As the discussion of stocks on social media is increasing its effect on the financial market is distinct. This has led to new opportunities in influencing private investors to make uninformed decisions affecting the value of stocks. This thesis aims to enable readers to distinguish patterns in social media discussion regarding stocks and thus provide an understanding of the effect it has on public opinion. By identifying significant events of big stock valuation changes and collecting corresponding stock-related data from the social media platforms Reddit and Twitter, analysis in the fields of frequency of posts and Sentiment analysis was performed. The results display trends of an increase in discussion on social media leading up to the occurrence of significant events and an overall increment of interest online for specific stocks after significant events have occurred. Furthermore, the overall sentiment in the discussion for both increasing and decreasing events is positive in almost every case, with the exception that the sentiment score of increasing events is higher than its counterpart. The day-to-day sentiment score during events indicates a much higher fluctuation in sentiment for Reddit compared to Twitter. However, a significant increase in score the day before an event occurs is prevalent for both. These findings imply the possibility to predict stock valuation changes using data gathered from social media platforms.

There is a growing interest in the utilization of digital services, such as software apps and cloud-based software services. The utilization of digital services enabled by ICT is increasing more rapidly than any other segment of the world trade. The availability of open data unlocks the possibility of generating huge market possibilities in the public and private sectors such as manufacturing, transportation, and trade. Digital service utilization can be improved by the adoption of cloud-based software services and through open data innovation for service development. However, open data has no value unless utilized and little is known about the development of digital services using open data. The use of contests to create awareness and call for crowd participation is vital to attract participation for digital service development. Also, digital innovation contests stimulate open data service development and are common means to generate digital services based on open data. Evaluation of digital service development processes stimulated by contests all the way to service deployment is indispensable. In spite of this, existing evaluation models are not specifically designed to measure open data innovation contest. Additionally, existing cloud-based digital service implications, opportunities and challenges, in literature are not prioritized and hence are not usable directly for adoption of cloud-based digital services. Furthermore, empirical research on user implications of cloud-based digital services is missing. Therefore, the purpose of this thesis is to facilitate the utilization of digital services by the adoption of cloud-based digital services and the development of digital services using open data. The main research question addressed in this thesis is: “How can contest-driven innovation of open data digital services be evaluated and the adoption of digital services be supported to improve the utilization of digital services?” The research approaches used are design science research, descriptive statistics, and case study for confirming the validity of the artifacts developed. The design science approach was used to design new artifacts for evaluating open data service development stimulated by contests. The descriptive statistics was applied on two surveys. The first one is for evaluating the implication of cloud-based digital service adoption. While the second one is a longitudinal survey to measure perceived barriers by external open data digital service developers. In this thesis, an evaluation model for digital innovation contest to stimulate service development, (Digital Innovation Contest Measurement Model) DICM-model, and (Designing and Refining DICM) DRD-method for designing and refining DICM-model to provide more agility are proposed. Additionally, the framework of barriers, constraining external developers of open data service, is also presented to better manage service deployment to enable viable service development. Organizers of open data innovation contests and project managers of digital service development are the beneficiaries of these arti-facts. The DICM-model and the DRD-method are used for the evaluation of contest and post contest deployment processes. Finally, the framework of adoption of cloud-based digital services is presented. This framework enables requirement engineers and cloud-based digital service adoption personnel to be able to prioritize factors responsible for an effective adoption. The automation of ideation, which is a key process of digital service development using open data, developer platforms assessment to suggest ways of including evaluation of innovation, ex-post evaluation of the proposed artifacts, and the expansion of cloud-based digital service adoption from the perspectives of sup-pliers are left for further investigations. / <p>DSV Report Series Series No. 18-008</p>

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This thesis examines the discussions on Reddit surrounding the launch of ChatGPTfrom late November 2022 until the end of March 2023. The objective of the study is to analyze the discussions concerning ChatGPT and how different topics have changed over time.Additionally, the thesis identifies significant events that have had an impact on the topicsand also how topics vary across different subreddits. To retrieve the data for the analysis, the PushShift API was used to gather almost half a million posts concerning ChatGPT.Topic modeling was then applied using BERTopic to identify common topics discussed onReddit and its unique subreddits. The results show several distinct topics, encompassing the technology behind ChatGPT, its societal implications, and its potential for creativeutilization. Furthermore, the thesis presents a clear correlation between significant newsconcerning ChatGPT and the frequency of posts on Reddit. Specifically, Microsoft’s investment in OpenAI and the incorporation of the GPT engine in Bing proved to have a greatinfluence on both the topics and frequency of posts. We also found some discrepancies between how subreddits discuss topics, most notably that more general topics tend to spreadout more, both over various subreddits as well as over time and being more sporadic, whilespecific topics tend to be more dictated by the occurence of significant events relevant tothe topic.

Machine learning and in particular deep-learning models have been in the spotlight for the last year. Particularly the release of ChatGPT caught the attention of the public. But many of the most popular models are large with millions or billions of parameters. Parallel with this, the number of smart products constituting the Internet of Things is rapidly increasing. The need for small resource-efficient machine-learning models can therefore be expected to increase in the coming years. This work investigates the implementation of two different models in embedded environments. The investigated models are, random forests, that are straight-forward and relatively easy to implement, and transformer models, that are more complex and challenging to implement. The process of training the models in a high-level language and implementing and running inference in a low-level language has been studied. It is shown that it is possible to train a transformer in Python and export it by hand to C, but that it comes with several challenges that should be taken into consideration before this approach is chosen. It is also shown that a transformer model can be successfully used for signal extraction, a new area of application. Different possible ways of optimizing the model, such as pruning and quantization, have been studied. Finally, it has been shown that a transformer model with an initial noise-filter performs better than the existing hand-written code on self-generated messages, but worse on real-world data. This indicates that the training data should be improved.

In 2020, 200 million children under the age of 5 were reported to be malnourished and between 720 and 811 million people around the world faced hunger. Yet, the global food production have the potential to feed every human being twice the amount required. So what is happening with all that food? 1.3 billion tonnes of the global food supply is wasted every year, which accounts for one third of the food produced. In Sweden, private households stand for 70% of the total waste. Food waste has been a problem for some time now. So, the goal with this study is to investigate reasons that contribute to this high food waste and suggest a solution or guidelines to prevent/reduce that in private households in the Skåne county. To explore the topic, academic literature were reviewed and Nine semi-structured interviews were conducted with the target group for this study, i.e., families living in Skåne county with children living at home and both parents working. Additionally, 103 responses were gathered through an online questionnaire from the same target group.  The findings revealed that families struggled with planning properly before they entered a grocery store, which meant that they ended up buying much more than they needed. Moreover, it was revealed that people had the tendency to get sidetracked during shopping. These practices, in most instances, resulted in double and over buying, and impulsive shopping, which meant that more food was going to waste without ever being consumed in their respective households.  With these findings in mind, we have hypothesized that online shopping has the potential to prevent food waste in private households, as well as created a design on how to get more people feeling comfortable doing grocery shopping online based on a human centred design approach.  To conclude this thesis, we define the contributing factors of household food waste and argue that food waste can be reduced by a significant amount if people are shopping online and are adhering to some sort of food budget to control their spendings.

This thesis takes a multidisciplinary approach to understanding the unique abilities of Volunteered Geographic Information (VGI) to enhance the utility of online mashups in ways not achievable with Professional Geographic Information (PGI). The key issues currently limiting the use of successful of VGI are the concern for quality, accuracy and value of the information, as well as the polarisation and bias of views within the user community. This thesis reviews different theoretical approaches in Human Factors, Geography, Information Science and Computer Science to help understand the notion of user judgements relative to VGI within an online environment (Chapter 2). Research methods relevant to a human factors investigation are also discussed (Chapter 3). (Chapter 5) The scoping study established the fundamental insights into the terminology and nature of VGI and PGI, a range of users were engaged through a series of qualitative interviews. This led the development of a framework on VGI (Chapter 4), and comparative description of users in relation to one another through a value framework (Chapter 5). Study Two produced qualitative multi-methods investigation into how users perceive VGI and PGI in use (Chapter 6), demonstrating similarities and the unique ability for VGI to provide utility to consumers. Chapter Seven and Study Three brought insight into the specific abilities for VGI to enhance the user judgement of online information within an information relevance context (Chapter 7 and 8). In understanding the outcomes of these studies, this thesis discusses how users perceive VGI as different from PGI in terms of its benefit to consumers from a user centred design perspective (Chapter 9). In particular, the degree to which user concerns are valid, the limitation of VGI in application and its potential strengths in enriching the user experiences of consumers engaged within an information search. In conclusion, specific contributions and avenues for further work are highlighted (Chapter 10).

Detta arbete fokuserade på att undersöka hur spelmusik påverkar spelarprestationer. I bakgrunden presenteras en kort historisk bakgrund av spelmusik samt studier som har gjorts inom ämnet. För att undersöka detta analyserades klassiska plattformsspelsbanor och utifrån den data som införskaffades via detta så skapades grunden för längden av spelet som skulle komma att utgöra artefakten. Efter detta så skapades ett ramverk för att kunna systematiskt bygga upp en nivå utifrån ett givet musikstycke. Resultaten av denna studie stärker idén om att musik påverkar spelarprestationer positivt. Den sammanställda datan indikerade dessutom att dynamisk musik verkar vara den typ som har störst påverkan på spelare överlag.

Denna studie har undersökt hur spelare motiverar sina val inom mekanisk karaktärsprogression i ett top-down ARPG. I bakgrunden presenteras en genomgång av mekanisk karaktärsprogression, hur det kan definieras, olika sätt att betrakta det samt olika sätt som det används i spel idag. För att undersöka detta skapades ett enkelt spel som innehöll mekanisk karaktärsprogression som spelades av 16 personer. Deltagarna deltog sedan i en semistrukturerad intervju och fyllde i en enkät för BrainHex, ett ramverk för spelartyper. Resultaten av studien visar att det kan finnas underlag för att vidare undersöka kopplingen mellan olika progressionssystem, även de som används inom samma spel. Den sammanställda datan visar även på att det kan finnas belägg för att spelare med spelartypen Conqueror gör val för att effektivisera sin karaktär. Dessa spelare verkar även till en stor utsträckning vara medvetna om kopplingen mellan förmågor och attributer, och hur dessa tillsammans kan effektivisera en karaktär.

This thesis considers the problem of churn within the electricity distribution sector. More specifically, this study evaluates how supervised machine learning can be used by a Swedish electricity distributor in order to identify customer churn. The data was by provided by the electricity distributor and covered personal, geographical and contract specific information regarding the company’s customers. The provided data was complemented with external data covering the customers’ financial positions. Based on this information the possibility to predict customer churn over a three-month period with a gradient boosted decision tree was evaluated. The results from the proposed models suggests that the possibility to identify customer churn is rather poor and could not be used in a practice. This is believed to be a result of unbalanced class distributions and that the data provided simply is not informative enough to accurately predict customer churn. If more information about the customers is collected, with predictive analyses in mind, the performance of the model is likely to increase.