This thesis explores the use of emotes in a synchronous environment where hundreds to thousands of people communicate with each other in different discourse communities. The thesis performs a study on emote use based on the frequencies they are used and their different CMC features in these communities to acquire knowledge on the contextual basis of emote use in synchronous environments. By using various corpus tools and implementing both a quantitative analysis and qualitative analysis, the results show that the frequency of messages that use emotes varies based on the discourse community that is involved in the use of the emotes. The different CMC features are covered by the basis of the discourse community’s behavior, where there is also some involvement based on the size of the different discourse communities on Twitch.

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Den tekniska utvecklingen bidrar till nya möjligheteter och sätt att kommunicera på nätet. Grafikoner introduceras successivt i Facebook och andra sociala medier och erbjuder nya funktioner för användare. I denna studie görs en jämförande analys av grafikoners användning och pragmatiska funktioner i en Facebookgrupp mellan 2012 och 2022. Syftet är att undersöka och diskutera grafikoners utveckling i en svensk kontext. Analysen är avgränsad till följande grafikontyper: emotikoner, emojier och dekaler. Grafikoner i inlägg och kommentarer analyseras med utgångspunkt i tidigare forskning om grafikoners funktioner. Funktionerna som identifieras i denna studie är: dekoration, benämning, handling/gest, reaktion och modifikation av ton. Studiens resultat visar att emotikoner endast förekommer i enstaka fall i materialet från 2012 och inte alls i materialet från 2022. Användningen av emojier har däremot ökat avsevärt mellan 2012 och 2022. I materialet från 2012 fyller emotikoner och emojier liknande funktioner, vilka främst är som modifikation av ton och som reaktion. Den vanligaste emojifunktionen i materialet från 2022 är som handling/gest. Resultatet visar en mer varierad användning av emojier i form av typ, placering och funktion 2022 jämfört med 2012. Den breddade användningen indikerar att emojin fortfarande är aktuell och användbar för svenska sociala media-användare 2022. Dekaler finns endast representerade i materialet från 2022 och den vanligaste funktionen är som reaktion på tidigare innehåll. Dekaler används fortfarande i mindre utsträckning än emojier 2022, men det finns indikationer som tyder på att dekalen kommer att följa samma utvecklingskurva som dess föregångare och öka i popularitet över tid.

La maladie de Lyme est la maladie transmise par tiques la plus répandue dans l’hémisphère du Nord. Le système de surveillance des cas humains de la maladie de Lyme est basé sur un système passif des cas par les professionnels de santé qui présente plusieurs failles rendant la surveillance incomplète. Avec l’expansion de l’usage de l’internet et des réseaux sociaux, des chercheurs proposent l’utilisation des données provenant des réseaux sociaux comme outil de surveillance, cette approche est appelée l’infodémiologie. Cette approche a été testée dans plusieurs études avec succès. L’objectif de ce mémoire est de construire une base de données à partir des tweets auto-déclarés, des tweets classifiés et étiquetés comme un cas potentiel de Lyme ou non à l’aide des modèles de classificateurs basés sur des transformateurs comme, BERTweet, DistilBERT et ALBERT. Pour ce faire, un total de 20 000 tweets en anglais en lien avec la maladie de Lyme sans restriction géographique de 2010 à 2022 a été collecté avec la plateforme API twitter. Nous avons procédé au nettoyage la base de données. Ensuite les données nettoyées ont été classifiées en binaire comme cas potentiels ou non de la maladie de Lyme sur la base des symptômes de la maladie comme mots-clés. À l’aide des modèles de classification basés sur les transformateurs, la classification automatique des données est évaluée en premier sans, et ensuite avec des émojis convertis en mots. Nous avons trouvé que les modèles de classification basés sur les transformateurs performent mieux que les modèles de classification classiques comme TF-IDF, Naive Bayes et autres ; surtout le modèle BERTweet a surpassé tous les modèles évalués avec un score F1 moyen de 89,3%, une précision de 97%, une exactitude de 90% et un rappel de 82,6%. Aussi l’incorporation des émojis dans notre base de données améliore la performance de tous les modèles d’au moins 5% mais BERTweet a une fois de plus le mieux performé avec une augmentation de tous les paramètres évalués. Les tweets en anglais sont majoritairement en provenance des États-Unis et pour contrecarrer cette prédominance, les futurs travaux devraient collecter des tweets de toutes langues en lien avec la maladie de Lyme surtout parce que les pays européens où la maladie de Lyme sont en émergence ne sont pas des pays anglophones. / Lyme disease is the most common tick-borne disease in the Northern Hemisphere. The surveillance system for human cases of Lyme disease has several flaws which make the surveillance incomplete. Nowadays with the extensive use of internet and social networks, researchers propose the use of data from social networks as a surveillance tool, this approach is called Infodemiology. This approach has been successfully tested in several studies. The aim of this thesis is to build a database from self-reported tweets, capable of classifying a tweet as a potential case of Lyme or not using BERT transformer-based classifier models. A total of 20,000 English tweets related to Lyme disease without geographical restriction from 2010 to 2022 were collected with twitter API. Then these data were cleaned and manually classified by binary classification as potential Lyme cases or not using as keywords the symptoms of Lyme disease; Also, emojis have been converted into words and integrated. Using classification models based on BERT transformers, the labeling of data as disease-related or non-disease-related is evaluated first without, and then with emojis. Transformer-based classification models performed better than conventional classification models, especially the BERTweet model outperformed all evaluated models with an average F1 score of 89.3%, precision of 97%, accuracy of 90%, and recall of 82.6%. Also, the incorporation of emojis in our database improves the performance of all models by at least 5% but BERTweet once again performed best with an increase in all parameters evaluated. Tweets in English are mostly from the United States and to counteract this predominance, future work should collect tweets of all languages related to Lyme disease especially because the European countries where Lyme disease are emerging are not English-speaking countries.

Emojis can be ambiguous, even when used within one and the same language and geographic region, but they are also a globally accessible language in computer-mediated communication. This paper aims to examine if emoji usage across five different national varieties of English (American, English, Australian, Indian, and Nigerian), geographically located on five different continents, exhibits similar pragmatic functionality and sentiment. To achieve this aim, an analysis was made into the usage of three of the most frequently used emojis in tweets written by users from these five English-speaking countries. The number of tweets analyzed is 50 per selected emoji per selected variety of English, for a total of 750 tweets. The analytical process was to qualitatively determine the pragmatic function and sentiment the selected emoji exhibited in tweets. The results indicated that the pragmatic functionality and sentiment of the targeted emojis across the samples were generally similar, especially for Loudly Crying Face, but also for Face with Tears of Joy, whereas Sparkles showed more individual differences across the samples. A substantial finding was that it was possible to analyze facial and non-facial emojis in the same way. / Emojis kan vara tvetydliga, även inom samma språk och geografiska region, men de utgör också ett globalt tillgängligt språk inom datorförmedlad kommunikation. Syftet med uppsatsen är att fastställa om användningen av emojis inom fem olika nationella varianter av engelska (amerikansk, engelsk, australiensk, indisk, och nigeriansk), geografiskt lokaliserade på fem olika kontinenter, uppvisar liknande pragmatisk funktionalitet och attityd. För att uppnå syftet analyserades användningen av tre av de mest använda emojisarna i tweets skrivna av användare från dessa fem engelsktalande länder. Antalet tweets som har analyserats är 50 per utvald emoji per nationell variant av engelska, sammanlagt 750 tweets. Den analytiska processen var baserad kring att bestämma den pragmatiska funktionen och den attityd som de utvalda emojisarna uppvisade i tweets. Resultatet indikerade att den pragmatiska funktionaliteten och attityden hos de utvalda emojisarna var generellt sett snarlika över urvalen, speciellt för Ljudligt gråtande ansikte, men även för Ansikte med tårar av skratt, medan Glitter påvisade mer individuella skillnader över urvalen. En betydande upptäckt var att det var möjligt att analysera faciala och icke-faciala emojisar på samma sätt.

Millennials are not as committed to charity as the generations before them. Millennials have also been proven very difficult to attract, which is problematic since Millennials are the future donors’ charity organizations must depend on. The purpose of this study is therefore to identify possible patterns, relationships and opportunities that would make Millennials donate to charity. The study presents the current situation of charity on the Swedish market, which follows by an introduction for how non-profit promote themselves and do their marketing. This leads to the research question: How should charity organizations promote themselves to get Millennials to become loyal? This study applies a qualitative research strategy. The primary data has been collected through semi-structured interviews with five representatives from five different charity organizations. All five organizations work with international projects.   The result shows that the five organizations have knowledge about how to market to attract and engage Millennials, but they do not apply this knowledge. The result presents four paradoxes. We consider that the perception of these paradoxes, as well as responding to them, determines the charity organizations outcomes and overall ability to attract Millennials. The result also shows that to engage with Millennials and create long-lasting relationships, charity organizations marketing must be innovative, cool, and creative with repetitive messages. Charity organizations also must use the communication channels that Millennials engage with. The result has given rise to the "Interactional Model for Targeting Millennials", which is built on 11 key factors that charity organizations must apply to establish, develop and maintain a relationship with Millennials.

Fans and fandoms are ever more salient aspects of our everyday lives offline and linked to the Internet's growing influence also online, particularly on social media. While fans have generally been pathologized via mass media but also early academic representations especially prior to the founding of the interdisciplinary Fan Studies in the 1970s/1980s, which sought to actively counter negative fan representations and foreground fans' creative productivity, nowadays, even though many types of fans have been 'mainstreamed' and are generally accepted, specific fan types are still systematically discriminated against - even within Fan Studies - along the lines of socio-demographic variables. These marginalised fans are predominantly female, young, queer and non-white. Moreover, even though Fan Studies define fan identity as one of their focal concerns, linguistic research on fan identity, particularly regarding its narrative and interactive construction, has widely been neglected. However, as narrative interaction and specifically small stories (as propsed within the small story paradigm by Bamberg & Georgakopoulou, 2007/8) have been found to play a very important role in the construction of identity, the investigation of how fan identity is constructed via small stories and - given the centrality of collective fandoms for fans - specifically shared (group) stories can severely contribute to fan (identity) research. Thus, combining decidedly linguistic research on narrative fan identity construction and the inclusion of previously marginalised fan communities, this thesis focuses on the construction of fan identity of Taylor Swift fans (Swifties) - a predominantly female and young fandom that has been ridiculed by mass media and dominant discourses - via shared stories. More specifically this study analyses the construction of Swiftie fan identity via shared stories both online in nicknames on Tumblr and Twitter and face to face in the form of a positioning analysis investigating the interactions of a Zoom focus group made up of five German Swifties. This research finds that within Swiftie nicknames Swiftie fan identity is centrally constructed by means of variously highly condensed, combined and/or personalised references (to shared stories of the overarching Swiftie community). The focus group interactions then reveal various positioning practices that are strongly intertwined with (often) more elaborate shared stories, which are 'shared' by the Swiftie participants both with regards to experiences on the story level and their interactive co-construction on the level of interaction. Despite their diverging local manifestations both within the investigated Swiftie nicknames and focus group interactions shared stories are centrally utilised to construct and communicate Swiftie fan identity as a particularly collectively experienced and defined ingroup identity that confers belonging and further functions as a shield against outgroup discrimination. Further research should then enlarge the present investigative focus to include also other online platforms and fan communicative acts, supplementary and also offline implemented focus groups and field studies, more heterogenous participants with regard to often neglected socio-demographic variables (next to age and gender) as well as other (marginalised) fandoms outside of the Swiftie community.