Browsing by study line "Politics and Security"

Gender inequalities persist in today's world and women face problems in many areas. One view is that the first thing to guarantee women's rights is to guarantee their political rights. By increasing women's political participation and their influence at the decision-making and leadership levels in the legislature and the executive, more female-friendly policies can be implemented and the gender gap can be narrowed. Finland was chosen for this thesis because of the relatively active political participation of Finnish women and because Finland has a high international index of female equality. The research materials are mainly UN documents and Finnish government reports. The Finnish material is drawn from Finland's follow-up reports to the 1995 Fourth World Conference on Women in Beijing, which are more continuous and reflect Finland's work on advancing gender equality over the last fifteen years. In addition, the thesis attempts to define women-friendly policies. Finally, this thesis discusses the contribution of women's political participation to the promotion of women-friendly policies, acknowledging its role and feasibility while pointing out some shortcomings and limitations.

This thesis examines computational propaganda techniques employed by RT (Russia Today) on Twitter by analyzing tweet sentiment and propaganda dynamics. Through quantitative analysis of RT English tweets spanning a decade, the study reveals RT’s strategies in terms of sentiment manipulation, including positive portrayals of Russia and Putin and demonization of adversaries. It further identifies a discernible political bias in RT’s coverage of international relations. The analysis also explores changes in country depictions within RT reports over the past ten years, highlighting the stability of RT’s narratives towards Putin, which suggests the importance of maintaining Putin’s image for Russian state-owned media. Moreover, the study identifies a correlation between the sentiment of tweets related to Russia, the extent of coverage of Putin, and Putin's approval ratings, shedding light on the dynamic nature of RT propaganda and its possible influencing factors. In terms of methodology, machine learning and dictionary-based approaches conducted in sentiment classification yield robust results, indicating the potential of computational methods in social science studies. In general, the research contributes to the understanding of modern propaganda and offers insights into the application of artificial intelligence in social science.