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1	$GME To The Moon : Mapping Memetic Discourse as Discursive Strategyin Reddit Trading Community r/WallStreetBets during the GameStop Short Squeeze Saga Olofsson, Simon January 2021 (has links) As social media has emerged to become a key site for contemporary communications and cultural production, the internet meme has penetrated every level of social networking online. Albeit being a global phenomenon with pervasive discursive power in a number of fields ranging from humour to international politics and cyber warfare, comparatively little research has been made into how internet memes work on the discursive level of identity formation and their influence on the formation of internet-based social movements. Using Reddit stock market anarchists r/WallStreetBets as case study, this thesis will use Critical Discourse Analysis to analyze how internet memes work on the level of socio-political formations and how their function can be understood in relation to entropic social environments online. This thesis investigates how internet memes are used as a tool for creation of motifs for action, identity markers, connective action, and social narrativization within an ambivalent social movement online. Introducing the novel term ”memetic discourse” as a way to understand memes as transferable units of memetically programmed content, this study shows the potential of memes to act as effective yet unstable modes of communication within networked environments. internet memes memetic discourse Reddit r/wallstreetbets WSB GameStop GME meme stock memestock short squeeze activist trading social movements connective action critical discourse analysis CDA Media Studies Medievetenskap
2	Predicting Stock Market Movement Using Machine Learning : Through r/wallstreetbets sentiment & Google Trends, Herding versus Wisdom of Crowds Norinder, Niklas January 2022 (has links) Stock market analysis is a hot-button topic, especially with the growth of online communities surrounding trading and investment. The goal of this paper is to examine the sentiment of r/wallstreetbets and the Google Trends score for a number of stocks – and then understanding whether the herding nature of investors on r/wallstreetbets is better at predicting the movement of the stock market than the WOC nature of Google Trends scores. Some combination of the herding and WOC values will also be used in predicting stock market fluctuations. Analysis will be done through the machine learning algorithms RFC and MLP. Through the mean and median precisions presented by the different machine learning algorithms the effectiveness of the predictor can be understood. This paper finds no real connection between either r/wallstreetbets sentiment or Google Trends data regarding predicting stock value fluctuations – with r/wallstreetbets yielding approximately 51%-52% mean precision depending on the machine learning algorithm used, and Google Trends precisions sitting at around 51%. The combination of r/wallstreetbets data and Google Trends data did not produce any significantly higher precision either, being between 51%-52%. Sentiment Analysis Machine Learning Stock Movement Prediction r/wallstreetbets Google Trends Business Administration Företagsekonomi Economics and Business Ekonomi och näringsliv Computer and Information Sciences Data- och informationsvetenskap

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