Historically author profiling has been used in forensic linguistics. However, it is not until the last decades that the analysis method has worked into computer science and machine learning. In comparison, determining author profiling characteristics in machine learning is nothing new. This paper investigates the possibility to improve upon previous results with modern frameworks using data sets that have seen limited usage. The purpose of this master thesis was to use pre-trained transformers or embeddings together with transfer learning. In addition, to examine if general author profiling characteristics of anonymous users on internet forums or conversations on social media could be determined. The data sets used to investigate the questions above were PAN15 and PANDORA, which contains various properties in text data based on authors paired with ground truth labels such as gender, age, and Big Five/OCEAN. In addition, transfer learning of BERT and GloVe was used as a starting point to decrease the learning time of a new task. PAN15, a Twitter data set, did not contain enough data when training a model and was augmented using PANDORA, a Reddit-based data set. Ultimately, BERT obtained the best performance using a stacked approach, achieving 86 − 91% accuracy for each label on unseen data.

Background: There has been a noticeable growth in the use of sponsorships and corporate social responsibility in the world of professional sports, especially in football. This phenomenon is not particularly surprising given that modern companies are forced to compete for the attention of more socially conscious audiences. The socially responsible reputation of a firm may be significantly strengthened by incorporating CSR and CRM efforts. Audiences have shown to have a more positive opinion of these initiatives when they are implemented in the sports industry, often differentiating them from direct corporate marketing due to their perceived authenticity and compassion. Hummel's decision to tone down the Danish national team's kit for The 2022 FIFA World Cup in Qatar serves as a compelling example of cause-related marketing within the football industry.   Purpose: The development of an in-depth understanding of how online communities perceive the cause-related campaign executed by Hummel at The 2022 FIFA World Cup in Qatar. Method: Qualitative Research; Interpretivism; Methodology – Exploratory Case Study; Inductive Approach; Data Collection – Systematic; Sampling – Deliberate/Nonprobability Sampling; Data Analysis – Content Analysis (creation of tree-diagrams based on quotes, generic categories and main categories)  Conclusion: The authors have undertaken a comprehensive analysis of the findings derived from content analysis, establishing meaningful connections between these findings and relevant theories regarding cause-related marketing (CRM) and corporate social responsibility (CSR). As a result, this study makes a contribution to the scholarly understanding of the potential responses exhibited by online communities towards cause-related marketing campaigns. The findings of this study shed light on the diverse range of reactions that can be anticipated when a company chooses to engage in such sponsorship endeavors.

Les échanges au sujet de phénomènes paranormaux sont présents et bien installés sur le web. Le choix de plateformes qui s’offre aux utilisateurs est vaste pour partager son expérience avec les autres. Certains choisissent Reddit et particulièrement des subreddits dédiés au paranormal. Ce mémoire analyse une communauté en ligne, active sur le subreddit r/Ghosts, constituée de fans des phénomènes paranormaux. Je m’interroge sur les moyens mis en œuvre par cette communauté pour interagir dans le but de faire sens de la question de la potentielle existence des fantômes. À l’aide de concepts comme l’interactionnisme symbolique, la culture participative et l’intelligence collective, j’ai analysé une quarantaine d’interactions textuelles issues d’un partage de contenu médiatique. Les membres de la communauté, appelés redditors, interagissent pour trouver des solutions à des énigmes perçues comme étant de nature paranormale, mais aussi pour discuter et se divertir. Ces redditors interagissent avec différentes valeurs et croyances, parfois partagées. / The web is awash with conversations about paranormal activity. There are a lot of platforms on which users can share their experience. Some choose to pick Reddit and particularly subreddits dedicated to the paranormal. This master’s thesis studies the subreddit r/Ghosts, an online community (OC) dedicated to ghosts. Here, I question the means used by this OC to make sense of the possible existence of ghosts via interaction. Using theories and concepts such as symbolic interactionism, participatory culture, and collective intelligence, I studied over forty textual interactions initiated by a post including a media (picture, video, or audio). The redditors of r/Ghosts interact with each other to figure out paranormal enigmas but also to simply discuss or for entertainment purposes. They interact with different values and beliefs that they sometimes share.

Distinguishing between information and disinformation is an ever growing issue. The dramatic structure of a conspiracy theory easily captures a large audience and with the advent of social media, this disinformation can spread at an ever growing rate. This is especially true with the infodemic following the Covid-19 pandemic in early 2020, where there was a drastic increase in Covid-19 related misinformation on social media. When misinformation replaces fact, some people will inevitably follow borderline dangerous advice. This could unfortunately be seen in the ivermection issue where people injected this substance in hope of preventing/curing a Covid-19 infection. This is why finding patterns in disinformation that distinguishes it from facts would allow us to take measures against the spread of conspiracy theories. We have found patterns in our dataset suggesting that there is a significant difference in the language patterns for terms relating to conspiracy theories, and non-conspiratorial terms. We find that the sentiment of conspiracy theories is very volatile when compared to that of non-conspiratorial terms which follow a more neutral pattern in terms of sentiment. Suggesting that the language usage in a post can be used as a factor when determining the credibility of its content. We also find that conspiracy theories tend to see a drastic increase in mentions when previously being relatively lowin mentions. The result of this thesis could therefore be used as a start for developing tools and processes which would seek to combat the spread of conspiracy theories and limit the potential harm that could come from them.

This thesis examines the online fan community of the HBO series The Last of Us on the social media platform Reddit. Within this study, the fans’ communication practices on the subreddit r/ThelastofusHBOseries are researched and analyzed from a community-building perspective. The subreddit was observed employing a netnographic approach. The observed communicational behavior was divided into categories. The patterns arising from this process were analyzed using discourse analysis and a theoretical framework rooted in fandom, knowledge communities, and uses and gratifications theory.  The research’s results show that the community on r/ThelastofusHBOseries provides a space for fans to connect, share their passion, and engage in meaningful discussions about the show. It embodies convergence culture, the value of mutual knowledge, and participatory practices. The use of insider language, adherence to spoiler etiquette, and organization of regular events further contribute to community building. The subreddit is a valuable platform for fans to exchange interpretations, enrich their reception experience, and foster community belonging. While extensive research has explored fan communities, there is a lack of studies on their online community-building practices. Notably, the subreddit r/ThelastofusHBOseries remains unexplored due to its novelty, alluding to the study’s relevance. Despite the limitations concerning its time frame, missing complementary methods like interviews, and possible sampling bias, the research is still a valuable snapshot, providing significant insights and findings. Future research could thus observe the community’s development over time to examine evolving communication and community building, paying a particular focus to regular events or insider language. Future research would also benefit from complementary methods.

Social media has proven to affect the dynamics of the stock market directly. The potential influence of social media makes it an excellent tool for stock market manipulation, especially in the era of online misinformation. Therefore, the work in this thesis aims to provide a better understanding of company discussion on social media. More precisely, collecting a dataset of company-specific discussion from Twitter, Reddit, Seeking Alpha, and Citron Research enabled us to perform time series analysis using smoothing and clustering, where associated events were identified, categorized, and summarized. A selected companies' time series was also more closely and qualitatively analyzed to build intuition and understanding of the dynamics. The results show that the dynamics of company discussion on social media can be evaluated using the discussion intensities. They also show the possibility of measuring whether a specific social media is, in general, reactive or proactive to abnormal events in the stock market. Moreover, external events seem to trigger discussion activity which propagates through all social media. Furthermore, the results seem to depend on the choice of bandwidth used for smoothing and the number of clusters given to the clustering algorithm, which requires further refinement as a continuation of this thesis.