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Syftet med den här undersökningen är att ta reda på om det finns några språkliga avvikelser från den generella skriftspråksnormen hos fotbollsjournalister på Twitter. Dessutom vill jag undersöka vilka eventuella avvikelser som förekommer samt vilka typer av eventuella språkliga förändringar som fotbollsjournalisterna vanligen gör. Detta har gjorts genom att skapa kategorier som bygger på språkliga mönster baserat på tidigare forskning. Totalt 818 twitterinlägg från åtta olika fotbollsjournalister har sedan kategoriserats. Undersökningen visar att fotbollsjournalisterna till viss del anpassar sitt språk på grund av den begränsade teckenmängden. Dessa anpassningar består huvudsakligen av förkortningar och användande av ikoner och emojis samt subjektslösa meningar. Andra språkliga drag som utrönts är att det är förhållandevis vanligt att fotbollsjournalister använder sig av en avvikande användning av skiljetecken. Vanligast av allt är dock att deras twitterinlägg inte har några större avvikelser från en generell skriftspråksnorm.

This study content analyzed interest group and candidate tweets from the 2020 American presidential election to determine what issues and substantive attributes were most salient on interest group and candidate agendas during the "hot phase" of the campaign. Cross-lagged correlations were conducted during two time periods from Labor Day to Election Day 2020 to measure agenda building effects. These tests were conducted for Democratic nominee and eventual President Joe R. Biden, and Republican nominee and former President Donald J. Trump. These tests were also conducted for two issue-based interest groups: Everytown for Gun Safety and the National Rifle Association. Findings indicate that Biden influenced Trump's campaign agenda, but Trump did not influence Biden's. The interest groups showed reciprocal influence with each other. Given the unprecedented nature of the 2020 election, the candidates were largely talking about the same issues. However, substantive attributes reveal the candidates' true issue agenda. This study offers methodological innovation by utilizing NVivo for content analysis. / Master of Arts / This study examined tweets from 2020 presidential candidates Donald J. Trump and Joseph R. Biden, as well as the National Rifle Association, a gun rights advocacy organization, and Everytown for Gun Safety, a gun control advocacy organization. These tweets were examined from September 7 to November 3, 2020, from Labor Day until Election Day. For the presidential candidates, it was found that although candidates were talking the same general campaign issues, they were using different substantive attributes when speaking of them. The findings also revealed that Biden was successful at influencing Trump's Twitter focus during the examined time period. Tweets from advocacy organizations were tested for presence of gun-related issues. The advocacy organizations spoke about the same issues as the other, but neither group was successful at influencing what the other said. This study highlights the importance of digital political public relations.

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.

Ο εικοστός πρώτος αιώνας θα μπορούσε εύκολα να χαρακτηριστεί ως τεχνολογικός αιώνας μιας και τα νέα δεδομένα μαρτυρούν αύξηση της χρήσης του ηλεκτρονικού υπολογιστή, του Διαδικτύου και κατ’ επέκταση των κοινωνικών δικτύων. Κι αυτό γιατί η επισκεψιμότητα των κοινωνικών δικτύων και η συμμετοχή σε αυτά, τόσο σε παγκόσμιο όσο και σε ελληνικό επίπεδο εκφράζεται ιδιαίτερα υψηλή. Η κοινωνική δικτύωση στο Ιντερνέτ και τα κοινωνικά δίκτυα στο web συνέβαλαν στην μεταβολή της δομής και της ανάπτυξης του Παγκόσμιου Ιστού. Στην παρούσα διπλωματική εργασία θα μελετηθούν τα κοινωνικά δίκτυα, η δομή τους, οι υπηρεσίες που παρέχουν καθώς και οι τεχνολογίες υποστήριξης τους. Επίσης, θα παρουσιαστούν οι τρόποι που επιτυγχάνεται η διαχείριση γνώσης μέσω των κοινωνικών δικτύων και θα αναλυθούν κάποια εργαλεία ποιοτικής και ποσοτικής ανάπτυξης. Η δομή της διπλωματικής εργασίας έχει ως εξής: Στο πρώτο κεφάλαιο ορίζεται η έννοια της εξατομίκευσης, της κοινωνικής δικτύωσης και παρουσιάζονται τα εξής κοινωνικά δίκτυα: My Space, Bebo, Linked In, Facebook, Twitter, YouTube.com, Google+, Friendster, Hi5, Sobees, Zokem, Gowalla, Qik. Στο δεύτερο κεφάλαιο παρουσιάζεται η ανάλυση των κοινωνικών δικτύων καθώς και οι μετρικές της ανάλυσης κοινωνικών δικτύων (μετρήσεις αναφορικά με τους κόμβους, μετρήσεις αναφορικά με τους γράφους). Στο τρίτο κεφάλαιο γίνεται αναφορά στα χαρακτηριστικά των κοινωνικών δικτύων, στους ρόλους κλειδιά εντός των κοινωνικών δικτύων (υπερβολικά κεντρικός ρόλος, ρόλος του αφανή ήρωα, ρόλος μεσάζοντα, ρόλος γεφύρωσης απομακρυσμένων ομάδων, υπερβολικά περιφερειακός ρόλος), στις υπηρεσίες, στις γλωσσικές ιδιότητες, καθώς και στους τομείς που εφαρμόζονται (εκπαιδευτικές, επιχειρηματικές, κυβερνητικές, ιατρικές εφαρμογές, εφαρμογές γνωριμιών). Στο τέταρτο κεφάλαιο παρουσιάζονται οι κίνδυνοι που ελλοχεύουν από την χρήση των κοινωνικών δικτύων. Η χρησιμοποίηση των προσωπικών δεδομένων των χρηστών με διαφόρους τρόπους, η αποπλάνηση ανηλίκων μέσω δημιουργίας ψεύτικων προφίλ, η κλοπή της ταυτότητας και των στοιχείων του λογαριασμού, η μετάδοση ιών, η αποστολή ανεπιθύμητης αλληλογραφίας, καθώς και η προσωποποιημένη επίθεση (phishing) είναι μερικοί από τους κίνδυνους που παρουσιάζονται από την χρήση των κοινωνικών δικτύων.Στο πέμπτο κεφάλαιο παρουσιάζεται εκτενέστερα το κοινωνικό δίκτυο Facebook, τα χαρακτηριστικά του και οι τεχνολογίες υλοποίησής τους. Γίνεται αναφορά στον τρόπο υλοποίησης της υπηρεσίας Chat με την χρήση του πρωτοκόλλου XMPP, Jabber ID. Επίσης, γίνεται αναφορά στο πρωτόκολλο επικοινωνίας IPv6, στην απόδοση διευθύνσεων, στις βελτιώσεις σε σχέση με το IPV4 κτλ. Στο έκτο κεφάλαιο εξετάζεται το λογισμικό για την ανάλυση των κοινωνικών δικτύων. Παρουσιάζονται κάποια εργαλεία ποσοτικής και ποιοτικής μέτρησης των κοινωνικών δικτύων, όπως το UCINET, Pajek, NetMiner II, STRUCTURE, MultiNet, και StOCNET. / The twenty-first century could easily be described as a century when great advance in technology was accomplished and facts reveal the increasing use of computer, internet and social networks. This is because the traffic of social networks and participation in them, both globally and also in Greece is expressed in very high level. Social networking on the Internet and social networks on the web helped to change the structure and development of the World Wide Web. This diploma will study social networks, their structure, the services they provide and their supporting technologies. Moreover, the ways that knowledge management is achieved through social networks will be presented and some tools for qualitative and quantitative growth will be analyzed. The structure of the thesis is as follows: The first chapter defines the concept of personalization, social networking and presents the following social networks: My Space, Bebo, Linked In, Facebook, Twitter, YouTube.com, Google+, Friendster, Hi5, Sobees, Zokem, Gowalla, Qik. The second chapter presents the analysis of social networks and the metrics of social network analysis (measurements with respect to the nodes, measurements regarding graphs). The third chapter refers to the features of social networks, to key roles within social networks (too central role, the role of silent hero role, intermediary role, role bridging remote teams, too peripheral role), services, language properties and applied fields (ex. education, business, government, medical applications, acquaintances). The fourth chapter describes the hazards posed through social networks. The use of personal data in different ways, the seduction of children by creating false profiles, identity and account information theft, transmission of viruses, spamming, 6 and personalized phishing attack are some of the dangers presented by the use of social networks. The fifth chapter presents in more detail the social network Facebook, the features and technologies implemented. This refers to how the Chat service is implemented using the protocol XMPP, Jabber ID. Reference is also made to the communication protocol IPv6, the addressing, the improvements over the IPV4 etc. The sixth chapter discusses the software for the analysis of social networks. It presents some quantitative and qualitative measurement of social networks such as UCINET, Pajek, NetMiner II, STRUCTURE, MultiNet, and StOCNET.

This thesis investigates how the U.S President’s Twitter mentions of individual companies’ investment announcements influence the short-term price of their stock. By assuming that the President’s comments on a company’s plans should be incorporated by markets as new information, testing the Efficient Market Hypothesis assumption that the markets incorporate all new information, the thesis seeks to contribute to a new, unexplored and growing, research field. This thesis utilizes a qualitative analysis method, studying Twitter mentions on the topic of Trump’s Tax Reform. The data in this thesis is derived from the President’s personal Twitter-account, company announcements, stock price charts, and the Standard & Poor’s S&P500 Index. To conclude, this study finds that although the President’s Twitter comments may signal his public approval of a company and its plans, it appears that any market reaction is only short-term, and that as the market incorporates additional information it returns to an informed state in terms of stock valuations. This study suggests that there are few observable indicators that Trump’s positive mentions on Twitter causes any significant market reaction.

No description available.

Background: Cyberbullying involves the use of digital technologies to harass, humiliate, or threaten individuals or groups. This form of bullying can occur on various platforms such as social media, messaging apps, gaming platforms, and mobile phones. With the outbreak of covid-19, there was a drastic increase in utilization of social media. And this upsurge was coupled with cyberbullying, making it a pressing issue that needs to be addressed. Sentiment analysis involves identifying and categorizing emotions and opinions expressed in text data using natural language processing and machine learning techniques. SVM is a machine learning algorithm that has been widely used for sentiment analysis due to its accuracy and efficiency. Objectives: The main objective of this study is to use SVM for sentiment analysis of cyberbullying tweets and evaluate its performance. The study aimed to determine the feasibility of using SVM for sentiment analysis and to assess its accuracy in detecting cyberbullying. Methods: The quantitative research method is used in this thesis, and data is analyzed using statistical analysis. The data set is from Kaggle and includes data about cyberbullying tweets. The collected data is preprocessed and used to train and test an SVM model. The created model will be evaluated on the test set using evaluation accuracy, precision, recall, and F1 score to determine the performance of the SVM model developed to detect cyberbullying. Results: The results showed that SVM is a suitable technique for sentiment analysis of cyberbullying tweets. The model had an accuracy of 82.3% in detecting cyberbullying, with a precision of 0.82, recall of 0.82, and F1-score of 0.83. Conclusions: The study demonstrates the feasibility of using SVM for sentimental analysis of cyberbullying tweets. The high accuracy of the SVM model suggests that it can be used to build automated systems for detecting cyberbullying. The findings highlight the importance of developing tools to detect and address cyberbullying in the online world. The use of sentimental analysis and SVM has the potential to make a significant contribution to the fight against cyberbullying.

This essay utilizes Speech Act Theory to assess Donald Trump’s role in inciting the riot that took place in Washington D.C. on the 6th of January, 2021 and culminated with the attack on the Capitol building. For the purposes of the study a corpus was created with tweets collected from the Trump Twitter Archive. The tweets cover the span between the latest presidential election, on the 3rd of November, 2020, to the day of the attack. The corpus was read manually and sorted into themes. The themes that emerged show that: a) Trump was convinced of his victory, b) felt that the election was rigged, c) accused news networks, the Democrats and even prominent Republicans for his loss, and d) called the people for action. A quantitative method that identified the most common words in the corpus corroborated the identification of the described themes. The themes were compared to Speech Act Theory’s felicitous conditions for directive speech acts. The study found that Trump’s tweets satisfy the conditions for the successful directive speech acts of Order and Command, thus providing grounds to make the case that he was responsible for inciting the attack.

A análise de sentimentos é um campo de estudo com recente popularização devido ao crescimento da Internet e do conteúdo que é gerado por seus usuários, principalmente nas redes sociais, nas quais as pessoas publicam suas opiniões em uma linguagem coloquial e em muitos casos utilizando de artifícios gráficos para tornar ainda mais sucintos seus diálogos. Esse cenário é observado no Twitter, uma ferramenta de comunicação que pode facilmente ser usada como fonte de informação para várias ferramentas automáticas de inferência de sentimentos. Esforços de pesquisas têm sido direcionados para tratar o problema de análise de sentimentos em redes sociais sob o ponto de vista de um problema de classificação, com pouco consenso sobre qual é o classificador com melhor poder preditivo, bem como qual é a configuração fornecida pela engenharia de atributos que melhor representa os textos. Outro problema é que em um cenário supervisionado, para a etapa de treinamento do modelo de classificação, é imprescindível se dispor de exemplos rotulados, uma tarefa árdua e que demanda esforço humano em grande parte das aplicações. Esta tese tem por objetivo investigar o uso de agregadores de classificadores (classifier ensembles), explorando a diversidade e a potencialidade de várias abordagens supervisionadas quando estas atuam em conjunto, além de um estudo detalhado da fase que antecede a escolha do classificador, a qual é conhecida como engenharia de atributos. Além destes aspectos, um estudo mostrando que o aprendizado não supervisionado pode fornecer restrições complementares úteis para melhorar a capacidade de generalização de classificadores de sentimento é realizado, fornecendo evidências de que ganhos já observados em outras áreas do conhecimento também podem ser obtidos no domínio em questão. A partir dos promissores resultados experimentais obtidos no cenário de aprendizado supervisionado, alavancados pelo uso de técnicas não supervisionadas, um algoritmo existente, denominado de C3E (Consensus between Classification and Clustering Ensembles) foi adaptado e estendido para o cenário semissupervisionado. Este algoritmo refina a classificação de sentimentos a partir de informações adicionais providas pelo agrupamento em um procedimento de autotreinamento (self-training). Tal abordagem apresenta resultados promissores e competitivos com abordagens que representam o estado da arte em outros domínios. / Sentiment analysis is a field of study that shows recent popularization due to the growth of Internet and the content that is generated by its users. More recently, social networks have emerged, where people post their opinions in colloquial and compact language. This is what happens in Twitter, a communication tool that can easily be used as a source of information for various automatic tools of sentiment inference. Research efforts have been directed to deal with the problem of sentiment analysis in social networks from the point of view of a classification problem, where there is no consensus about what is the best classifier, and what is the best configuration provided by the feature engineering process. Another problem is that in a supervised setting, for the training stage of the classification model, we need labeled examples, which are hard to get in the most of applications. The objective of this thesis is to investigate the use of classifier ensembles, exploring the diversity and the potential of various supervised approaches when these work together, as well as to provide a study about the phase that precedes the choice of the classifier, which is known as feature engineering. In addition to these aspects, a study showing that unsupervised learning techniques can provide useful and additional constraints to improve the ability of generalization of the classifiers is also carried out. Based on the promising results got in supervised learning settings, an existing algorithm called C3E (Consensus between Classification and Clustering Ensembles) was adapted and extended for the semi-supervised setting. This algorithm refines the sentiment classification from additional information provided by clusters of data, in a self-training procedure. This approach shows promising results when compared with state of the art algorithms.