Nous proposons dans cette thèse un système permettant de déterminer, à partir des données envoyées sur les microblogs, les évènements qui stimulent l’intérêt des utilisateurs durant une période donnée et les dates saillantes de chaque évènement. Étant donné son taux d’utilisation élevé et l’accessibilité de ses données, nous avons utilisé la plateforme Twitter comme source de nos données. Nous traitons dans ce travail les tweets portant sur la Tunisie dont la plupart sont écrits par des tunisiens. La première tâche de notre système consistait à extraire automatiquement les tweets d’une façon continue durant 67 jours (de 8 février au 15 avril 2012). Nous avons supposé qu’un évènement est représenté par plusieurs termes dont la fréquence augmente brusquement à un ou plusieurs moments durant la période analysée. Le manque des ressources nécessaires pour déterminer les termes (notamment les hashtags) portant sur un même sujet, nous a obligé à proposer des méthodes permettant de regrouper les termes similaires. Pour ce faire, nous avons eu recours à des méthodes phonétiques que nous avons adaptées au mode d’écriture utilisée par les tunisiens, ainsi que des méthodes statistiques. Pour déterminer la validité de nos méthodes, nous avons demandé à des experts, des locuteurs natifs du dialecte tunisien, d’évaluer les résultats retournés par nos méthodes. Ces groupes ont été utilisés pour déterminer le sujet de chaque tweet et/ou étendre les tweets par de nouveaux termes. Enfin, pour sélectionner l'ensemble des évènements (EV), nous nous sommes basés sur trois critères : fréquence, variation et TF-IDF. Les résultats que nous avons obtenus ont montré la robustesse de notre système. / In this thesis, we propose a method to highlight users’ concerns from a set of Twitter messages. In particular, we focus on major events that stimulate the user’s interest within a given period. Given its rate of use and accessibility of data, we used Twitter as a source of our data. In this work, we use tweets related to Tunisia, most of them being written by Tunisians. The first task of our system was to continuously extract tweets during 67 days (from February 8th to April 15th, 2012). We assumed that an event is represented by several terms whose frequency sharply increases one or more times during the analyzed period. Due to the lack of resources that allow determining the terms (including hashtags) referring to the same topic, we propose methods that help grouping similar terms. To do this, we used phonetic methods adapted to the way Tunisians write and statistical methods. To determine the validity of our methods, we asked the experts, who are native speakers of the Tunisian dialect, to evaluate the results returned by our methods. These clusters are used to determine the subject of each tweet and/or expand the tweets by new terms. Finally, to select the set of events (EV), we relied on three criteria: frequency, variation and TF-IDF. The results that we obtained show the robustness of our system.

The aim of this thesis is to get an understanding of how Daniel Wellington is using visual communication to communicate their brand identity on Instagram. It is also about how they use hashtags to engage their consumers into their brand community. The questions this thesis answers are “How does Daniel Wellington use visual communication to communicate their brand identity on Instagram?” and “How does Daniel Wellington use hashtags to engage their consumers?”. To answer these questions consumer to consumer marketing, influencer marketing, semiotics and snapshot theory have been used as a theoretical basis. Methods used for this thesis is a semiotic analysis with denotation and connotation. Through denotation and connotation, the chosen pictures have been carefully analysed to answer the questions of the thesis.             The results from the analysis shows that Daniel Wellington communicate their brand identity very good because the pictures posted on their Instagram represented it well. The sign of engagement in the hashtags turned out to be very engaging for the consumers to keep wanting to post pictures with and of Daniel Wellington’s products.

Social media has become a large part of today’s pop culture and keeping up with what is going on not only in our social circles, but around the world. It has given many a platform to unite their causes, build fandoms, and share their commentary with the world. A tool in helping group posts together or give commentary on a thought is the hashtag. In this paper I explore the evaluative roles of hashtags in social media discourse, specifically on Twitter. I use a sample of randomly selected tweets from the Twitter API stream I collected and compiled myself. I collected a total of 200,000 tweets and filtered out Re-tweets. Looking at each individual hashtag I sorted them into the categories outlined by the Appraisal Theory proposed by Martin and White (Martin & White, 2005). I explore the types of evaluation expressed in hashtags, the relationships between evaluative hashtags and how users negotiate evaluations using meme hashtags.

Microblogging is an increasingly popular form of social media. One of the most popular microblogging services is Twitter. The number of messages posted to Twitter on a daily basis is extremely large. Accordingly, it becomes hard for users to sort through these messages and find ones that interest them. Twitter offers search mechanisms but they are relatively simple and accordingly the results can be lacklustre. Through participation in the 2011 Text Retrieval Conference's Microblog Track, this thesis examines real-time ad hoc search using standard information retrieval approaches without microblog or Twitter specific modifications. It was found that using pseudo-relevance feedback based upon a language model derived from Twitter posts, called tweets, in conjunction with standard ranking methods is able to perform competitively with advanced retrieval systems as well as microblog and Twitter specific retrieval systems. Furthermore, possible modifications both Twitter specific and otherwise are discussed that would potentially increase retrieval performance. Twitter has also spawned an interesting phenomenon called hashtags. Hashtags are used by Twitter users to denote that their message belongs to a particular topic or conversation. Unfortunately, tweets have a 140 characters limit and accordingly all relevant hashtags cannot always be present in tweet. Thus, Twitter users cannot easily find tweets that do not contain hashtags they are interested in but should contain them. This problem is investigated in this thesis in three ways using learning methods. First, learning methods are used to determine if it is possible to discriminate between two topically different sets of a tweets. This thesis then investigates whether or not it is possible for tweets without a particular hashtag, but discusses the same topic as the hashtag, to be separated from random tweets. This case mimics the real world scenario of users having to sift through random tweets to find tweets that are related to a topic they are interested in. This investigation is performed by removing hashtags from tweets and attempting to distinguish those tweets from random tweets. Finally, this thesis investigates whether or not topically similar tweets can also be distinguished based upon a sub-topic. This was investigated in almost an identical manner to the second case. This thesis finds that topically distinct tweets can be distinguished but more importantly that standard learning methods are able to determine that a tweet with a hashtag removed should have that hashtag. In addition, this hashtag reconstruction can be performed well with very few examples of what a tweet with and without the particular hashtag should look like. This provides evidence that it may be possible to separate tweets a user may be interested from random tweets only using hashtags they are interested in. Furthermore, the success of the hashtag reconstruction also provides evidence that users do not misuse or abuse hashtags since hashtag presence was taken to be the ground truth in all experiments. Finally, the applicability of the hashtag reconstruction results to the TREC Microblog Track and a mobile application is presented.

Political contention has entered a new age. Over the past three years unprecedented large-scale movements have challenged states across the globe, and social media has been an important component in their development and articulation. With the advent of social media sites, such as Facebook and Twitter, ordinary people have the technological ability to instantaneously transcend space, time and resources (Aouraugh and Alexander 2011; Castells 2012; Earl and Kimport 2009, 2011; Eltantawy, Nahed and Wiest 2011; Gerbaudo 2012; Hands 2011; Holmes 2012; Mason 2012). Are we currently living in a historical moment where a new repertoire of contention is emerging? If so, how is social media changing the way we collectively contest for our interests? The theoretical framework I propose in this paper advances and elaborates a social geographic approach in the framing of political contention that emphasizes the importance of the spatiality and temporality created by the hashtag (#) in the development and articulation of today's social movements. In addition to secondary sources about the protests in Brazil (#VemPraRua), I draw on participant observations to analyze a new modular form of protest I call the "hashtag movement." I claim that the hashtag (#) creates a new space/time (Massey 1992, 2007; Soja 1996) that fundamentally shifts the process of nation-ness (Anderson 2006) and marks a new phase in the mediazation of modern culture (Thompson 1991); two fundamental shifts that I argue are comparable to the structural and cultural shifts that formed the modern repertoire of contention (Anderson 2006; Della Porta and Diani 1999; McAdam 1999; McAdam, Tarrow and Tilly 2001; Sewell 1990, 1996; Swidler 1986; Tarrow 1993, 1994; Tilly 1986, 1995a, 1995b; Young 2002). / text

Microblogging is an increasingly popular form of social media. One of the most popular microblogging services is Twitter. The number of messages posted to Twitter on a daily basis is extremely large. Accordingly, it becomes hard for users to sort through these messages and find ones that interest them. Twitter offers search mechanisms but they are relatively simple and accordingly the results can be lacklustre. Through participation in the 2011 Text Retrieval Conference's Microblog Track, this thesis examines real-time ad hoc search using standard information retrieval approaches without microblog or Twitter specific modifications. It was found that using pseudo-relevance feedback based upon a language model derived from Twitter posts, called tweets, in conjunction with standard ranking methods is able to perform competitively with advanced retrieval systems as well as microblog and Twitter specific retrieval systems. Furthermore, possible modifications both Twitter specific and otherwise are discussed that would potentially increase retrieval performance. Twitter has also spawned an interesting phenomenon called hashtags. Hashtags are used by Twitter users to denote that their message belongs to a particular topic or conversation. Unfortunately, tweets have a 140 characters limit and accordingly all relevant hashtags cannot always be present in tweet. Thus, Twitter users cannot easily find tweets that do not contain hashtags they are interested in but should contain them. This problem is investigated in this thesis in three ways using learning methods. First, learning methods are used to determine if it is possible to discriminate between two topically different sets of a tweets. This thesis then investigates whether or not it is possible for tweets without a particular hashtag, but discusses the same topic as the hashtag, to be separated from random tweets. This case mimics the real world scenario of users having to sift through random tweets to find tweets that are related to a topic they are interested in. This investigation is performed by removing hashtags from tweets and attempting to distinguish those tweets from random tweets. Finally, this thesis investigates whether or not topically similar tweets can also be distinguished based upon a sub-topic. This was investigated in almost an identical manner to the second case. This thesis finds that topically distinct tweets can be distinguished but more importantly that standard learning methods are able to determine that a tweet with a hashtag removed should have that hashtag. In addition, this hashtag reconstruction can be performed well with very few examples of what a tweet with and without the particular hashtag should look like. This provides evidence that it may be possible to separate tweets a user may be interested from random tweets only using hashtags they are interested in. Furthermore, the success of the hashtag reconstruction also provides evidence that users do not misuse or abuse hashtags since hashtag presence was taken to be the ground truth in all experiments. Finally, the applicability of the hashtag reconstruction results to the TREC Microblog Track and a mobile application is presented.

abstract: In trading, volume is a measure of how much stock has been exchanged in a given period of time. Since every stock is distinctive and has an alternate measure of shares, volume can be contrasted with historical volume inside a stock to spot changes. It is likewise used to affirm value patterns, breakouts, and spot potential reversals. In my thesis, I hypothesize that the concept of trading volume can be extrapolated to social media (Twitter). The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its (Twitter) usage by news channels, financial experts and pandits, the global economy does seem to hinge on 140 characters. By analyzing the number of tweets hash tagged to a stock, a strong relation can be established between the number of people talking about it, to the trading volume of the stock. In my work, I overt this relation and find a state of the breakout when the volume goes beyond a characterized support or resistance level. / Dissertation/Thesis / Masters Thesis Computer Science 2015

Context. Social media marketing is constantly gaining interest as a powerful tool, for advertisement campaigns, in order to maximize their audience to reach potential new customers. To efficiently target customers, the knowledge of social network structure and user behavior is of crucial importance. Among these online social networks, Twitter’s popularity is rapidly increasing. Its key feature is to link different topics and posts by using the hashtag symbol. This particular characteristic is one of the principal causes that direct users to specific topics, and lead them to expand their network. Objectives. In this study we investigate a correlation between hashtags and increase of followers motivated by a specific research question. The question is whether the addition of hashtags to tweets produces new followers. Methods. We designed a controlled experiment in which we gather tweets from two types of users: users tweeting with hashtags and users tweeting without hashtags. Users tweeting with hashtags will belong to the experimental group and users tweeting without hashtags will form part of the control group. Their statistical behavior is analyzed by conducting the non-parametrical Mann-Whitney U-test. Results. The results of the Mann-Whitney U-Test show that the null hypothesis is rejected at confidence level 0.05. Based on that, a correlation is shown between hashtags and followers, therefore tweets that contain hashtags are more likely to lead to a higher increase in the number of followers than tweets without hashtags. Conclusions. This thesis contributes to describe the functionality of hahstags in the online social network Twitter. It provides an original correlational study on the use of hashtags and increase of followers. We discover that users tweeting with hashtags are more likely to increase their number of followers than users that tweet without hashtags. This discovery opens a new research direction regarding hashtags and followers, specifically to discover which hashtags increase the number of followers and which do not.

This thesis aims to interrogate how “issue-attention cycle” theory corresponds to online debates that address the issue of sexism, specifically the hashtag campaign #MeToo, in German online media. The issue-attention dynamics of #MeToo on Twitter are analyzed in order to understand the relationship between mainstream media and hashtag activism in Germany, and it is demonstrated what the #MeToo coverage can tell about issue-attention theory on the one hand, and how the theory can help to understand #MeToo on the other hand. To this end, the results of a content analysis of Twitter posts with #MeToo by four major German newspapers, representative of the German online media landscape, were compared to previous hashtag campaigns in Germany that addressed the same topic. In addition, five media experts as well as academics were interviewed, and their insights used to identify the issue-attention dynamics of #MeToo. Anthony Downs’ (1972) “issue-attention cycle” theory is then applied to the hashtag. The results show that so far there have been many ups and downs of attention in the lifecycle of #MeToo, but public attention has not ended. The research also finds that hashtags emanating from the United States, and especially from individuals related to the American entertainment industry, receive far more attention than corresponding hashtags originating in Germany, even though they address the same topic. Finally, and perhaps most significantly, the deployment of the issue-attention cycle showed that a modified model is necessary to address the fast-changing attention dynamics of hashtags on Twitter. Instead of a cycle, attention can be better demonstrated through waves. Adding the variables “new events” and the hashtag as a connector of events and issues to the model helps to better understand current media structures and their attention dynamics, which are strongly influenced by social media.

I denna studie undersöks det hur den svenska mediebranschens arbetsmiljö har påverkats avmetoo-rörelsen hittills. För att ta reda på detta analyseras även kopplingen mellan hashtags och metoo för att förstå vilket inflytande själva hashtagen har haft för rörelsen och dess spridning. Studiens syfte är därför att undersöka hur den svenska mediebranschens arbetsmiljö har påverkats av metoo samt att ta reda på hur hashtags fungerar för att förstå vilken innebörd den haft för metoo. För att besvara studiens frågeställning har ett flertal kvalitativa intervjuer med flera väsentliga personer utifrån frågeställningen utförts. Majoriteten av informanterna arbetar inom mediebranschen och har därför både insikt och egna uppfattningar gällande vilken påverkan metoo haft på deras arbetsplats hittills. För att samla ytterligare relevant information har en expert på metoo intervjuats, samt en digital expert för att undersöka kopplingen mellan hashtagen och metoo. Både teorin och den insamlade datan från informanterna indikerar att metoo påverkat den svenska mediebranschen arbetsmiljön. Det går också att se att själva hashtagen har varit betydande för metoo:s spridning. Jämställdhet och sexuella trakasserier på arbetsplatsen har börjat diskuteras mer öppet vilket bland annat lett till att många företag har börjat arbeta mer aktivt med att få bort sexuella trakasserier och dylikt på arbetsplatsen efter att metoo slog igenom. / This study will examine how the Swedish media industry’s work environment has beeninfluenced by the metoo movement so far. To figure this out the connection between hashtagsand metoo will be analyzed to understand which influence the hashtag itself has had on themovement and its spread. The study’s aim is therefore to examine how the influence Swedishmedia industry’s work enviroment has been influenced by metoo so far, and to examine how ahashtag functions to understand the meaning it has had for the metoo-movement. To answer the study’s research question, several qualitative interviews with several important persons based on the research question has been made. The majority of the informants are working in the media industry and they therefore have insight and their own perspectives regarding how metoo has affected their workplace so far. To gain more relevant information, a metoo-expert has been interviewed, and to examine the connection between the hashtag and metoo a digital expert has been interviewed as well. Both theory and the collected data from the informants indicates that metoo has affected the Swedish media industry’s work environment. It is also possible to see that the hashtag itself has been important for metoo’s spread. The question about equality and sexual harassment has begun to be discussed more openly, which among other things has led to the fact that many companies have begun to work more actively to remove sexual harassment and similar at their workplaces after metoo broke through.