This thesis describes the design and implementation of a system for social media analysis. This system provides a way of identifying social media user's influence. The system has been open sourced under the MIT license and is designed to be easily extensible. Example usage of this system is demonstrated for a chosen use case of analysing several selected Czech individuals and political parties which are active on the Facebook social network. The thesis compared their influence and activity. A new way of activity and influence prediction has been proposed, based on the identification of dedicated users.

Social media has been getting more and more attention from common users as well as businesses. Although the level of social media adoption varies by sector and geographical location, all companies strive to understand which social media platforms adopt and how to effectively use them. There is a specific position for the winery sector, which is widely recognised as traditional. However, more and more people search for information, share information and purchase goods or services online, which made presence on social media inevitable even for wineries. There is a body of literature concerning wineries' social media adoption and usage, however there is none of such kind to be found in the Czech Republic. Thus, this paper's aim is to fill this gap and to find out to what extent do Czech wineries use social media and why. This paper investigates 96 responses from wineries from all regions of the Czech Republic. The evidence is that the common communication channels of Czech wineries are rather traditional, being it personal communication, email or phone. However, the level of social media adoption in comparison with Australia, Germany or New Zealand is rather high. Although Czech wineries value social media, there is a significant lack of knowledge in how to use them efficiently.

In this thesis, we have studied the problem of detecting automated Twitter accounts related to the Ukraine conflict using supervised learning. A striking problem with the collected data set is that it was initially lacking a ground truth. Traditionally, supervised learning approaches rely on manual annotation of training sets, but it incurs tedious work and becomes expensive for large and constantly changing collections. We present a novel approach to synthetically generate large amounts of labeled Twitter accounts for detection of automation using a rule-based classifier. It significantly reduces the effort and resources needed and speeds up the process of adapting classifiers to changes in the Twitter-domain. The classifiers were evaluated on a manually annotated test set of 1,000 Twitter accounts. The results show that rule-based classifier by itself achieves a precision of 94.6% and a recall of 52.9%. Furthermore, the results showed that classifiers based on supervised learning could learn from the synthetically generated labels. At best, the these machine learning based classifiers achieved a slightly lower precision of 94.1% compared to the rule-based classifier, but at a significantly better recall of 93.9% / Detta exjobb har undersökt problemet att detektera automatiserade Twitter-konton relaterade till Ukraina-konflikten genom att använda övervakade maskininlärningsmetoder. Ett slående problem med den insamlade datamängden var avsaknaden av träningsexempel. I övervakad maskininlärning brukar man traditionellt manuellt märka upp en träningsmängd. Detta medför dock långtråkigt arbete samt att det blir dyrt förstora och ständigt föränderliga datamängder. Vi presenterar en ny metod för att syntetiskt generera uppmärkt Twitter-data (klassifieringsetiketter) för detektering av automatiserade konton med en regel-baseradeklassificerare. Metoden medför en signifikant minskning av resurser och anstränging samt snabbar upp processen att anpassa klassificerare till förändringar i Twitter-domänen. En utvärdering av klassificerare utfördes på en manuellt uppmärkt testmängd bestående av 1,000 Twitter-konton. Resultaten visar att den regelbaserade klassificeraren på egen hand uppnår en precision på 94.6% och en recall på 52.9%. Vidare påvisar resultaten att klassificerare baserat på övervakad maskininlärning kunde lära sig från syntetiskt uppmärkt data. I bästa fall uppnår dessa maskininlärningsbaserade klassificerare en något lägre precision på 94.1%, jämfört med den regelbaserade klassificeraren, men med en betydligt bättre recall på 93.9%.

General purpose Twitter sentiment analysis is a novel field that is closely related to traditional Twitter sentiment analysis but slightly differs in some key aspects. The main difference lies in the fact that the novel approach considers the unfiltered public Twitter stream while most of the previous approaches often applied various filtering steps which are not feasible for many applications. Another goal is to yield more reliable results by only classifying a tweet as positive or negative if it distinctly consists of the respective sentiment and mark the remaining messages as uncertain. Traditional approaches are often not that strict. Within the course of this thesis it could be verified that the novel approach differs significantly from the traditional approach. Moreover, the experimental results indicated that the archetypical approaches could be transferred to the new domain but the related domain data is consistently sub par when compared to high quality in-domain data. Finally, the viability of the best classification algorithm could be qualitatively verified in a real-world setting that was also developed within the course of this thesis.

La dynamique humaine est un sujet essentiel de l'informatique centrée sur l’homme. Elle se concentre sur la compréhension des régularités sous-jacentes, des relations, et des changements dans les comportements humains. En analysant la dynamique humaine, nous pouvons comprendre non seulement des comportements individuels, tels que la présence d’une personne à un endroit précis, mais aussi des comportements collectifs, comme les mouvements sociaux. L’exploration de la dynamique humaine permet ainsi diverses applications, entre autres celles des services géo-dépendants personnalisés dans des scénarios de ville intelligente. Avec l'omniprésence des smartphones équipés de GPS, les réseaux sociaux de géolocalisation ont acquis une popularité croissante au cours des dernières années, ce qui rend les données de comportements des utilisateurs disponibles à grande échelle. Sur les dits réseaux sociaux de géolocalisation, les utilisateurs peuvent partager leurs activités en temps réel avec par l'enregistrement de leur présence à des points d'intérêt (POIs), tels qu’un restaurant. Ces données d'activité contiennent des informations massives sur la dynamique humaine. Dans cette thèse, nous explorons la dynamique humaine basée sur les données massives des réseaux sociaux de géolocalisation. Concrètement, du point de vue individuel, nous étudions la préférence de l'utilisateur quant aux POIs avec des granularités différentes et ses applications, ainsi que la régularité spatio-temporelle des activités des utilisateurs. Du point de vue collectif, nous explorons la forme d'activité collective avec les granularités de pays et ville, ainsi qu’en corrélation avec les cultures globales / Human dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individual’s behavior, such as a presence at a specific place, but also collective behaviors, such as social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services. However, before the availability of ubiquitous smart devices (e.g., smartphones), it is practically hard to collect large-scale human behavior data. With the ubiquity of GPS-equipped smart phones, location based social media has gained increasing popularity in recent years, making large-scale user activity data become attainable. Via location based social media, users can share their activities as real-time presences at Points of Interests (POIs), such as a restaurant or a bar, within their social circles. Such data brings an unprecedented opportunity to study human dynamics. In this dissertation, based on large-scale location centric social media data, we study human dynamics from both individual and collective perspectives. From individual perspective, we study user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities. From collective perspective, we explore the global scale collective activity patterns with both country and city granularities, and also identify their correlations with diverse human cultures

The rapid development and disitribution of COVID-19 vaccines have sparked diverse public reactions globally, often reflected through social media platförms like Twitter. This study aims to analyze the sentiment andd public discourse surrounding COVID-19 vaccines on Twitter, utilizing advanced text classification techniques to navigare the vast, unstructured nature of sicial media dfata. By implementing sentiment analysis, the research categoizes tweets into positive, negative, and neutral sentiments to gauge public opinion more effectively. In-depth analysis thorugh topic modelingtecniques helped identify seven key topicvs influencing public sentiment including aspects related to efficiacy, logisticl challenges, safety concens, and personal experiences, each varying in prominence depending on the country, as well as the specific timeline of vaccine deployment. Additionally, this study explorers geographical variations in sentiment, notig significant differences in public opinion across different countries. These variations could be tied to local cultural, social, and political contexts. Reults from this study show a polarized response towards vaccination, with significant discourse clusers showing either strong supprt for or resistance against the COVID-19 vaccination efforts. This polarization is further pronounced by the logistical challenges and trust issues related to vaccine science, particularly emphasized in tweets from couintries with lower vaccine acceptance rates. This sentiment analysis on Twitter offers valuable insights into the public's perception and acceptancce of COVID-19 vaccines, providing a useful tool for policymakers and public health officials to understand and address publiv concerns effectively. By identifying and understanding the key factors influencing vaccine sentiment, tageted communication strategies can be developed to enhance publiv engagement and vaccine uptake.

This diploma thesis "Development of model of goals, instruments and performance indicators for digital advertising campaigns for universities" deals with the issue of marketing commu-nication and is specifically focused on advertisement of higher education in the Czech repub-lic. Theoretical part examines goals of marketing communication, instruments of digital adverti-sing campaings and their performance indicators. In practical part, this knowledge is further modified to suit the specific segment of higher education and there is created a model of goals, instruments and performance indicators. This model is then used as a base for analysis of the digital campaign Buď dobrej!, which had been made for the University of economics in Prague, specifically for the Faculty of informa-tics and statistics. Using the results from the previous analysis, we made practical recommendations, which further expand the model created in the practical part.

[pt] Esta pesquisa de tese teve como objetivo explorar a análise de mídias sociais, especialmente as disponíveis em comunidades online de sites de redes sociais, a fim de realizar estudos sociais sobre questões de saúde. Com base em uma abordagem prática foi definido um processo para realizar esses estudos. Este processo contou com ferramentas computacionais adaptados para fornecer apoio em tarefas específicas, tais como recuperação de conteúdo, seleção e análise. Duas ferramentas que se destacam são apresentadas por causa de sua utilidade e a complexidade do processo em que a sua construção se baseou. Para o benefício da análise de comunidades online, o Mapa de Associação de Comunidades é um processo desenvolvido para apoiar especialistas em compreender os interesses dos usuários com base em suas associações dentro de suas comunidades. A outra ferramenta visa auxiliar analistas a selecionar discussões de fóruns online a serem analisados manualmente com técnicas de pesquisa qualitativa, por exemplo, análise de conteúdo e do discurso. Esta ferramenta, TorchSR, foi criada baseada em aprendizado de máquina não supervisionado, usando agrupamento hierárquico, para dar suporte na resolução do problema de seleção de conteúdo. Um estudo de caso exploratório mostra que esta ferramenta ajuda na resolução do problema. O processo proposto foi utilizado em dois estudos sobre questões relevantes de saúde (hepatite C e o abuso de drogas), que resultou em descobertas relevantes sobre saúde pública. Em conclusão, este trabalho apresenta a aplicação prática de ciência social computacional no campo da saúde, através do desenvolvimento de um processo e ferramentas utilizadas para apoiar os analistas e melhorar a sua aplicação. / [en] This research thesis is aiming to exploit valuable social media, especially those available in online communities of social network sites, in order to perform social studies about healthcare issues. Based on a practical approach, a process was defined to conduct such studies. This process relied on tailored computational tools to provide support for specific tasks such as contente retrieval, selection, and analysis. Two tools that stand out are presented because of their utility and the complexity of the process in which their development was based on. The first tool, for the benefit of online community analysis, is the Community Association Map, a process developed to support experts in understanding users’ interests based on their associations within their communities. Our second tool (TorchSR) aims to aid analysts in the selection of discussions from online forums to be manually analyzed by (qualitative) research techniques (e.g. content and discourse analysis). This task, which was defined as solving the content selection problem, was tackled with a tool based on unsupervised machine learning techniques, such as hierarchical clustering. An exploratory study case shows that TorchSR helps analysts in dealing with the problem. The proposed process was employed in two studies about relevant healthcare issues (i.e. hepatitis C and drug abuse) which resulted in interesting findings in the field of public health. In conclusion, this thesis presents a practical application of computational social science to the field of health, through development of a process and tools used to support analysts and improve its application.