållbarhetsredovisning som till exempel GRI. Tidigare forskning menar att hållbarhetsredovisningen skiljer sig  beroende på i vilken bransch företaget verkar. Dessa skillnader gör det i sin tur svårt att jämföra hållbarhetsredovisningar. Syfte: Syftet med denna studie är att undersöka hur tillämpningen av GRI-standards påverkar innehållet i hållbarhetsrapporter och huruvida innehållet skiljer sig mellan olika branscher inom den svenska marknaden. Vidare är syftet att bidra med en djupare förståelse kring hur branschtillhörighet påverkar skillnaden i prioritering av ämnesspecifika upplysningar i svenska företags hållbarhetsrapporter som är upprättade enligt GRI-standards. Metod: Studien använder sig av en kvantitativ innehållsanalys med en deduktiv forskningsansats för att jämföra 40 företags hållbarhetsrapporter som är upprättade enligt GRI-standards. Företagen i studien valdes ut från fyra olika branscher totalt 10 företag från varje bransch. I studien används  statistiska tester i form av One Way Anova och Post Hoc Tukey test. Slutsats: Studien visar att branschtillhörighet påverkar skillnaden i prioritering av ämnesspecifika upplysningar inom GRI-standards. Det empiriska resultat visar att det finns skillnader mellan företagens hållbarhetsredovisningar. Jämförelsen av ekonomikategorin visade att det inte finns någon signifikant skillnad mellan branscherna. Redovisning av socialkategori och miljökategori visade att det finns en signifikant skillnad i redovisning av de ämnesspecifika aspekterna mellan de undersökta branscherna. / Background: Sustainability reporting has gained great importance and more attention in recent years. Increasing pressure on sustainability has led to new legal requirements and rules for companies reporting on sustainability in their annual reports. Among others, regulations and organisations have emerged to help companies be transparent and provide a clear common structure for corporate sustainability reporting, such as the GRI. Previous research has found that sustainability reporting varies by the industry in which the company operates. These differences in turn make it difficult to compare sustainability reports. Purpose: The aim of this study is to investigate how the application of the GRI standards affects the content of sustainability reports and whether the content differs between different industries within the Swedish market. It also aims to gain a deeper understanding of how industry affiliation influences the different prioritisation of topic-specific information in Swedish companies sustainability reports that have been prepared in accordance with GRI standards.   Method: The study used a quantitative content analysis with a deductive research approach to compare the sustainability reports of 40 companies prepared according to the GRI standards. The companies studied were selected from four different industries, totaling 10 companies from each industry. The study uses statistical tests in the form of One Way Anova and Post Hoc Tukey tests. Conclusion: The study shows that industry affiliation has an impact on the different prioritisation of subject-specific information within the GRI standards. The empirical results show that there are differences between companies' sustainability reports. The comparison of the business category shows that there is no significant difference between the industries. Reporting in the social and environmental categories shows a significant difference in reporting on the subject-specific aspects between the industries studied.

Identifying topics of a textual document is useful for many purposes. We can organize the documents by topics in digital libraries. Then, we could browse and search for the documents with specific topics. By examining the topics of a document, we can quickly understand what the document is about. To augment the traditional manual way of topic tagging tasks, which is labor-intensive, solutions using computers have been developed. This dissertation describes the design and development of a topic identification approach, in this case applied to disaster events. In a sense, this study represents the marriage of research analysis with an engineering effort in that it combines inspiration from Cognitive Informatics with a practical model from Information Retrieval. One of the design constraints, however, is that the Web was used as a universal knowledge source, which was essential in accessing the required information for inferring topics from texts. Retrieving specific information of interest from such a vast information source was achieved by querying a search engine's application programming interface. Specifically, the information gathered was processed mainly by incorporating the Vector Space Model from the Information Retrieval field. As a proof of concept, we subsequently developed and evaluated a prototype tool, Xpantrac, which is able to run in a batch mode to automatically process text documents. A user interface of Xpantrac also was constructed to support an interactive semi-automatic topic tagging application, which was subsequently assessed via a usability study. Throughout the design, development, and evaluation of these various study components, we detail how the hypotheses and research questions of this dissertation have been supported and answered. We also present that our overarching goal, which was the identification of topics in a human-comparable way without depending on a large training set or a corpus, has been achieved. / Ph. D.

This study examines The Guardian’s portrayal of intersectional feminism, with a focus on gender and race, analysing how social movements, particularly Black Lives Matter, influence its political coverage. Arising from Kimberlé Crenshaw’s concept of intersectionality, which recognizes the interconnected nature of various forms of oppression and privilege, the research employs a combination of quantitative and qualitative methods to analyse 647 political articles. Quantitative methods, including topic modeling and keyword frequency analysis, provide the structural framework of the thesis. Topic modeling reveals twenty topics, and keyword frequency analysis emphasizes in nine keywords related to intersectional feminism and their prevalence. Qualitative methods, such as collocation analysis and close reading, examine particularly “gender” and “race”. Close reading is used for a deeper examination at every step of the analysis. Despite theme variations, certain subjects like the gender gap and gender identity consistently underscore their enduring significance. Discussions related to Black Lives Matter show spikes in coverage post – 2020, indicating an increased emphasis on diversity and racial justice themes. However, the infrequent use of the term “intersectionality” suggests a potential disparity between the conceptual framework and its direct representation in The Guardian’s political articles.

In many ways, science represents a complex system which involves technical, social, and economic aspects. An analysis of such a system requires employing and combining different methodological perspectives and incorporation of different sources of data. In this dissertation, we use a variety of methods to analyze large sets of data in order to examine the effects of various domestic and institutional factors on scientific activities. First, we evaluate how the contributions of behavioral and social sciences to studies of health have evolved over time. We use data analytics to conduct a textual analysis of more than 200,000 publications on the topic of HIV/AIDS. We find that the focus of the scientific community within the context of the same problem varies as the societal context of the problem changes. Specifically, we uncover that the focus on the behavioral and social aspects of HIV/AIDS has increased over time and varies in different countries. Further, we show that this variation is related to the mortality level that the disease causes in each country. Second, we investigate how different sources of funding affect the science enterprise differently. We use data analytics to analyze more than 60,000 papers published on the subject of specific diseases globally and highlight the role of philanthropic money in these domains. We find that philanthropies tend to have a more practical approach in health studies as compared with public funders. We further show that they are also concerned with the economic, policy related, social, and behavioral aspects of the diseases. We uncover that philanthropies tend to mix and combine approaches and contents supported both by public and private sources of funding for science. We further show that in doing so, philanthropies tend to be closer to the position held by the public sector in the context of health studies. Finally, we find that studies funded by philanthropies tend to receive higher citations, and hence have higher impact, in comparison to those funded by the public sector. Third, we study the effect of different schemes of funding distribution on the career of scientists. In this study, we develop a system dynamics model for analyzing a scientist's career under different funding and competition contexts. We investigate the characteristics of optimal strategies and also the equilibrium points for the cases of scientists competing for financial resources. We show that a policy to fund the best can lead scientists to spend more time on writing proposals, in order to secure funding, rather than writing papers. We find that when everyone receives funding (or have the same chance of receiving funding) the overall optimal payoff of the scientists reaches its highest level and at this optimum, scientists spend all their time on writing papers rather than writing proposals. Our analysis suggests that more egalitarian distributions of funding results in higher overall research output by scientists. We also find that luck plays an important role in the success of scientists. We show that following the optimal strategies do not guarantee success. Due to the stochastic nature of funding decisions, some will eventually fail. The failure is not due to scientists' faulty decisions, but rather simply due to their lack of luck. / Ph. D. / Science helps us understand the world and enables us to improve how we interact with our environment. But science itself has also been the subject of inquiry by philosophers, sociologists, economists, historians, and scientists. The goal in the investigations of science has been to better understand how scientific advances occur, how to foster innovation, and how to improve the institutions that push science forward. This dissertation contributes to this area of research by asking and responding to several questions about the science enterprise. First, we study how communities of scientists in different parts of the world look at the seemingly same problem differently. We use a computational method to read through a large set of publications on the topic of HIV/AIDS (which includes more than 200,000 papers) and uncover the topics of these papers. We find that in the context of HIV/AIDS, contributions of behavioral and social scientists have increased over time. Moreover, we show that the share of these contributions in any counties’ total research output differs significantly. We further find that there is a significant relationship between one country’s rate of death, due to HIV/AIDS, and the share of behavioral and social studies in the overall research profile of that country on the topic of HIV/AIDS. Second, we investigate how different sources of research funding affect scientific activities differently. Specifically, we focus on the role of philanthropic money in science and its effect on the content and impact of research studies. In our analysis, we rely on computational techniques that distinguishes between different themes of research in the studies of a few diseases and also different statistical methods. We find that philanthropies tend to have a more practical approach to health studies as compared with public sources of funding. Meanwhile, we find that they are also concerned with the economic, policy related, social, and behavioral aspects of the diseases. Moreover, we show that philanthropies tend to mix and combine approaches and contents supported both by public and private sources of funding for science. We find that, in doing so, philanthropies tend to be closer to the position held by the public sector in the context of health studies. Finally, we show that studies funded by philanthropies tend to receive higher citations. This finding suggests that these studies have a higher impact in comparison to those funded by the public sector. Third, we study how different mechanisms for distributing research funding among scientists can affect their career and success. Many scientists should spend time on both writing papers and research grant proposals. In this work, we aim at understanding how a scientists should allocate her time between these two activities to maximize her career long number of papers. We develop a small mathematical model to capture the mechanisms related to the research career of a scientist in an academic setting. Then, for different schemes of funding distribution, we find the scientist’s time allocation that maximizes the number of papers she publishes over her career. We find that when funding is being allocated to the best scientists and best grant proposals, scientists’ best strategy is to spend more time on writing research grant proposals rather than papers. This decreases the total number of papers published by the scientists over their career. We also find that luck is important in determining the career success of scientists. Due to errors in evaluation of proposal qualities, a scientist may fail in her career regardless of whether she has followed the best strategy that she could.

Social media has recently become an essential component of our daily modern life, with platforms like Facebook, YouTube, and Twitter serving as popular venues for people to share their opinions on various topics, including sustainable food. The interest in consumer sentiments towards sustainable practices has increased particularly after Covid-2019. This study investigates the attitudes and needs of Swedish consumers regarding sustainable food consumption as reflected in their social media interactions using 4588 comments from Facebook and YouTube. The methodology used are sentiment analysis and topic modelling with VADER and Latent Dirichlet Allocation (LDA) respectively. The results reveal a generally strong positive attitude toward sustainable food. However, the study observes further a decline in positive sentiments over time, indicating changing consumer opinions. The primary topic identified is market challenges, such as high pricing. Furthermore, health concerns and environmental considerations are identified both as important factors influencing the choice of sustainable food. The findings highlight the necessity for policy interventions to enhance the affordability and accessibility of sustainable food, as well as the effective use of social media for raising consumer awareness.

Sweden is generally regarded as progressive in politics related to LGBTQI people, and the work for LGBTQI-rights is often described as a success story. Nevertheless, success and resistance have coincided throughout history with different discourses characterizing the political conversation. The study’s aim was to identify and deconstruct the subject positioning of LGBTQI people in Swedish politics. Furthermore, I analysed how LGBTQI people are constructed as a threat or as threatened in relation to the majority society. Using the AI-based topic modeling tool BERTopic, speeches from parliamentary debates from the period 2010–2023 were sampled for a qualitative discourse analysis. The theoretical framework consists of discourse theory, intersectionality, and concepts from queer- and postcolonial theory. The results show that the positioning of LGBTQI people is made in a hegemonic vulnerability discourse. The construction of LGBTQI people as vulnerable relates to an unwanted social development in which LGBTQI people are positioned as an already vulnerable group risking further vulnerability. Two competing discourses emerge, one that constructs threats to LGBTQI people as imported problems, and one that constructs LGBTQI people as threatened by right-wing nationalism. When LGBTQI people are constructed as a threat, it is primarily a threat to the prevailing gender order.

In this paper we investigate the relationship of Information Science (IS) and the Digital Humanities (DH) by means of a scientometric comparison of academic journals from the respective disciplines. In order to identify scholarly practices for both disciplines, we apply a recent variant of LDA topic modeling that makes use of additional hierarchical clustering. The results reveal the existence of characteristic topic areas for both IS (information retrieval, information seeking behavior, scientometrics) and DH (computational linguistics, distant reading and digital editions) that can be used to distinguish them as disciplines in their own right. However, there is also a larger shared area of practices related to information management and also a few shared topic clusters that indicate a common ground for – mostly methodological – exchange between the two disciplines.

Keyword analysis in scientific articles is a method used to identify and evaluate the importance and relevance of specific words or phrases (keywords) within scientific literature. The primary goal of keyword analysis is to uncover the core themes, research trends, and conceptual frameworks within a given field or across multiple disciplines. It helps researchers understand scientific discourse's focus and ideas' evolution over time. This thesis performs keyword analysis on a repository of scientific publications through a combination of methods. It starts with extracting the available keywords, and it deals with the missing keywords data through data augmentation. Then, it utilizes a variety of statistical methods to gain insight into the publications. The study employs an implementation of LDA topic modeling to accurately categorize keywords into thematic groups, a Vector autoregression to explore keyword relationships, and temporal dynamics of keywords. Next, the research further examines the interdisciplinary connectivity of keywords, clarifying the collective nature of modern science. In conclusion, the thesis presents a comprehensive framework for keyword analysis in scientific literature, through a blend of data augmentation, natural language processing, temporal dynamics, and interdisciplinary examination, the study provides a robust tool for understanding the development and structure of scientific literature. The findings of this research have important implications for scholars, it allows navigating the vast amount of scientific literature more effectively and to discern the most influential ideas and trends shaping target fields. The methodologies implemented here offer an opportunity for any studies to methodologically search, extract, and identify keywords to find relevant papers and interpret the complex landscape of scientific communication. / Nyckelordsanalys i vetenskapliga artiklar är en metod som används för att identifiera och utvärdera vikten och relevansen av specifika ord eller fraser (nyckelord) inom vetenskaplig litteratur. Det primära målet med nyckelordsanalys är att avslöja kärnteman, forskningstrender och konceptuella ramverk inom ett givet fält eller över flera lämnar. Det hjälper forskare att förstå den vetenskapliga diskursens fokus och idéernas utveckling över tid. Denna avhandling utför nyckelordsanalys på ett arkiv av vetenskapliga publikationer genom en kombination av metoder. Den börjar med att extrahera de tillgängliga nyckelorden och hanterar de saknade nyckelordsdata genom dataaugtation. Därefter använder den en mängd statistiska metoder för att få insikt i publikationerna. Studien använder en implementering av LDA-ämnesmodellering för att noggrant kategorisera nyckelord i tematiska grupper, en vektorautoregression för att utforska nyckelordsrelationer och tidsmässig dynamik av nyckelord. Nästa steg i forskningen är att ytterligare undersöka den tvärvetenskapliga kopplingen mellan nyckelord, vilket klargör den kollektiva naturen av modern vetenskap. Sammanfattningsvis presenterar avhandlingen ett omfattande ramverk för nyckelordsanalys i vetenskaplig litteratur. Genom en blandning av dataaugmentation, naturlig språkbehandling, tidsmässig dynamik och tvärvetenskaplig undersökning, erbjuder studien ett robust verktyg för att förstå utvecklingen och strukturen av vetenskaplig litteratur. Forskningens resultat har viktiga implikationer för forskare; det möjliggör effektivare navigering i den omfattande mängden vetenskaplig litteratur och att urskilja de mest inflytdrikelserika idéerna och trenderna som formar målfälten. De metoder som införas här erbjuder en möjlighet för vilken studie som helst att metodiskt söka, extrahera och identifiera nyckelord för att hitta relevanta artiklar och tolka det komplexa landskapet av vetenskaplig kommunikation.

The aim of this study was to analyze the salience of issue dimensions in the Swedish parliament debates by the established parties during the rise of the Sweden Democrats Party (SD). Structural topic modeling was used to construct a measurement of the salience of issues, examining the full body of speeches in the Swedish parliament between September 2006 and December 2019. Trend analysis revealed a realignment from a focus on socio-economic to socio-cultural issues in Swedish politics. Cross-correlation analyses had conflicting results, indicating a weak positive relationship between the salience of issues and the support of SD – but low predictive ability; it also showed that changes in the support of SD did lead (precede) changes in the salience of issues in the parliament. The ramifications of socio-cultural issues being the most salient are that so-called radical right-wing populist parties (RRPs), or neo-nationalist parties, has a greater opportunity to gain support. It can make voters more inclined to base their voting decision on socio-cultural issues, which favors parties who fight for and are trustworthy in those issues – giving them more valence in the eyes of the voters.