Syftet med denna uppsats är att, genom en textanalys av tweets på mikrobloggen Twitter, undersöka hurinteraktion skapas och upprätthålls mellan idol och fans. Exemplet som används är popstjärnan JustinBieber och hur tweets konstrueras på hans Twitter-sida för att bekräfta frågor om pseudo-interaktion,gemenskap och närvaro med sina följare som ofta kallas ”Beliebers”. Jag vill ta reda på vilkakommunikationskoder som används och hur teorier om interaktion kan kopplas till de tweets jagundersöker närmare. Som främsta metod används en kvalitativ textanalys för att se om det går att hittatydliga indikationer genom språkbruk, tilltal och innehåll som kan kopplas till teorier om hurinteraktionen med publiken presenteras, och huruvida publiken ses som okänd eller iakttagbar.Resultatet av studien har visat att den centrala kommunikationsmodellen som används i stjärnansTwitter-flöde fokuserar på gemenskap och samhörighet i budskapet som överförs snarare än självaöverföringen av information mellan till sändare, Bieber, och mottagare; fans, ”Beliebers” och följare.

This project aims to determine if and how the relatively new journalistic tool of Twitter is impacting journalistic decision-making and news production as a legitimate tool amongst sports writers. Using the methods of qualitative textual analysis and in-depth interviewing, this project analyzes the words and tweets of nine journalists at prominent U.S. newspapers in an attempt to fill a void in research among the topics of journalistic decision-making, sports journalism, and Twitter and to answer questions that arise from the marriage of a certain type of journalism and a particular new media platform.

Η χρήση του Twitter από ολοένα και περισσότερους ανθρώπους έχει ως συνέπεια την παραγωγή μεγάλου όγκου «υποκειμενικών» δεδομένων. Η ανάγκη για εξεύρεση τυχόν πολύτιμης κρυμμένης πληροφορίας σε αυτά τα δεδομένα, έδωσε ώθηση στην ανάπτυξη ενός νέου πεδίου έρευνας, του Sentiment Analysis, που έχει ως αντικείμενο τον εντοπισμό του συναισθήματος ενός χρήστη (ή μιας ομάδας χρηστών) ως προς κάποιο θέμα. Οι παραδοσιακοί αλγόριθμοι και μέθοδοι εντοπισμού συναισθήματος στηρίζονται στην λεκτική ανάλυση φράσεων ή προτάσεων σε «επίσημα» κείμενα και καλούνται word based approaches. Ωστόσο, το μικρό μέγεθος των κειμένων του Twitter, σε συνδυασμό με την χαλαρότητα της χρησιμοποιούμενης γλώσσας (από πλευράς χρηστών), δεν επιτρέπει την αποτελεσματική χρήση αυτών των τεχνικών. Για τον λόγο αυτό, προτιμάται η χρήση τεχνικών που βασίζονται σε χαρακτήρες (αντί για λέξεις) και καλούνται character based approaches. Στόχος της διπλωματικής εργασίας είναι η εφαρμογή της character based μεθόδου στην ανάλυση tweets πολιτικού περιεχομένου. Συγκεκριμένα, χρησιμοποιήθηκαν δεδομένα από την πολιτική σκηνή των Η.Π.Α., με σκοπό να εντοπιστεί η προτίμηση ενός χρήστη ως προς το Ρεπουμπλικανικό ή το Δημοκρατικό κόμμα μέσω σχετικών tweets. Για την ανάλυση χρησιμοποιήθηκε επιβλεπόμενη μάθηση με την βοήθεια του Naive Bayes ταξινομητή. Αρχικά, συλλέχθηκε ένα σύνολο από 7904 tweets, προερχόμενα από τους επίσημους λογαριασμούς Twitter 48 γερουσιαστών. Το σύνολο αυτό χωρίσθηκε σε δυο επιμέρους σύνολα, το σύνολο εκπαίδευσης και το σύνολο ελέγχου, ελέγχοντας για κάθε μια από τις δυο μεθόδους ανάλυσης (την word based και character based μέθοδο) την ακρίβεια της ταξινόμησης. Από τα πειράματα πρόεκυψε πως η character based μέθοδος ταξινομεί τα tweets με μεγαλύτερη ακρίβεια. Στην συνέχεια συλλέξαμε δυο νέα σύνολα έλεγχου, ένα από τον επίσημο λογαριασμό Twitter του Ρεπουμπλικανικού κόμματος και ένα από τον επίσημο λογαριασμό Twitter του Δημοκρατικού κόμματος. Αυτή την φορά, ως σύνολο εκπαίδευσης χρησιμοποιήθηκε ολόκληρο το αρχικό σύνολο από τα tweets των γερουσιαστών και ελέγχθηκε η ακρίβεια ταξινόμησης για την character based μέθοδο στα δυο νέα σύνολα ελέγχου. Αν και στην περίπτωση του Democratic Twitter account τα αποτελέσματα μπορούν να χαρακτηριστούν ως «ικανοποιητικά», μιας και η ακρίβεια της ταξινόμησης πλησίασε το 80%, για την περίπτωση του Republican Twitter account κάτι τέτοιο δεν ισχύει. Για το λόγο αυτό, προχωρήσαμε σε μια πιο διεξοδική μελέτη της δομής και του περιεχομένου αυτών tweets. Από την ανάλυση προέκυψαν ορισμένα ενδιαφέροντα αποτελέσματα για την προέλευση των χαμηλών ποσοστών στην ακρίβεια ταξινόμησης. Συγκεκριμένα, πρόεκυψε πως στην πλειοψηφία των tweets που έγιναν από τους Ρεπουμπλικάνους γερουσιαστές, δεν περιέχονταν κάποια προσωπική τους άποψη. Ήταν απλά μια αναφορά σε κάποιο άρθρο ή video που είδαν στον διαδίκτυο. Άρα, η πλειοψηφία των tweets αυτών περιέχουν «αντικειμενική» αντί για «υποκειμενική» πληροφορία. Συνεπώς, δεν είναι δυνατόν να εξαχθούν τα χαρακτηριστικά εκείνα που θα βοηθήσουν στον εντοπισμό της πολικότητας των χρηστών. / As more people enter the “social web”, social media platforms are becoming an increasingly valuable source of subjective information. The large volume of social media content available requires automatic techniques in order to process and extract any valuable information. This need recently gave rise to the field of Sentiment Analysis, also known as Opinion Mining. The goal of sentiment analysis is to identify the position of a user (or a group of users – a crowd), with respect to a particular issue or topic. Existing sentiment analysis systems aim at extracting patterns mainly from formal documents with respect to a particular language (most techniques concern English). They either search for discriminative series of words or use dictionaries that assess the meaning and sentiment of specific words and phrases. The limited size of Twitter posts in conjunction with the non-standard vocabulary and shortened words (used by its users) inserts a great deal of noise, making word based approaches ineffective. For all of the above reasons, a new approach was recommended in the literature. This new approach is not based on the study of words but rather on the study of consecutive character sequences (namely character-based approaches). In this work, we demonstrate the superiority of the character based approach over the word based one in determining political sentiment. We argue that this approach can be used in order to efficiently determine the political preference (e.g. Republican or Democrat) of voters or to identify the importance that particular issues have on particular voters. This type of feedback can be useful in the organization of political campaigns or policies. We created a corpus consisting of 7904 tweets, collected from the Twitter accounts of 48 U.S. senators. This corpus was then separated into two sets, the training set and the test set, in order to measure for each method (word and character based) the accuracy of the classification. From the experiments it was found that the character based method classified the tweets with greater accuracy. In the next test, we used two new test sets, one from the official Twitter account of the Republican Party and one from the official Twitter account of the Democratic Party. The main difference, with respect to the previous test, was the use of the total set of tweets collected from the senators’ Twitter accounts as a training set and the use of the tweets from the official Twitter accounts of each party as a test set. Even though from the official Democrat Twitter account, 80% of the tweets were correctly classified as Democrat, for the official Republican Twitter account this is not the case (56.7% accuracy). This was found to be partly because the majority of the Republican account tweets were references to online articles or videos and not the personal opinions or views of the users. In other words, such tweets cannot be characterized as personal (subjective), in order to classify the respective user as leaning towards one party or the other, but rather should be considered as objective.

In recent years, mobility empowered by smart phones, tablets and numerous applications running on those mobile devices is transforming the way people live and work in the digital age. Innovations and new business models are emerging that take advantages of this rise of mobile computing. Despite tremendous opportunities promised by the transition to mobility, challenges exist before its full potential can be realized by society as well as by companies. For example, the spread of real-time targeting technologies in mobile display advertising creates a new challenge of how to efficiently allocate countless categories of advertising opportunities, or impressions, in real time. For another example, social broadcasting networks such as Twitter in the U.S. and "Weibo" in China are making it extremely convenient for consumers to spread word-of-mouth (WOM) among them, which both poses new challenges and offers new opportunities to companies wishing to harness the power of consumer WOM. The dissertation contains three essays exploring those issues. In the first essay, the concept of "smart market" for impression allocation is proposed, which emphasizes allocation contingent on uncertain supply and promotes coordination among advertisers across impression categories. A new theory is developed to solve the complicated optimization problem, which leads to a "decomposition and standardization" algorithm. In the second essay, I investigated whether and how Twitter WOM affects movie sales by estimating a dynamic panel data model using publicly available data and well-known machine learning algorithms. I found that chatter on Twitter does matter; however, the magnitude and direction of the effect depends on whom the WOM is from and what the WOM is about. The findings provide new perspectives to understand the effect of WOM on product sales and have important managerial implications. The third essay examines the possibility of designing social-broadcasting-based business intelligence (BI) systems that utilizes real-time information extracted from social broadcasting networks with text mining techniques. A new framework is proposed for this purpose and a Twitter-based BI system is designed and implemented that forecasts movie box office revenues during the opening weekend and daily revenue four weeks after the release of a movie. Preliminary results suggest that social-broadcasting-based BI systems have great potential and are worth exploring by both researchers and practitioners. / text

Ingeniero Civil Eléctrico / El análisis de sentimientos o sentiment analysis es el estudio por el cual se determina la opinión de las personas en Internet sobre algún tema en específico, prediciendo la polaridad de los usuarios (a favor, en contra, neutro, etc), abarcando temas que van desde productos, películas, servicios a intereses socio-culturales como elecciones, guerras, fútbol, etc. En el caso particular de esta memoria, se estudian los principales métodos usados en la literatura para realizar un análisis de sentimientos y se desarrolla un caso empleando parte de estas técnicas con sus respectivos resultados. La plataforma escogida fue Twitter, debido a su alto uso en Chile y el caso de estudio trata acerca de las elecciones presidenciales primarias realizadas en la Alianza por Chile entre los candidatos Andrés Allamand de Renovación Nacional (RN) y Pablo Longueira del partido Unión Demócrata Independiente (UDI). De esta forma, se busca predecir los resultados de las primarias, identificando la gente que está a favor de Allamand y la gente que apoya a Longueira. De igual manera, se busca identificar a los usuarios que están en contra de uno o ambos candidatos. Para predecir la opinión de los usuarios se diseñó un diccionario con palabras positivas y negativas con un puntaje asociado, de manera que al encontrar estos términos en los tweets se determina la polaridad del mensaje pudiendo ser positiva, neutra o negativa. El Algoritmo diseñado tiene un acierto cercano al 60% al ocupar las 3 categorías, mientras que si sólo se ocupa para determinar mensajes positivos y negativos la precisión llega a un 74%. Una vez catalogados los tweets se les asigna el puntaje a sus respectivos usuarios de manera de sumar estos valores a aquellas cuentas que tengan más de un tweet, para luego poder predecir el resultado de las elecciones por usuario. Finalmente, el algoritmo propuesto determina como ganador a Pablo Longueira (UDI) por sobre Andrés Allamand (RN) con un 53% de preferencia mientras que en las elecciones en urnas realizadas en Julio de 2013 en Chile el resultado fue de un 51% sobre 49% a favor de Longueira, lo cual da un error de un 2%, lo que implica que el análisis realizado fue capaz de predecir, con un cierto margen de error, lo que sucedió en las elecciones. Como trabajo futuro se plantea usar el diccionario y algoritmo diseñados para realizar un análisis de sentimientos en otro tema de interés y comprobar su efectividad para diferentes casos y plataformas.

Background: Social media is connecting billions of people from across the globe by fulfilling basic human needs of socializing and getting entertained. While companies are now actively turning to social media to know their customers better, build strong relationships, and spread marketing messages, many are still struggling to figure out what corporate social media content actually works on social media. Purpose: This research aims to understand what type of corporate social media content generates the most consumer response. Methodology: This study employs content analysis of recent social media posts by a selection of top brands in Sweden on two main social media platforms: Facebook and Twitter. A total of ten brands with origin from Sweden were selected, based on 2015 Swedish Brand Award ranking, and their posts were examined to find out influence of corporate social media content on consumer response. A total of 400 posts were examined on verified Twitter profiles and Facebook Pages of these brands. Findings: Type of content which refers to whether the post has image, video, or text-only content, and content orientation which can be task-oriented, self-oriented, or interaction-oriented have a statistically significant relationship with consumer response. In terms of type of content, posts with video and image content can lead to higher consumer response and in terms of content orientation task-oriented content can lead to higher consumer response. Other variables in the study, namely, communication cues, traceability cues, and time-frame have not emerged as significant in this study. Implications: When developing corporate social media content, it’s important to focus on type of content and content orientation. In terms of type of content, managers need to focus on having video and image content as this could lead to higher consumer response and in terms of content orientation, content related to brand / product / promotion (task-oriented) can lead to higher consumer response. Limitations: The study relies only on two main social media platforms and on the last 20 posts of each brand on each of these platforms and doesn’t take into account any seasonality as a full year period has not been studied. The study also relies on a general brand ranking list based in Sweden and not a ranking of brands on social media space. Further Research Suggestions: Future studies could focus on bringing more social media platforms into inquiry, improving sampling robustness by having a larger sample size and broader coverage of time period to account for any seasonality in data, comparing results between different countries, having a broader mix of brands in terms of type of business area or sector or stage of brand development, and blending together the corporate and consumer perspectives. Finally, to account for platform size differences, researchers need to come up with a measure that controls for this variation across platforms. Keywords: Social Media Content, Social Media Platforms, Facebook, Twitter, Communication

The purpose of this study is to examine the relationship between black millennial political activism and social media use. In Phase One of the study, the attitudes of 126 black 18-29 year olds were measured via survey. Results from the survey show that there is a significant relationship between social media use and political activism. In Phase Two, ten high-scoring participants from Phase One were interviewed and analyzed using thematic coding. Examination of the influence of social media on black millennials can inform strategy used for the advancement of black communities and black activism through widespread, effective communication and an advocacy platform accessible by all.

The purpose of this study is to estimate and compare the entropy and redundancy of written English and Swedish. We also investigate and compare the entropy and redundancy of Twitter language. This is done by extracting n consecutive characters called n-grams and calculating their frequencies. No precise values are obtained, due to the amount of text being finite, while the entropy is estimated for text length tending towards infinity. However we do obtain results for n = 1,...,6  and the results show that written Swedish has higher entropy than written English and that the redundancy is lower for Swedish language. When comparing Twitter with the standard languages we find that for Twitter, the entropy is higher and the redundancy is lower.

Syftet med denna studie är delvis att bidra till den rådande forskningsluckan beträffande hur muslimer framställs och representeras, efter ett terrorattentat, av svenskspråkiga användare på Twitter. Studien avser också att konkretisera rollen som användarna besitter på Twitter som gatekeepers och studera mönstren i det genererade innehållet som skapas inom användarens personal public. Detta utgör en grund för att visa hur användare befäster en stigmatiserad bild av muslimer, men också visar solidaritet.  Studien har med utgångspunkt i en kvantitativ innehållsanalys undersökt hur muslimer framställs av svenska twitteranvändare under veckan som följer efter attentatet mot Charlie Hebdo, samt en månad innan attentatet för att se eventuella skillnader i framställningen. Totalt har 588 analysenheter i form av tweets inkluderats i studien, varav 60 stycken under nedslagsveckan en månad innan attentatet och 528 stycken under veckan som följde attentatet. Analysen utgår ifrån teorierna Audience Gatekeeping, Stigmatisering, Personal Publics samt begreppen Solidaritet och ”Den Andre”. Resultatet visade att muslimer i regel framställs som en grupp och sällan skildras som individer. 43 procent av användarna framställde muslimer på ett stigmatiserande sätt men 35 procent uttryckte också solidaritet efter terrorattentatet. Den svenska twitteranvändaren i studien är oftast en privatperson med omkring 500 följare som får begränsat med uppmärksamhet gentemot dem få elitaktörerna på Twitter, så som journalister. Tweetsens innehåll bestod till en tredjedel av länkar, företrädesvis nyhetsmedier, vilket teoretiskt gör en lika stor del av användarna till gatekeepers.

This paper examines and analyzes Hillary Clinton's Twitter account activity between July 1, 2016 and August 28, 2016 in an attempt to determine the perception of authenticity on social media and whether Hillary Clinton was effective in improving her image during the 2016 presidential election. This thesis questions whether Twitter is a reliable tool in determining authenticity.