Cette thèse rassemble et présente la part plus importante de mes recherches durant ces dernières années, portant sur les systèmes de stockage décentralisés. Dans cette thèse, nous affirmons que les méta-données générées par les utilisateurs dans les systèmes pleinement distribués de gestion de contenu généré par les utilisateurs représentent une source d’informations fiable nous permettant de déterminer lors de la mise en ligne d’un contenu, où il sera consommé dans le futur. Cela rend donc possible le placement prédictif de contenus proche de ses futurs consommateurs, augmentant par là même la pertinence des caches de proximité. / This thesis gathers and presents the most important part of the research we carried out over the last few years on decentralised storage systems. In this thesis, we argue that user-generated meta-data in fully distributed user-generated-content systems constitute a reliable source to determine where uploaded content will be consumed in the future. This makes it possible to predictively place this content close to future consumers, thereby maximizing the suitability of proximity caches.

Vad är det som gör att en miljon människor lägger tid på att streama datorspel? Live streaming har funnits sedan 1920-talet, då i form av musik i hissar. På 1990-talet började hemdatorer utvecklas hårdvarumässigt tillräckligt för att kunna streama film och ljud. Med en kvalitativ enkät publicerad på nätet undersöker vi i den här uppsatsen varför en miljon personer idag väljer att streama när de spelar dator eller tv spel. Resultat vi kommer fram till är ett antal kategorier med olika motiveringar till varför en person väljer att dela med sig av sina spelupplevelser till andra.

UGC has become an important part of traveling both for the companies and for the travellers themselves. The topic is currently at its forefront of research, which makes the area of study even more relevant. Followed by the development of social media in the corporate world and especially its major part in the everyday life of Generation Y. The competition has become tougher among the destinations since social media, a positive online image has, therefore, become essential on these platforms due to the new ways of obtaining information in a previsiting setting. Instagram is an attractive platform for generation Y and the main channel for tourist to publish their visual word-of-mouth experience after a travel. The concept has its own name called, UGC which together with the consumers perceptions of it has been studied in this thesis. The purpose of the study was to see a comparison between the differences of perceived -usefulness, - credibility and -authenticity based on DMOC and UGC. Moreover, how the participants respond concerning the intention to visit Vienna based on an illustrated destination image of UGC or DMOC. The conceptual S-O-R-model was implemented to illustrate how the input of destination image, became a process of perceptions and outcome of intentions belongs together like a common thread. A quantitative study approach was applied with the strategy of Experimental vignette methodology (EVM), to formulate and illustrated the experiment also, to obtain empirical data which later was analyzed and defined the result. Additionally, the study found that there is no significant difference between USG and DMOC in perceived -usefulness, -credibility, and visiting intention. However, findings of perceived authenticity of the destination image demonstrated that UGC communicates a more positive destination image compared to the DMOC regarding perceived authenticity. Moreover, the practical contributions of this study suggest in accordance with previous studies DMO to cooperate with the users and in that way combine UGC with DMOC in the destination presentation.

A área de Análise de Sentimentos ou Mineração de Opiniões tem como um dos objetivos principais analisar computacionalmente opiniões, sentimentos e subjetividade presentes em textos. Por conta da crescente quantidade de textos opinativos nas mídias sociais da web, e também pelo interesse de empresas e governos em insumos que auxiliem a tomada de decisões, esse tópico de pesquisa tem sido amplamente estudado. Classificar opiniões postadas na web, usualmente expressas em textos do tipo conteúdo gerado por usuários, ou UGC (user-generated content), é uma tarefa bastante desafiadora, já que envolve o tratamento de subjetividade. Além disso, a linguagem utilizada em textos do tipo UGC diverge, de várias maneiras, da norma culta da língua, o que impõe ainda mais dificuldade ao seu processamento. Este trabalho relata o desenvolvimento de métodos e sistemas que visam (a) a normalização de textos UGC, isto é, o tratamento do texto com correção ortográfica, substituição de internetês, e normalização de caixa e de pontuação, e (b) a classificação de opiniões, particularmente de avaliações de produtos, em nível de texto, para o português brasileiro. O método proposto para a normalização é predominantemente simbólico, uma vez que usa de forma explícita conhecimentos linguísticos. Já para a classificação de opiniões, que nesse trabalho consiste em atribuir ao texto um valor de polaridade, positivo ou negativo, foram utilizadas abordagens baseadas em léxico e em aprendizado de máquina, bem como a combinação de ambas na construção de um método híbrido original. Constatamos que a normalização melhorou o resultado da classificação de opiniões, pelo menos para métodos baseados em léxico. Também verificamos extrinsecamente a qualidade de léxicos de sentimentos para o português. Fizemos, ainda, experimentos avaliando a confiabilidade das notas dadas pelos autores das opiniões, já que as mesmas são utilizadas para a rotulação de exemplos, e verificamos que, de fato, elas impactam significativamente o desempenho dos classificadores de opiniões. Por fim, obtivemos classificadores de opiniões para o português brasileiro com valores de medida F1 que chegam a 0,84 (abordagem baseada em léxico) e a 0,95 (abordagem baseada em AM), e que são similares aos sistemas para outras línguas, que representam o estado da arte no domínio de avaliação de produtos. / Sentiment Analysis or Opinion Mining has as a main goal to process opinions, feelings and subjectivity expressed in texts. The large number of opinions in social media has increased the interest of companies and governments, who have changed their decisionmaking systems. This has caused a great interest in this research area. Opinions are usually expressed by subjective text, and their processing is a hard task. Moreover, reviews posted on the web are of a especial text type, also called user-generated content (UGC), whose processing is a very challenging task, since they differ in many ways from the standard language. This work describes the design of methods and systems aimed at (a) the normalization of UGC texts, through the use of spell checking, substitution of web slangs, case and punctuation correction, and (b) the classification of opinions at document level, especially for reviews of products in Brazilian Portuguese. The method proposed for normalization of UGC is linguistically motivated. For the classification of opinions, which, in this work, consists in assigning a polarity value (positive or negative) to a opinion text, some lexicon-based and machine learning approaches, as well as a combination of both in a new hybrid manner have been implemented and evaluated. We noticed that the text normalization has improved the results of opinion classification for lexicon-based methods. The quality of the sentiment lexicons for Portuguese was extrinsically evaluated. The reliability of the opinions authors was verified, since they are used for labeling samples. We concluded that they significantly impact the performance of the opinion classifiers. Finally, we proposed some opinion classifiers for Brazilian Portuguese whose F1-measures values reach 0.84 (lexicon-based approach) and 0.95 (machine learning approach), which are analogous to the the similar systems for other languages, which represent the state of the art in the domain of reviews of products.

A área de Análise de Sentimentos ou Mineração de Opiniões tem como um dos objetivos principais analisar computacionalmente opiniões, sentimentos e subjetividade presentes em textos. Por conta da crescente quantidade de textos opinativos nas mídias sociais da web, e também pelo interesse de empresas e governos em insumos que auxiliem a tomada de decisões, esse tópico de pesquisa tem sido amplamente estudado. Classificar opiniões postadas na web, usualmente expressas em textos do tipo conteúdo gerado por usuários, ou UGC (user-generated content), é uma tarefa bastante desafiadora, já que envolve o tratamento de subjetividade. Além disso, a linguagem utilizada em textos do tipo UGC diverge, de várias maneiras, da norma culta da língua, o que impõe ainda mais dificuldade ao seu processamento. Este trabalho relata o desenvolvimento de métodos e sistemas que visam (a) a normalização de textos UGC, isto é, o tratamento do texto com correção ortográfica, substituição de internetês, e normalização de caixa e de pontuação, e (b) a classificação de opiniões, particularmente de avaliações de produtos, em nível de texto, para o português brasileiro. O método proposto para a normalização é predominantemente simbólico, uma vez que usa de forma explícita conhecimentos linguísticos. Já para a classificação de opiniões, que nesse trabalho consiste em atribuir ao texto um valor de polaridade, positivo ou negativo, foram utilizadas abordagens baseadas em léxico e em aprendizado de máquina, bem como a combinação de ambas na construção de um método híbrido original. Constatamos que a normalização melhorou o resultado da classificação de opiniões, pelo menos para métodos baseados em léxico. Também verificamos extrinsecamente a qualidade de léxicos de sentimentos para o português. Fizemos, ainda, experimentos avaliando a confiabilidade das notas dadas pelos autores das opiniões, já que as mesmas são utilizadas para a rotulação de exemplos, e verificamos que, de fato, elas impactam significativamente o desempenho dos classificadores de opiniões. Por fim, obtivemos classificadores de opiniões para o português brasileiro com valores de medida F1 que chegam a 0,84 (abordagem baseada em léxico) e a 0,95 (abordagem baseada em AM), e que são similares aos sistemas para outras línguas, que representam o estado da arte no domínio de avaliação de produtos. / Sentiment Analysis or Opinion Mining has as a main goal to process opinions, feelings and subjectivity expressed in texts. The large number of opinions in social media has increased the interest of companies and governments, who have changed their decisionmaking systems. This has caused a great interest in this research area. Opinions are usually expressed by subjective text, and their processing is a hard task. Moreover, reviews posted on the web are of a especial text type, also called user-generated content (UGC), whose processing is a very challenging task, since they differ in many ways from the standard language. This work describes the design of methods and systems aimed at (a) the normalization of UGC texts, through the use of spell checking, substitution of web slangs, case and punctuation correction, and (b) the classification of opinions at document level, especially for reviews of products in Brazilian Portuguese. The method proposed for normalization of UGC is linguistically motivated. For the classification of opinions, which, in this work, consists in assigning a polarity value (positive or negative) to a opinion text, some lexicon-based and machine learning approaches, as well as a combination of both in a new hybrid manner have been implemented and evaluated. We noticed that the text normalization has improved the results of opinion classification for lexicon-based methods. The quality of the sentiment lexicons for Portuguese was extrinsically evaluated. The reliability of the opinions authors was verified, since they are used for labeling samples. We concluded that they significantly impact the performance of the opinion classifiers. Finally, we proposed some opinion classifiers for Brazilian Portuguese whose F1-measures values reach 0.84 (lexicon-based approach) and 0.95 (machine learning approach), which are analogous to the the similar systems for other languages, which represent the state of the art in the domain of reviews of products.

User Generated Content (UGC) blir allt vanligare på webben och flera av de mest besökta hemsidorna på internet är till stor del baserade på innehåll skapat av dess användare. Varje dag redigeras exempelvis över 120,000 artiklar på Wikipedia av dess hängivna användare som i snitt spenderar över 8 timmar i veckan på att skapa nytt eller redigera existerande material. Att motivera användare att bidra med innehåll på detta sätt blir allt viktigare. För att kunna göra detta krävs en förståelse för vad som motiverar användarna att bidra. Uppsatsen tar sin utgångspunkt från detta och vi studerar vilka motivationsfaktorer som ligger till grund för att användare skapar UGC samt om det finns några samband mellan dessa motivationsfaktorer. En litteraturstudie över tidigare forskning inom området genomförs och resulterar i 15 motivationsfaktorer kategoriserade i fyra kategorier. Dessa presenteras i en matris som strukturerar upp motivationsfaktorerna i de olika kategorierna. Matrisen med de 15 motivationsfaktorerna ligger som teoretisk grund för en kvantitativ enkätundersökning ämnad att se i vilken grad användare motiverats av olika motivationsfaktorer för skapandet av UGC. Genom enkätundersökningen etableras en förståelse för hur stor andel av respondenterna som motiverats av de olika motivationsfaktorerna. Resultatet från undersökningen används för att studera sambanden mellan motivationsfaktorerna. Detta leder fram till en korrelationstabell som presenterar alla signifikanta samband mellan faktorer samt en motivationsmatris som kan fungera som ett stöd för att förstå och diskutera kring motivationsfaktorer för UGC och dess kategoriseringar. Studiens resultat kan hjälpa utvecklare att ge stöd för flera motivationsfaktorer i tjänster baserade på UGC. Den motivationsmatris och det resultat som presenteras kan även vara utgångspunkt för framtida forskning. / User Generated Content (UGC) is becoming increasingly common on the web and many of the most frequently visited websites on the Internet is largely based on content created by its users. Every day 120,000 articles gets edited on Wikipedia by its devoted users who spend an average of eight hours a week on creating new or edit existing material. To motivate users to contribute with content is becoming increasingly important. This requires an increased understanding of what motivates users to contribute.  In this paper we study the motivational factors that motivates users to contribute with UGC and if there is any correlation between these motivational factors. A literature review of previous research in the field of UGC is performed and results in 15 motivational factors categorized into four categories. These are presented in a matrix that structures the motivation factors in the different categories. The matrix of the 15 motivational factors are the theoretical foundation for a quantitative survey designed to see to what extent users are motivated by different motivational factors for the creation of UGC. Through the survey we establish an understanding to what extent different motivational factors motivate the participiants of the study to create UGC. The results from the survey are then used to study the relationships between motivational factors. This leads to a correlation table that presents all the significant relationships between factors and a motivation matrix that can serve as a basis for understanding and discuss the motivators for the UGC and its categorizations. Our results may help developers to support multiple motivational factors in services based on UGC. The motivation matrix and the results presented may also be the basis for future research.

Since its emergence, user generated content (UGC) has become the driving force in the growth of Internet traffic. As one of the most successful and popular UGC systems, YouTube contributes a great share of Internet traffic volume and has attracted a lot of academic interest. The continuously increasing amount of IP traffic motivates the need for better network design, more efficient content distribution mechanisms, and more sustainable system development. Web caching is one of the widely used techniques to reduce the inter Internet Service Provider (ISP) traffic. Web caching is considered an important part in the design of a content distribution infrastructure. This master’s thesis utilizes a one month trace of YouTube traffic in two residential networks in Sweden. Based upon a systematic and in-depth measurement we focus on analyzing the geographic locality of traffic patterns within small areas for these two networks. We summarize the YouTube traffic characteristics and user replay patterns, and then discuss why caching can be useful for YouTube-like systems. We present the optimal caching gain on a per area basis and also divide users into two groups: PC and mobile device users to show the caching gain for these two groups. Overall, an infinite capacity proxy cache for each small area could reduce the YouTube streaming data traffic by 30% to 45%. The result presented in this paper help us to understand YouTube traffic and user behaviors and provides valuable information for the ISPs to enable them to design more efficient caching mechanisms. When this work began we thought that a reduction of backhaul traffic (especially for mobile operators) may delay the need to make investments in upgrading their network capacity. However, an important conclusion from this thesis project is that the cache efficiency depends on the terminal type. For mobile terminals (smart phones, iPads, etc) a terminal cache solution is found to be the most efficient. For PCs in fixed networks, a network cache would be more efficient. It should be noted that the mobile terminals covered in the project are connected through home Wi-Fi, so further research is needed in order to draw definite conclusions for caching solutions for cellular networks. / Sedan dess tillkomst har användargenererat innehåll (på engelska: User Generated Content UGC) blivit den drivande kraften bakom ökningen av internettrafiken. Ett av de mest använda och populära UGC-systemen är Youtube, som bidrar med en stor del av volymen i internettrafiken, och har på så sätt lockat till sig ett stort akademiskt intresse. Den konstant ökande mängden av IP-trafik motiverar behovet av bättre nätverksdesign, effektivare mekanismer för delning av data, och en mer långsiktig system utveckling. Mellanlagring i nätet (network caching) är en av de mer använda teknikerna för att reducera trafiken för Internetoperatörer. Mellanlagring i nätet anses vara en viktig del i designen av den framtida media-distributionens infrastruktur. Det här examensarbetet använder en månads data från Youtube-trafik i två lokala nätverk i Sverige. Baserat på en systematisk och detaljerad mätning, fokuserar vi på att analysera specifika trafikmönster geografisk små områden för dessa två nätverk. Vi analyserar Youtube-trafikens egenskaper och karakteristik och användarnas beteende mönster. Baserat på dessa analyserar vi om mellanlagring kan vara en nyttig lösning för att reducera trafiken för Youtube-liknande system. Vi presenterar den optimala lagringsvinsten (cache gain) för geografiskt definierade populationer och vi delar även upp användare i två grupper: PC och mobila enheter, för att visa lagringsvinsten individuellt för dessa grupper. Generellt sett, om man hade en oändlig lagringskapacitet hos en proxy cache inom ett visst område, så skulle man kunna reducera Youtube-trafiken med 30-45%. Resultaten som presenterats i detta dokument, hjälper oss att förstå Youtube trafik och användar beteende, och ger värdefull information till operatörer, så att de kan designa effektivare lagringsmekanismer. Some utgångspunkt för detta arbete antog vi att en minskning av backhaultrafiken (särskilt för mobiloperatörer) kan fördröja behovet av att göra investeringar för att uppgradera kapaciteten i nätet. En viktig slutsats av detta examensarbete är att effektiviteten hos en proxy cache beror av terminaltypen. För mobila terminaler (smarta telefoner, iPads, etc) ger terminal-cache högre effektivitet, medan en nätverks-cache är effektivare för PCs. Det bör dock nämnas att mätningarna i detta arbete är från terminaler uppkopplade via fast bredband. Det behövs vidare analys för att dra konkreta slutsatser för användarbeteende och cache-lösningar i mobilnät.

Background: The following thesis explores the use of UGC pictures as part of the product presentation in the online shop of a fashion retailer. Of interest are four variables and which influence these are having on the perception of the UGC pictures through the lens of authenticity. The theoretical background and the analysis are done through a developed model based on the SOR model of Mehrabian and Russell (1974). Purpose: The purpose of this thesis is to gain deeper insights about the role of authenticity on the perception of consumers towards UGC pictures which are part of the product presentation of a fashion online shop. Method: This thesis follows a qualitative study by conducting two focus group sessions with in total 15 participants, as well as semi-structured interviews with four participants of the focus group. Findings: Resulting from the findings of this thesis it could be argued, that UGC pictures would be theoretically well suited as part of the product presentation. However, not in terms of authenticity, as consumers have an existing attitude towards content from social media. Value: The findings of this thesis are providing valuable insights into the perception of UGC pictures in the context of an online shopping setting through the lens of authenticity. The presented findings are beneficial and are giving tangible advice for online shop providers to not only understand the perception of consumers to the UGC pictures but also how authenticity is influencing this perception.

In this thesis we will discuss the subject of user participation in the development process of products, specifically video games, through a concept called User-generated content. Product development demands speed and flexibility in the development process and it has been suggested that managers should revise the process of product development to become more flexible and integrate the consumer in increasingly more steps of the process. Video games will often be modified after its release. In fact, it has been estimated that between 95% and 100% of the files in most software will be modified after its initial release. User participation, referring to behaviors and activities performed in a system development process, is a definite feature for websites that consider their content user-generated. Customers who participate in online video game UGC are actively changing games, modifying existing content and creating new content related to all aspects of the game bit by bit, while also contributing this content to others, usually over the internet through some sort of video game content sharing site.User participation is determined by a user’s ability to participate and his motivation to do so, the latter of which is the focus of this thesis. Two major branches of study can be distinguished from motivational theory; intrinsic motivation and extrinsic motivation. The main purpose of this thesis is to examine the effects of motivational factors of intrinsic motivation, extrinsic motivation and toolkits that motivate customers to participate in UGC for video games. We examine what effects intrinsic motivational factors enjoyment, altruism and continuance commitment, as well as extrinsic motivational factors rewards, future rewards, personal need and reputation have on user participation. The toolkits approach to product development is a common user-oriented product development methods in the video game industry, which allows users to modify and create content for games. We will also study what effects the usefulness and ease of use of these toolkits have on user participation. Conducting a quantitative study, we presented a questionnaire to members of four online video game UGC communities; Steam Workshop, GameBanana, ModDB and MODSonline, in order to assess users’ attitudes of aforementioned concepts in relation to their user participation.We have not found any relevant research that examines both motivational factors’ and toolkits’ effects on user participation in video game UGC. With recent turbulent developments in the video game industry regarding monetary compensation for UGC, we decided to put great weight on this area in this thesis, both through our review of previous literature and regarding the results of our study.Our multiple regression analysis showed that toolkit ease of use, intrinsic motivational factors enjoyment and altruism, as well as extrinsic motivational factor reputation have significant positive effects on user participation, while toolkit usefulness showed a significant negative effect on user participation. We also find trends suggesting the positive effect of continuance commitment on user participation, and, finally, a trend suggesting the negative effect of rewards on user participation.

During 2020, online brokers experienced an increase in people who began to invest their money on the stock market. This, because covid-19 made people realize that they needed an economic buffer in difficult times. Moreover, the stock market declined in the beginning of 2020 due to the pandemic which made people see the chance to make profits. Simultaneously, it was evident that the social media usage surged due to covid-19 as people could not meet each other psychically, which resulted in them using social media to interact. In the beginning of 2021, two events occurred in which investors made decisions based on turmoil on social media, which had tremendous effects on investors and the financial market as a whole. These events depicted how financial markets and social media are interrelated, which was interesting for us as we have specialized in finance and marketing respectively. Evidently, research merging these two areas is scarce which motivated us to explore how social media and its mechanisms affect investors when making investment decisions. From the above mentioned events, it could be seen that certain stocks were hyped within online communities, such as Twitter and Reddit. Subsequently, we deemed that it would be interesting to investigate how investors are influenced by online communities. Moreover, it appeared as if investors adhered to the large mass rather than the information that others expressed. Hence, we deemed that we would investigate online communities, electronic word-of-mouth (eWOM) and user-generated content (UGC). The main purpose of this thesis was to gain a deeper understanding of how social media influences, i.e. online communities, eWOM and UGC, affect non-professional investors’ investment decisions. By examining theories of investment decisions, we explored how investors act and also which factors that influence them in their decision-making process. The second area framed both the features and the power of online communities, eWOM and UGC which influence social media users. We performed a qualitative study to examine the mindset of nonprofessional investors and to investigate how they related to and acted upon the quick and accessible information produced online regarding firms and their activities. By our research, we aimed to provide insights on how marketing and finance affect one another, especially in the wake of the rapidly developing digital environment where information sharing on social media is taking new heights. This research was performed by using a qualitative study, in which semi-structured interviews were conducted to get a broader comprehension of the behaviour that investors adopt. The participants of our study consisted of non-professional investors, as we deemed the professional investors to be less adherent to external noise. Using the data from the interviews, we conducted a thematic analysis which resulted in specific themes related to areas of investment decisions, online communities and eWOM/UGC. The findings suggest that investors are affected by online communities, eWOM and UGC although mainly for inspiration, knowledge and information. Hence, our findings signify that investors use them to widen their knowledge and find new companies and sectors to invest in. Concurrently, investors highlight the importance of making their own analysis and research before making a final decision, which signifies that eWOM and UGC do not have a direct impact on investors.