This thesis was aimed to evaluate if sentiment related to stocks expressed on the subforum “Wallstreetbets” also reflects the traded volume in the stock market. For this purpose, a collection of comment data from posts filtered under the “Hot” section was issued between the 6th of April 2021 and the 20th of April 2021 on daily basis at 22.00 (GMT+2). The comments were preprocessed to filter out noise, and thereafter comments that contained mentions of stocks were analyzed using VADER, an algorithm for grading sentiment. In total sentiment regarding 13 different stocks were fitted into a mixed effect model with random slopes and intercepts. The results showed a positive correlation between sentiment expressed and the traded volume. This indicates that by studying the forum we can better understand how people invested in stocks make investment decisions, which potentially could lead to a competitive advantage over time.

In the beginning of 2021, the stock of strained and heavily shorted company GameStop rose by almost 2,000 percent. The meteoric rise was caused after users of the subreddit r/wallstreetbets mobilized to buy up stock of the business, hoping that short- sellers would be forced to close their positions, leading to further upside. The purpose of this study is to describe the culture of the subreddit, using Stuart Hall’s (1997) theory of representation to explain how users create meaning. Furthermore, we attempt to explain how the digital nature of the movement affected its structure and organisation, for which Bennett and Segerbergs (2012) framework The logic of connective action is applied. The study used a netnographic approach for data-collection, which was later analyzed using qualitative text- analysis and semiotic-image- analysis. In total, 50 different posts were analyzed which included both texts and memes. Our results indicate that the movement is characterized by the lack of hierarchy and a substantial amount of user- generated content. Narratives and motives have been naturally constructed collectively through the interaction of users rather than enforced by a central authority. A unique feature of the movement is its exclusively digital presence, allowing for fluid participation and individualized framing. The premises and boundaries of activism change as activism becomes more dependent on the digital world. This study offers insight into how digital movements can be constructed and how the digital fundamentally changes the preconditions of activism and mobilization.

Background: “APES TOGETHER STRONG“ was one of the slogans, that participants of the influential GameStop movement (who ironically called themselves “apes“) utilized to show their unity (“together strong“) and relentlessness in their seemingly irrational actions. Erupting in January 2021, retail investors that had formed a community via Reddit, collectively achieved to multiply the stock price of American gaming retail chain GameStop, causing huge losses for hedge funds, resulting in political discussions and social outrage. This community was quickly labelled as a social movement. Research Problem: Despite the widespread understanding that social media had and has major impacts on social movements and their constitution, research on various aspects concerning movements in the context of social media remain underdeveloped. On a broader level, the formation of digital social movements within online communities presents a suitable area of research. On a finer level, newcomers and their socialization were identified as research gaps. Research Purpose: The purpose of this study is to close these research gaps by identifying key factors of socialization within digital movements. Additionally, it aims at showcasing the implications of these factors on the broader community and movement development. Research Question: How are newcomers socialized and integrated in digital movements? Research Method: This study is a qualitative, inductive research. It follows the relativistic ontology and the social constructionism epistemology. The methodology is an explorative, single case study and data is purposively collected through interviews and from Reddit. The data is analyzed utilizing the Gioia method. Conclusion: Our findings concentrate on four dualities concerning socialization and community development. Community growth, purpose, jargon and activity are found to be inherently divergent themes and mechanisms within the movement. From this we abstract a framework towards a spectrum of socialization approaches ranging from regulated to unregulated socialization. As such, we showcase the implications of both ofthese ends and how communities must be flexible in their socialization approach.

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.

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%.