De nos jours, les médias sociaux ont largement affecté tous les aspects de la vie humaine. Le changement le plus significatif dans le comportement des gens après l'émergence des réseaux sociaux en ligne (OSNs) est leur méthode de communication et sa portée. Avoir plus de connexions sur les OSNs apporte plus d'attention et de visibilité aux gens, où cela s'appelle la popularité sur les médias sociaux. Selon le type de réseau social, la popularité se mesure par le nombre d'adeptes, d'amis, de retweets, de goûts et toutes les autres mesures qui servaient à calculer l'engagement. L'étude du comportement de popularité des utilisateurs et des contenus publiés sur les médias sociaux et la prédiction de leur statut futur sont des axes de recherche importants qui bénéficient à différentes applications telles que les systèmes de recommandation, les réseaux de diffusion de contenu, les campagnes publicitaires, la prévision des résultats des élections, etc. Cette thèse porte sur l'analyse du comportement de popularité des utilisateurs d'OSN et de leurs messages publiés afin, d'une part, d'identifier les tendances de popularité des utilisateurs et des messages et, d'autre part, de prévoir leur popularité future et leur niveau d'engagement pour les messages publiés par les utilisateurs. A cette fin, i) l'évolution de la popularité des utilisateurs de l'ONS est étudiée à l'aide d'un ensemble de données d'utilisateurs professionnels 8K Facebook collectées par un crawler avancé. L'ensemble de données collectées comprend environ 38 millions d'instantanés des valeurs de popularité des utilisateurs et 64 millions de messages publiés sur une période de 4 ans. Le regroupement des séquences temporelles des valeurs de popularité des utilisateurs a permis d'identifier des modèles d'évolution de popularité différents et intéressants. Les grappes identifiées sont caractérisées par l'analyse du secteur d'activité des utilisateurs, appelé catégorie, leur niveau d'activité, ainsi que l'effet des événements externes. Ensuite ii) la thèse porte sur la prédiction de l'engagement des utilisateurs sur les messages publiés par les utilisateurs sur les OSNs. Un nouveau modèle de prédiction est proposé qui tire parti de l'information mutuelle par points (PMI) et prédit la réaction future des utilisateurs aux messages nouvellement publiés. Enfin, iii) le modèle proposé est élargi pour tirer profit de l'apprentissage de la représentation et prévoir l'engagement futur des utilisateurs sur leurs postes respectifs. L'approche de prédiction proposée extrait l'intégration de l'utilisateur de son historique de réaction au lieu d'utiliser les méthodes conventionnelles d'extraction de caractéristiques. La performance du modèle proposé prouve qu'il surpasse les méthodes d'apprentissage conventionnelles disponibles dans la littérature. Les modèles proposés dans cette thèse, non seulement déplacent les modèles de prédiction de réaction vers le haut pour exploiter les fonctions d'apprentissage de la représentation au lieu de celles qui sont faites à la main, mais pourraient également aider les nouvelles agences, les campagnes publicitaires, les fournisseurs de contenu dans les CDN et les systèmes de recommandation à tirer parti de résultats de prédiction plus précis afin d'améliorer leurs services aux utilisateurs / Nowadays, social media has widely affected every aspect of human life. The most significant change in people's behavior after emerging Online Social Networks (OSNs) is their communication method and its range. Having more connections on OSNs brings more attention and visibility to people, where it is called popularity on social media. Depending on the type of social network, popularity is measured by the number of followers, friends, retweets, likes, and all those other metrics that is used to calculate engagement. Studying the popularity behavior of users and published contents on social media and predicting its future status are the important research directions which benefit different applications such as recommender systems, content delivery networks, advertising campaign, election results prediction and so on. This thesis addresses the analysis of popularity behavior of OSN users and their published posts in order to first, identify the popularity trends of users and posts and second, predict their future popularity and engagement level for published posts by users. To this end, i) the popularity evolution of ONS users is studied using a dataset of 8K Facebook professional users collected by an advanced crawler. The collected dataset includes around 38 million snapshots of users' popularity values and 64 million published posts over a period of 4 years. Clustering temporal sequences of users' popularity values led to identifying different and interesting popularity evolution patterns. The identified clusters are characterized by analyzing the users' business sector, called category, their activity level, and also the effect of external events. Then ii) the thesis focuses on the prediction of user engagement on the posts published by users on OSNs. A novel prediction model is proposed which takes advantage of Point-wise Mutual Information (PMI) and predicts users' future reaction to newly published posts. Finally, iii) the proposed model is extended to get benefits of representation learning and predict users' future engagement on each other's posts. The proposed prediction approach extracts user embedding from their reaction history instead of using conventional feature extraction methods. The performance of the proposed model proves that it outperforms conventional learning methods available in the literature. The models proposed in this thesis, not only improves the reaction prediction models to exploit representation learning features instead of hand-crafted features but also could help news agencies, advertising campaigns, content providers in CDNs, and recommender systems to take advantage of more accurate prediction results in order to improve their user services

[ES] Esta tesis doctoral se centra en el estudio, implementación y aplicación de técnicas de aprendizaje automático y estadística multivariante en el emergente campo de la analítica deportiva, concretamente en el fútbol. Se aplican procedimientos comunmente utilizados y métodos nuevos para resolver cuestiones de investigación en diferentes áreas del análisis del fútbol, tanto en el ámbito del rendimiento deportivo como en el económico. Las metodologías empleadas en esta tesis enriquecen las técnicas utilizadas hasta el momento para obtener una visión global del comportamiento de los equipos de fútbol y pretenden ayudar al proceso de toma de decisiones. Además, la metodología se ha implementado utilizando el software estadístico libre R y datos abiertos, lo que permite la replicabilidad de los resultados. Esta tesis doctoral pretende contribuir a la comprensión de los modelos de aprendizaje automático y estadística multivariante para la predicción analítica deportiva, comparando su capacidad predictiva y estudiando las variables que más influyen en los resultados predictivos de estos modelos. Así, siendo el fútbol un juego de azar donde la suerte juega un papel importante, se proponen metodologías que ayuden a estudiar, comprender y modelizar la parte objetiva de este deporte. Esta tesis se estructura en cinco bloques, diferenciando cada uno en función de la base de datos utilizada para alcanzar los objetivos propuestos. El primer bloque describe las áreas de estudio más comunes en la analítica del fútbol y las clasifica en función de los datos utilizados. Esta parte contiene un estudio exhaustivo del estado del arte de la analítica del fútbol. Así, se recopila parte de la literatura existente en función de los objetivos alcanzados, conjuntamente con una revisión de los métodos estadísticos aplicados. Estos modelos son los pilares sobre los que se sustentan los nuevos procedimientos aquí propuestos. El segundo bloque consta de dos capítulos que estudian el comportamiento de los equipos que alcanzan la Liga de Campeones o la Europa League, descienden a segunda división o permanecen en mitad de la tabla. Se proponen varias técnicas de aprendizaje automático y estadística multivariante para predecir la posición de los equipos a final de temporada. Una vez realizada la predicción, se selecciona el modelo con mejor precisión predictiva para estudiar las acciones de juego que más discriminan entre posiciones. Además, se analizan las ventajas de las técnicas propuestas frente a los métodos clásicos utilizados hasta el momento. El tercer bloque consta de un único capítulo en el que se desarrolla un código de web scraping para facilitar la recuperación de una nueva base de datos con información cuantitativa de las acciones de juego realizadas a lo largo del tiempo en los partidos de fútbol. Este bloque se centra en la predicción de los resultados de los partidos (victoria, empate o derrota) y propone la combinación de una técnica de aprendizaje automático, random forest, y la regresión Skellam, un método clásico utilizado habitualmente para predecir la diferencia de goles en el fútbol. Por último, se compara la precisión predictiva de los métodos clásicos utilizados hasta ahora con los métodos multivariantes propuestos. El cuarto bloque también comprende un único capítulo y pertenece al área económica del fútbol. En este capítulo se aplica un novedoso procedimiento para desarrollar indicadores que ayuden a predecir los precios de traspaso. En concreto, se muestra la importancia de la popularidad a la hora de calcular el valor de mercado de los jugadores, por lo que este capítulo propone una nueva metodología para la recogida de información sobre la popularidad de los jugadores. En el quinto bloque se revelan los aspectos más relevantes de esta tesis para la investigación y la analítica en el fútbol, incluyendo futuras líneas de trabajo. / [CA] Aquesta tesi doctoral se centra en l'estudi, implementació i aplicació de tècniques d'aprenentatge automàtic i estadística multivariant en l'emergent camp de l'analítica esportiva, concretament en el futbol. S'apliquen procediments comunament utilitzats i mètodes nous per a resoldre qu¿estions d'investigació en diferents àrees de l'anàlisi del futbol, tant en l'àmbit del rendiment esportiu com en l'econòmic. Les metodologies emprades en aquesta tesi enriqueixen les tècniques utilitzades fins al moment per a obtindre una visió global del comportament dels equips de futbol i pretenen ajudar al procés de presa de decisions. A més, la metodologia s'ha implementat utilitzant el programari estadístic lliure R i dades obertes, la qual cosa permet la replicabilitat dels resultats. Aquesta tesi doctoral pretén contribuir a la comprensió dels models d'aprenentatge automàtic i estadística multivariant per a la predicció analítica esportiva, comparant la seua capacitat predictiva i estudiant les variables que més influeixen en els resultats predictius d'aquests models. Així, sent el futbol un joc d'atzar on la sort juga un paper important, es proposen metodologies que ajuden a estudiar, comprendre i modelitzar la part objectiva d'aquest esport. Aquesta tesi s'estructura en cinc blocs, diferenciant cadascun en funció de la base de dades utilitzada per a aconseguir els objectius proposats. El primer bloc descriu les àrees d'estudi més comuns en l'analítica del futbol i les classifica en funció de les dades utilitzades. Aquesta part conté un estudi exhaustiu de l'estat de l'art de l'analítica del futbol. Així, es recopila part de la literatura existent en funció dels objectius aconseguits, conjuntament amb una revisió dels mètodes estadístics aplicats. Aquests models són els pilars sobre els quals se sustenten els nous procediments ací proposats. El segon bloc consta de dos capítols que estudien el comportament dels equips que aconsegueixen la Lliga de Campions o l'Europa League, descendeixen a segona divisió o romanen a la meitat de la taula. Es proposen diverses tècniques d'aprenentatge automàtic i estadística multivariant per a predir la posició dels equips a final de temporada. Una vegada realitzada la predicció, se selecciona el model amb millor precisió predictiva per a estudiar les accions de joc que més discriminen entre posicions. A més, s'analitzen els avantatges de les tècniques proposades enfront dels mètodes clàssics utilitzats fins al moment. El tercer bloc consta d'un únic capítol en el qual es desenvolupa un codi de web scraping per a facilitar la recuperació d'una nova base de dades amb informació quantitativa de les accions de joc realitzades al llarg del temps en els partits de futbol. Aquest bloc se centra en la predicció dels resultats dels partits (victòria, empat o derrota) i proposa la combinació d'una tècnica d'aprenentatge automàtic, random forest, i la regressió Skellam, un mètode clàssic utilitzat habitualment per a predir la diferència de gols en el futbol. Finalment, es compara la precisió predictiva dels mètodes clàssics utilitzats fins ara amb els mètodes multivariants proposats. El quart bloc també comprén un únic capítol i pertany a l'àrea econòmica del futbol. En aquest capítol s'aplica un nou procediment per a desenvolupar indicadors que ajuden a predir els preus de traspàs. En concret, es mostra la importància de la popularitat a l'hora de calcular el valor de mercat dels jugadors, per la qual cosa aquest capítol proposa una nova metodologia per a la recollida d'informació sobre la popularitat dels jugadors. En el cinqué bloc es revelen els aspectes més rellevants d'aquesta tesi per a la investigació i l'analítica en el futbol, incloent-hi futures línies de treball. / [EN] This doctoral thesis focuses on studying, implementing, and applying machine learning and multivariate statistics techniques in the emerging field of sports analytics, specifically in football. Commonly used procedures and new methods are applied to solve research questions in different areas of football analytics, both in the field of sports performance and in the economic field. The methodologies used in this thesis enrich the techniques used so far to obtain a global vision of the behaviour of football teams and are intended to help the decision-making process. In addition, the methodology was implemented using the free statistical software R and open data, which allows for reproducibility of the results. This doctoral thesis aims to contribute to the understanding of the behaviour of machine learning and multivariate models for analytical sports prediction, comparing their predictive capacity and studying the variables that most influence the predictive results of these models. Thus, since football is a game of chance where luck plays an important role, this document proposes methodologies that help to study, understand, and model the objective part of this sport. This thesis is structured into five blocks, differentiating each according to the database used to achieve the proposed objectives. The first block describes the most common study areas in football analytics and classifies them according to the available data. This part contains an exhaustive study of football analytics state of the art. Thus, part of the existing literature is compiled based on the objectives achieved, with a review of the statistical methods applied. These methods are the pillars on which the new procedures proposed here are based. The second block consists of two chapters that study the behaviour of teams concerning the ranking at the end of the season: top (qualifying for the Champions League or Europa League), middle, or bottom (relegating to a lower division). Several machine learning and multivariate statistical techniques are proposed to predict the teams' position at the season's end. Once the prediction has been made, the model with the best predictive accuracy is selected to study the game actions that most discriminate between positions. In addition, the advantages of our proposed techniques compared to the classical methods used so far are analysed. The third block consists of a single chapter in which a web scraping code is developed to facilitate the retrieval of a new database with quantitative information on the game actions carried out over time in football matches. This block focuses on predicting match outcomes (win, draw, or loss) and proposing the combination of a machine learning technique, random forest, and Skellam regression model, a classical method commonly used to predict goal difference in football. Finally, the predictive accuracy of the classical methods used so far is compared with the proposed multivariate methods. The fourth block also comprises a single chapter and pertains to the economic football area. This chapter applies a novel procedure to develop indicators that help predict transfer fees. Specifically, it is shown the importance of popularity when calculating the players' market value, so this chapter is devoted to propose a new methodology for collecting players' popularity information. The fifth block reveals the most relevant aspects of this thesis for research and football analytics, including future lines of work. / Malagón Selma, MDP. (2023). Machine Learning and Multivariate Statistical Tools for Football Analytics [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/197630

La présente étude vise à examiner dans quelle mesure le sexe ainsi que les caractéristiques comportementales et relationnelles des agresseurs permettent de rendre compte de l’augmentation, sur une période d’un an, du sentiment de solitude chez les victimes de harcèlement par les pairs au secondaire. L’échantillon est composé de 538 élèves de secondaire I et II de la région de Montréal. Au cours de deux années consécutives, le niveau de victimisation des élèves ainsi que l’identité et les caractéristiques des agresseurs (i.e.: sexe, agressivité, popularité et victimisation) ont été évalués à partir de mesures auto-révélées et de procédures de nominations par les pairs. Les résultats démontrent, qu’au-delà de la fréquence à laquelle les élèves sont victimisés, le sexe des agresseurs permet de rendre compte de l’augmentation à travers le temps du sentiment de solitude chez les filles et les garçons. Plus spécifiquement, le nombre d’agresseurs féminins identifiés par les élèves constitue un facteur de risque étroitement lié au développement du sentiment de solitude. Par ailleurs, les caractéristiques des agresseurs ne sont pas associées à l’accroissement du sentiment de solitude à travers le temps. Cependant, le fait de se faire agresser par des élèves qui présentent des difficultés d’ajustement social importantes (i.e. : agressifs et victimisés) est associé de manière concomitante à un moins fort sentiment de solitude. La discussion aborde les processus intra- et interpersonnels permettant d’expliquer pourquoi les sentiments de solitude associés à la victimisation par les pairs sont susceptibles de varier en fonction des caractéristiques des agresseurs. / The goal of the present study is to examine to what extend bullies’ behavioral and relational characteristics account for changes over time in loneliness feelings among victimized middle school students. The sample was composed of 538 grade 7 and 8 students from two middle schools in Montreal. During two consecutive years, students’ level of victimization and bullies’ characteristics (gender, aggressive behaviors, popularity and victimization) were evaluated with self-reported measures and peer nominations. Results show that, beyond the frequency to which students are victimized, bullies’ gender was associated with an increase over a one year period in loneliness feelings for girls and boys. Specifically, the number of females bullies identified by the students, constitutes a risk factor closely linked to the development of loneliness feelings. Moreover, the behavioral and relational characteristics of the bullies were not associated with an increase over time of loneliness feelings. However, being bullied by students characterized by social adjustment difficulties (aggressive and victimized bullies) was negatively associated with concurrent loneliness feelings. The discussion highlights the intra- and interpersonal processes explaining why the loneliness feelings linked to victimization by peers are likely to vary according to the bullies’ characteristics.

This diploma thesis deals with the topic called "Preferences and Popularity of Individual Sports Branches by Children of Older School and Junior Age in Kolín". In the theoretic part I focused on definition of the main terms which occur in the name of the thesis as preferences, popularity, sports branch, children of older school age and children of junior age. Discovered data were processed on the basis of quantitative method via questionnaire survey and the evaluation was carried out via spreadsheet and statistical methods. The performed survey answered the stated survey questions from which emerged that children of older school age are more interested in performance sport. The most often performance sports done are football and dancing by both groups of the respondents. Further, girls would not change performance sport for their favourite one. Junior age children would like to do their favourite sport at performance level. Popularity of physical education does not vary by children doing sport for leisure or performance. KEYWORDS sport, sports branch, children, older school age, junior age, health, physical education, lifestyle, preferences, popularity

Diploma thesis is entitled, "Collective Evaluation of Quality of Movie and Serial Production of Czech-Slovak Movie Database on http://www.csfd.cz". The main focus of this thesis is on the aggregated evaluation study of quality of movies in several internet movie databases, primarily in CSFD and IMDb. The study also defines these internet databases in the context of social discourse, briefly informs about the history of these databases, and introduces the reader into their functioning. Additionally concerning the quality of movies, databases represent the main authority, and sorting of the movies according to their 'colour' in CSFD is considered a form of agenda-setting. This study discusses the issues of quality, through analysis in an attempt to determine the quality of movies on the database. Much of the thesis discussion revolves around the author's research, which was carried out from September 2011 till the end of March 2012. Unfortunately, because of the extent of diploma thesis and the methodology used in the research, it was not possible to analyze serial production in more detail.

近年來社群平台(Social Media)的興起，提供了人與人之間簡便且快速互相交換各式各樣內容的機會。社群多媒體(Social Multimedia)指的就是使用者在社群平台上所互相交換的多媒體內容，相較於單純的多媒體內容而言，社群多媒體多了寶貴的大量社群平台使用者之間分享互動的記錄，以及社群平台使用者在社群網絡(Social Network)中的各項資訊。如此一來為多媒體內容提供了更多面向的資料，讓社群多媒體比起單純的多媒體內容有更多的應用的可能。 微網誌(Microblog)是個可以讓使用者自由的即時分享文字訊息的平台，有著許多使用者的當下的心情、眼前所看到聽到的事或與朋友對話等。而微網誌平台相較於其它單純用來分享多媒體內容的社群平台(例如YouTube或Flickr)而言，在微網誌平台上的多媒體內容有明顯的分享傳遞現象。而本研究的目標，就是要利用些多媒體內容在微網誌平台上的分享傳遞的特性與資料，針對群多媒體內容進行熱門預測。 隨著時間的前進，若以單一同樣的規則來進行熱門預測，將可能造成預測準確率的下降；再者，即使是在同樣的時間點，不同的多媒體內容會有各自隨著時間在熱門上的變化趨勢，還是會有需要不同的規則來進行熱門預測的可能性，也就是所謂的局部概念飄移現象。在此我們將熱門預測問題轉為資料探勘(Data Mining)中的分類(Classification)問題，並同時將局部概念飄移現象納入考慮，提出一個針對微網誌平台上多媒體內容的熱門預測方法。實驗結果顯示，有考慮局部概念飄移的熱門預測方法，在準確率的表現上明顯的優於GCD方法(平均有4%的提升)與Baseline方法(平均有10%的提升)，代表我們的熱門預測方法更適合微網誌平台上的多媒體內容，也代表的確有概念飄移與局部概念飄移的現象存在。 / In recent years, the rise of social media offers an easy and fast way for information exchange. Social multimedia refers to the multimedia content that users share on the social media. Different from traditional multimedia, social multimedia contains both the multimedia and user behavior information on social media. Microblog is one type of social media. Compared to other social media such as YouTube and Flickr, microblogs provide a more friendly environment for users to propagate social multimedia. The goal of this thesis is to make use of the characteristics and information of propagation on microblogs for popularity prediction of social multimedia. The popularity prediction method based on concept drift mining is proposed. In particular, the local concept drift mechanism is employed to capture the local characteristics of social multimedia. By taking the local concept drift into consideration, the task of popularity prediction is transformed into the ensemble classification problem. Experiments on social multimedia collected from plurk show that the proposed approach performs well.

La présente étude vise à examiner dans quelle mesure le sexe ainsi que les caractéristiques comportementales et relationnelles des agresseurs permettent de rendre compte de l’augmentation, sur une période d’un an, du sentiment de solitude chez les victimes de harcèlement par les pairs au secondaire. L’échantillon est composé de 538 élèves de secondaire I et II de la région de Montréal. Au cours de deux années consécutives, le niveau de victimisation des élèves ainsi que l’identité et les caractéristiques des agresseurs (i.e.: sexe, agressivité, popularité et victimisation) ont été évalués à partir de mesures auto-révélées et de procédures de nominations par les pairs. Les résultats démontrent, qu’au-delà de la fréquence à laquelle les élèves sont victimisés, le sexe des agresseurs permet de rendre compte de l’augmentation à travers le temps du sentiment de solitude chez les filles et les garçons. Plus spécifiquement, le nombre d’agresseurs féminins identifiés par les élèves constitue un facteur de risque étroitement lié au développement du sentiment de solitude. Par ailleurs, les caractéristiques des agresseurs ne sont pas associées à l’accroissement du sentiment de solitude à travers le temps. Cependant, le fait de se faire agresser par des élèves qui présentent des difficultés d’ajustement social importantes (i.e. : agressifs et victimisés) est associé de manière concomitante à un moins fort sentiment de solitude. La discussion aborde les processus intra- et interpersonnels permettant d’expliquer pourquoi les sentiments de solitude associés à la victimisation par les pairs sont susceptibles de varier en fonction des caractéristiques des agresseurs. / The goal of the present study is to examine to what extend bullies’ behavioral and relational characteristics account for changes over time in loneliness feelings among victimized middle school students. The sample was composed of 538 grade 7 and 8 students from two middle schools in Montreal. During two consecutive years, students’ level of victimization and bullies’ characteristics (gender, aggressive behaviors, popularity and victimization) were evaluated with self-reported measures and peer nominations. Results show that, beyond the frequency to which students are victimized, bullies’ gender was associated with an increase over a one year period in loneliness feelings for girls and boys. Specifically, the number of females bullies identified by the students, constitutes a risk factor closely linked to the development of loneliness feelings. Moreover, the behavioral and relational characteristics of the bullies were not associated with an increase over time of loneliness feelings. However, being bullied by students characterized by social adjustment difficulties (aggressive and victimized bullies) was negatively associated with concurrent loneliness feelings. The discussion highlights the intra- and interpersonal processes explaining why the loneliness feelings linked to victimization by peers are likely to vary according to the bullies’ characteristics.

Submitted by Fernando Menucci (menucci@gmail.com) on 2015-09-25T20:16:09Z No. of bitstreams: 1 merged.pdf: 5551154 bytes, checksum: ffba1681eef4e87cee11f102f098b7d6 (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2015-10-08T14:05:39Z (GMT) No. of bitstreams: 1 merged.pdf: 5551154 bytes, checksum: ffba1681eef4e87cee11f102f098b7d6 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-10-13T12:32:12Z (GMT) No. of bitstreams: 1 merged.pdf: 5551154 bytes, checksum: ffba1681eef4e87cee11f102f098b7d6 (MD5) / Made available in DSpace on 2015-10-13T12:32:24Z (GMT). No. of bitstreams: 1 merged.pdf: 5551154 bytes, checksum: ffba1681eef4e87cee11f102f098b7d6 (MD5) Previous issue date: 2015-08-25 / No jornalismo, são chamadas suítes as matérias que trazem a sequência de um fato já noticiado. Conforme a imprensa cresce na Internet, podemos ver frequentemente um mesmo fato sendo repetido em portais de notícias dia após dia. Este trabalho visa medir as quantidades de artigos a respeito de um mesmo assunto que tenha iniciado uma suíte, com esta medição acontecendo ao longo dos dias em que ele foi explorado. Os resultados permitiram que fossem encontrados padrões que identifiquem os dias em que os fatos mais relevantes foram noticiados, bem como o tempo em que o assunto foi desenvolvido. Para esta análise, foram escolhidos alguns dos mais importantes fatos que viraram suítes no Brasil ao longo dos últimos anos. As quantidades de artigos são provenientes do maior portal de notícias do país, o G1, e da base de dados do Media Cloud Brasil. / Follow-ups are that kind of article that brings a sequence of a fact. As long as Journalism becomes digital, we can often see a same fact being repeated day after day on news websites. This work will measure the amounts of articles about a fact that starts a follow-up along the days that it was explored, finding patterns and the extension of the follow-up, also identifying days when the most significant facts were registered. Some of the main events that were turned to follow-ups in Brazil in the last years were choosen to be analysed. The amounts of articles comes from the biggest news website of the country, G1, and from the database of Media Cloud Brasil.

Charles University in Prague Faculty of Science Chemistry Education Attitude of Students to Study Chemistry at the Different Types of Secondary Schools Author: Mgr. Miloš Halúzka Supervisor: Mgr. Jiří Šibor, Ph.D. Prague 2015 2 Abstract In the theoretical part, author quotes technical literature which describes the effects specifying the access of students to study and he estimates expressions of individual effects in real teaching practice in chemistry lessons. Author describes the difference between students studying secondary vocational school and students from apprentice school with regard to absence. Author also illustrates the crucial influence of family background on the example of educational achievements of one student in 8 past years. Furthermore, the teoretical part summarizes the various influences that participates in the access of students to the school subject of chemistry, recommended approaches and didactic methods. There is also defined the issue of cousework as a project teaching method. Everything is evaluated in the context of conditions prevailing on the different types of secondary schools in the Czech Republic. Is describing the position of the general courses at the apprentice school on a small sample of teachers. The teoretical part is devoted to different approach of different...

News value assessment has been done forever in the news media industry and is today often done in real-time without any documentation. Editors take a lot of different qualitative aspects into consideration when deciding what news stories will make it to the first page. This thesis explores how the complex news value assessment process can be translated into a quantitative model, and also how those news values can be predicted in an effective way using machine learning and NLP. Two models for news value were constructed, for which the correlation between modeled and manual news values was measured, and the results show that the more complex model gives a higher correlation. For prediction, different types of features are extracted, Random Forest and SVM are used, and the predictions are evaluated with accuracy, F1-score, RMSE, and MAE. Random Forest shows the best results for all metrics on all datasets, the best result being on the largest dataset, probably due to the smaller datasets having a less even distribution between classes.