In this thesis we have performed a study involving behavior changes in anonymityonline with different techniques. We have researched anonymous platforms and howthe anonymity changes people’s behavior, for a greater understanding of the field.From our results we were able to conclude that changes of people’s behaviors areeffected by which platform they were using. We categorized the behaviors into threedifferent categories based on the results of a qualitative analysis method of our data.We were able to conclude that one of the most common reasons to use anonymity ishow much more users perceived others to be more open and honest. However, wewere also able to see how anonymity had its drawback in forms of, most commonly,“trolls”. Based on our results we draw the conclusion that the behavior changespeople have are balanced between the positives and the negative behaviors. We alsofind in our study that the positives outweigh the negative.

Metalcore är en subgenre som är ständigt ifrågasatt av många. En del metalfans ser metalcore som någoting dåligt i metalgenren och tycker att subgenren inte är metal. I denna studie undersöks hur olika gränser skapas mellan genrerna metal och metalcore, detta genom att undersöka argumenten som metalfansen har om metalcore. Argumenten analyseras med hjälp av en kvalitativ argumentationsanalys med material från internetforumet Reddit. Resultatet visar på att negativitet finns mot metalcore, genren är alldeles för simpel och alldeles för mainstream för metalfans. Metalcore-låtar är endast en omstuvning av andra metalcore-låtar enligt metalfans. Den visar också en sorts splittring av fansen där några av metalfansen tycker om äldre metalcore.

The purpose of this project is to examine the visual rhetoric of one online community. Drawing heavily from the work of Laurie Gries (2015), I track the evolution of an image as it circulates through a forum of photo manipulators in the group “PhotoShopBattles.” While Gries’ work traced the evolution of the iconic Obama Hope poster and its iterations in various media, this project restricts its observations to the images posted to one webpage, focusing on one evolutionary chain. By narrowing the focus to one internet forum page that evolved over the course of one week, we can observe linear evolution that occurs quite rapidly. While the study of Obama Hope covered years, the works in this study were constructed in a matter of days. Additionally, the site records the step-by-step progress of the reformed work. The Obama Hope work offers guidance to the work in this project. Using this method, an image’s evolution is broken down into steps and principles noted in the life of the image or multiple-image. By recreating a slightly modified version of Gries’ method, this work seeks to decode the meanings and outcomes that are created and changed around one set of evolving images. New materialism offers a way to explore the visual rhetorical moves of members of an online community and discuss the outcomes associated with those moves. With a deeper understanding of the environment surrounding image creation and the outcomes derived from an image, we can better understand the way image is used rhetorically online.

The Canadian Online Harms Legislation (COHL) proposal identifies proactive Automated Moderation as a solution to classifying and removing online content which violates norms such as hate. Emerging automated moderation algorithms include Google Perspective, a machine learning model which scores hateful features in text content as “toxicity.” This study identifies that hateful community content norms are currently emerging on volunteer user moderation platforms such as Reddit. To operationalize these concepts, a Theoretical Framework is constructed using Gorwa’s (2019) Platform Governance models and Massanari’s (2017) overview of Toxic Technoculture communities. While previous research exploring community toxicity is discussed, there is a gap in research which analyzes the Post, Comment, and Image Meme contributions of Reddit Moderator users to hateful community content norms. As such, an analysis of the Reddit community R/Metacanada is constructed which compares the toxicity of Moderator and user contributions using Google Perspective. The results of the applied Mann-Whitney U test analysis indicate that r/Metacanada Moderators and users contribute content at similar toxicity levels. Supplementing these tests, RQ1 then structures a qualitative analysis of false negative results which may emerge in the automated classification of multi-modal image content. Identifying that hate in online memes is structured through layered Signifier and Signified elements, a critical discussion is established which interprets potential marginalizing effects of the COHL’s automated moderation applying Noble’s (2018) theory of Technological Redlining. As such, this thesis immerses itself within the contemporary context of online content regulation, drawing upon existing conceptualizations and methodological approaches, offering a critical discussion of regulating hate content using automated algorithms.

Online communities provide a fertile ground for analyzing people\'s behavior and improving our understanding of social processes. For instance, when modeling social interaction online, it is important to understand when people are reacting to each other. Also, since both people and communities change over time, we argue that analyses of online communities that take time into account will lead to deeper and more accurate results. In many cases, however, users behavior can be easily missed: users react to content in many more ways than observed by explicit indicators (such as likes on Facebook or replies on Twitter) and poorly aggregated temporal data might hide, misrepresent and even lead to wrong conclusions about how users are evolving. In order to address the problem of detecting non-explicit responses, we present a new approach that uses tf-idf similarity between a user\'s own tweets and recent tweets by people they follow. Based on a month\'s worth of posting data from 449 ego networks in Twitter, this method demonstrates that it is likely that at least 11% of reactions are not captured by the explicit reply and retweet mechanisms. Further, these uncaptured reactions are not evenly distributed between users: some users, who create replies and retweets without using the official interface mechanisms, are much more responsive to followees than they appear. This suggests that detecting non-explicit responses is an important consideration in mitigating biases and building more accurate models when using these markers to study social interaction and information diffusion. We also address the problem of users evolution in Reddit based on comment and submission data from 2007 to 2014. Even using one of the simplest temporal differences between usersyearly cohortswe find wide differences in people\'s behavior, including comment activity, effort, and survival. Furthermore, not accounting for time can lead us to misinterpret important phenomena. For instance, we observe that average comment length decreases over any fixed period of time, but comment length in each cohort of users steadily increases during the same period after an abrupt initial drop, an example of Simpson\'s Paradox. Dividing cohorts into sub-cohorts based on the survival time in the community provides further insights; in particular, longer-lived users start at a higher activity level and make more and shorter comments than those who leave earlier. These findings both give more insight into user evolution in Reddit in particular, and raise a number of interesting questions around studying online behavior going forward. / Comunidades online proporcionam um ambiente fértil para análise do comportamento de indivíduos e processos sociais. Por exemplo, ao modelarmos interações sociais online, é importante compreendemos quando indivíduos estão reagindo a outros indivíduos. Além disso, pessoas e comunidades mudam com o passar do tempo, e levar em consideração sua evolução temporal nos leva a resultados mais precisos. Entretanto, em muitos casos, o comportamento dos usuários pode ser perdido: suas reações ao conteúdo ao qual são expostos não são capturadas por indicadores explícitos (likes no Facebook, replies no Twitter). Agregações temporais de dados pouco criteriosas podem ocultar, enviesar ou até levar a conclusões equivocadas sobre como usuários evoluem. Apresentamos uma nova abordagem para o problema de detectar respostas não-explicitas que utiliza similaridade tf-idf entre tweets de um usuário e tweets recentes que este usuário recebeu de quem segue. Com base em dados de postagens de um mês para 449 redes egocêntricas do Twitter, este método evidencia que temos um volume de ao menos 11% de reações não capturadas pelos mecanismos explicitos de reply e retweet. Além disso, essas reações não capturadas não estão uniformemente distribuídas entre os usuários: alguns usuários que criam replies e retweets sem utilizar os mecanismos formais da interface são muito mais responsivos a quem eles seguem do que aparentam. Isso sugere que detectar respostas não-explicitas é importante para mitigar viéses e construir modelos mais precisos a fim de estudar interações sociais e difusão de informação. Abordamos o problema de evolução de usuários no Reddit com base em dados entre o período de 2007 a 2014. Utilizando métodos simples de diferenciação temporal dos usuários -- cohorts anuais -- encontramos amplas diferenças entre o comportamento, que incluem criação de comentários, métricas de esforço e sobrevivência. Desconsiderar a evolução temporal pode levar a equívocos a respeito de fenômenos importantes. Por exemplo, o tamanho médio dos comentários na rede decresce ao longo de qualquer intervalo de tempo, mas este tamanho é crescente em cada uma das cohorts de usuários no mesmo período, salvo de uma queda inicial. Esta é uma observação do Paradoxo de Simpson. Dividir as cohorts de usuários em sub-cohorts baseadas em anos de sobrevivência na rede nos fornece uma perspectiva melhor; usuários que sobrevivem por mais tempo apresentam um maior nível de atividade inicial, com comentários mais curtos do que aqueles que sobrevivem menos. Com isto, compreendemos melhor como usuários evoluem no Reddit e levantamos uma série de questões a respeito de futuros desdobramentos do estudo de comportamento online.

Online communities provide a fertile ground for analyzing people\'s behavior and improving our understanding of social processes. For instance, when modeling social interaction online, it is important to understand when people are reacting to each other. Also, since both people and communities change over time, we argue that analyses of online communities that take time into account will lead to deeper and more accurate results. In many cases, however, users behavior can be easily missed: users react to content in many more ways than observed by explicit indicators (such as likes on Facebook or replies on Twitter) and poorly aggregated temporal data might hide, misrepresent and even lead to wrong conclusions about how users are evolving. In order to address the problem of detecting non-explicit responses, we present a new approach that uses tf-idf similarity between a user\'s own tweets and recent tweets by people they follow. Based on a month\'s worth of posting data from 449 ego networks in Twitter, this method demonstrates that it is likely that at least 11% of reactions are not captured by the explicit reply and retweet mechanisms. Further, these uncaptured reactions are not evenly distributed between users: some users, who create replies and retweets without using the official interface mechanisms, are much more responsive to followees than they appear. This suggests that detecting non-explicit responses is an important consideration in mitigating biases and building more accurate models when using these markers to study social interaction and information diffusion. We also address the problem of users evolution in Reddit based on comment and submission data from 2007 to 2014. Even using one of the simplest temporal differences between usersyearly cohortswe find wide differences in people\'s behavior, including comment activity, effort, and survival. Furthermore, not accounting for time can lead us to misinterpret important phenomena. For instance, we observe that average comment length decreases over any fixed period of time, but comment length in each cohort of users steadily increases during the same period after an abrupt initial drop, an example of Simpson\'s Paradox. Dividing cohorts into sub-cohorts based on the survival time in the community provides further insights; in particular, longer-lived users start at a higher activity level and make more and shorter comments than those who leave earlier. These findings both give more insight into user evolution in Reddit in particular, and raise a number of interesting questions around studying online behavior going forward. / Comunidades online proporcionam um ambiente fértil para análise do comportamento de indivíduos e processos sociais. Por exemplo, ao modelarmos interações sociais online, é importante compreendemos quando indivíduos estão reagindo a outros indivíduos. Além disso, pessoas e comunidades mudam com o passar do tempo, e levar em consideração sua evolução temporal nos leva a resultados mais precisos. Entretanto, em muitos casos, o comportamento dos usuários pode ser perdido: suas reações ao conteúdo ao qual são expostos não são capturadas por indicadores explícitos (likes no Facebook, replies no Twitter). Agregações temporais de dados pouco criteriosas podem ocultar, enviesar ou até levar a conclusões equivocadas sobre como usuários evoluem. Apresentamos uma nova abordagem para o problema de detectar respostas não-explicitas que utiliza similaridade tf-idf entre tweets de um usuário e tweets recentes que este usuário recebeu de quem segue. Com base em dados de postagens de um mês para 449 redes egocêntricas do Twitter, este método evidencia que temos um volume de ao menos 11% de reações não capturadas pelos mecanismos explicitos de reply e retweet. Além disso, essas reações não capturadas não estão uniformemente distribuídas entre os usuários: alguns usuários que criam replies e retweets sem utilizar os mecanismos formais da interface são muito mais responsivos a quem eles seguem do que aparentam. Isso sugere que detectar respostas não-explicitas é importante para mitigar viéses e construir modelos mais precisos a fim de estudar interações sociais e difusão de informação. Abordamos o problema de evolução de usuários no Reddit com base em dados entre o período de 2007 a 2014. Utilizando métodos simples de diferenciação temporal dos usuários -- cohorts anuais -- encontramos amplas diferenças entre o comportamento, que incluem criação de comentários, métricas de esforço e sobrevivência. Desconsiderar a evolução temporal pode levar a equívocos a respeito de fenômenos importantes. Por exemplo, o tamanho médio dos comentários na rede decresce ao longo de qualquer intervalo de tempo, mas este tamanho é crescente em cada uma das cohorts de usuários no mesmo período, salvo de uma queda inicial. Esta é uma observação do Paradoxo de Simpson. Dividir as cohorts de usuários em sub-cohorts baseadas em anos de sobrevivência na rede nos fornece uma perspectiva melhor; usuários que sobrevivem por mais tempo apresentam um maior nível de atividade inicial, com comentários mais curtos do que aqueles que sobrevivem menos. Com isto, compreendemos melhor como usuários evoluem no Reddit e levantamos uma série de questões a respeito de futuros desdobramentos do estudo de comportamento online.

Studiens syfte är att undersöka hur pseudonymitet och/eller pseudoanonymitet påverkar diskussionen av svensk politik på nätet samt de för och nackdelar som kan finnas med denna eventuella förändring. Undersökningen är dock begränsad till effekterna på diskussionerna som hålls på Reddit/r/svenskpolitik. För att kunna granska detta undersöks användarnas egen uppfattning om hur beteenden och diskussioner förändras på ett forum med pseudonymitet, samt att det genomförs en argumentationsanalys av en slumpvis utvald post på forumet för att kunna ställa användarnas uppfattningar emot det faktiska beteende och kommunicerade som sker på forumet. Användarnas egen uppfattning inhämtas genom en enkätundersökning och bearbetas genom en kvantitativ innehållsanalys. Den slumpvis utvalda posten bearbetas med en pro et contra metod för att finna strukturer och analyseras sedan efter en kvalitativ innehållsanalys. Undersökningen lutar sig på forskning kring hur anonymitet på nätet påverkar individer och gruppers beteende och kommunikation samt gruppolariseringsteorin för att kunna granska och förklara hur pseudonymitet och/eller pseudoanonymitet kan förändra beteenden och kommunikation. Undersökningen finner att användarna upplever en förändring i beteende och kommunikation på grund av pseudonymitet och/eller pseudoanonymitet samt att de upplever att denna förändring är större bland andra användare än land dem själva. Undersökningen finner även att det finns en upplevd gruppolarisering hos användarna. Användarna finner att fördelarna och nackdelarna båda beror på den mer öppna diskussionen som medföljer konsekvensfriheterna på ett forum med pseudonymitet och pseudoanonymitet. Undersökningen finner också att det i den slumpvis utvalda posten återfinns spår av gruppolarisering samt mer extrema åsikter. Det finns också en tydlig invandringskritisk underton i posten som får stöd i de röstningssystem som finns på forumet. Resultaten visar också på en komplexitet i frågan om anonymitetens vara eller icke vara med tydliga för- och nackdelar men ingen koncensus.

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Den här studien undersöker hur diskussioner på ekokammare ser ut i sociala medier. Mer specifikt undersöks kommentarsfälten i två olika länkar från den sociala länkaggregatorn Reddit och underforumet /r/Politics. Undersökningen görs i syfte att se hur diskussioner tar form på Reddit för att ge en klarare bild av ekokammare som fenomen på internet. Studiens teoretiska grund ligger i teorierna om selektiv exponering och partisk assimilering, samt i en teoretisering av hur grupper formas på de sociala medierna Twitter och Reddit. Studien använder sig av både en kvantitativ innehållsanalys och en kvalitativ textanalys, vilka främst grundar sig i argumentationsanalysen. Innehållsanalysen används först för att jämföra /r/Politics användares ställningstagande till de länkar som de kommenterar på och sedan för att möjliggöra en korrelationsanalys av arguments styrka och dess användargivna poäng på Reddit. Textanalysen görs med hjälp av begrepp från argumentationsanalysen och används för att sätta in studiens material i fem olika teman. Det mest framträdande temat får namnet Kommentarer av papegojor och beskriver kommentarer som konstrueras likadant som tidigare kommentarer utan försök till att föra något nytt till diskussionen. Överlag visar studiens resultat att responsen ser likadan ut för båda länkarna och att en stor majoritet av kommentarerna är på samma sida i argumentet. Resultatet visar också att argumentens styrka är oberoende av om en kommentar får en positiv respons eller inte. / This study explores how discussions in echo chambers take form on social media. More specifically, the study explores the comment section of two different links on the social news-aggregator website Reddit and the sub-forum /r/Politics. The purpose of the aforementioned exploration is to give a clearer picture of echo chambers as a phenomena on the Internet. The study’s theoretical background is based on selective exposure and biased assimilation theory, as well as a theorization of how groups form on Twitter and Reddit. The study uses a quantitative content analysis and a qualitative text analysis, which are both based on argumentation theory. The content analysis is first used to compare the viewpoint of /r/Politics users with the subject of the links they comment on, and then to make a correlation analysis of the strength of arguments and the arguments’ user-given points on Reddit. Last a qualitative text analysis is done using concepts from argumentation theory, which allows for the observation of five different themes. The most prominent of these themes was given the name Comments by parrots and describes comments that are constructed in a similar fashion to older comments, without an attempt to bring something new to the discussion.  Overall, the study’s result shows that the response to the two links is similar, and that both have an overwhelming majority of comments supporting one side. The result also shows that a strong argument does not necessarily mean that the comment will get a positive response.

In an attempt to shed light on the further sophistication of opinion leadership in online communities, this study examined the forces and structures that affect their formation in the League of Legends subreddit. By investigating what users thought about the various types of individuals with which the communicate, the researcher hoped to begin to understand and record how those forces work bother on this particular subreddit and in mass media beyond. Opinion leadership continues to be an integral force in deciding what information is consumed by a public and under what frames and agendas it is contextualized. If researchers can operationalize formal definitions for the influences and structures that occur online, they can better navigate the deep waters that are global communication on the internet.