Relevant bibliographies by topics / YouTube commenters

Academic literature on the topic 'YouTube commenters'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "YouTube commenters"

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Thelwall, Mike, and Amalia Mas-Bleda. "YouTube science channel video presenters and comments: female friendly or vestiges of sexism?" Aslib Journal of Information Management 70, no. 1 (January 15, 2018): 28–46. http://dx.doi.org/10.1108/ajim-09-2017-0204.

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Purpose The purpose of this paper is to analyse popular YouTube science video channels for evidence of attractiveness to a female audience. Design/methodology/approach The influence of presenter gender and commenter sentiment towards males and females is investigated for 50 YouTube science channels with a combined view-count approaching ten billion. This is cross-referenced with commenter gender as a proxy for audience gender. Findings The ratio of male to female commenters varies between 1 and 39 to 1, but the low proportions of females seem to be due to the topic or presentation style rather than the gender of the presenter or the attitudes of the commenters. Although male commenters were more hostile to other males than to females, a few posted inappropriate sexual references that may alienate females. Research limitations/implications Comments reflect a tiny and biased sample of YouTube science channel viewers and so their analysis provides weak evidence. Practical implications Sexist behaviour in YouTube commenting needs to be combatted but the data suggest that gender balance in online science presenters should not be the primary concern of channel owners. Originality/value This is the largest scale analysis of gender in YouTube science communication.
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Perrino, Sabina. "Recontextualizing racialized stories on YouTube." Storytelling in the Digital Age 27, no. 2 (October 6, 2017): 261–85. http://dx.doi.org/10.1075/ni.27.2.04per.

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Abstract When stories (re)appear in YouTube videos, they are recontextualized not only by their titling and editing, but especially by chains of online comments. This article explores how a Northern Italian politician’s racialized story, which was first recontextualized by a TV news reporter on a YouTube video, is further recontextualized for different ends by commenters on that video. I show how these commenters negotiate and reframe the racial aspects of digital discourse and its sociocultural meanings through their various responses and across different temporal and spatial scales. By applying linguistic anthropological and sociolinguistic theories and narrative analytical tools to digital storytelling, this study emphasizes how racialized language is neither stable nor unidirectional, but is rather constantly negotiated discursively among various groups of virtual audience members. Besides investigating the pragmatics of narrative interaction in the digital realm, this article speaks to methodological challenges that surround the study of online storytelling.
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Bodrunova, Svetlana S., and Ivan S. Blekanov. "A Self-Critical Public: Cumulation of Opinion on Belarusian Oppositional YouTube before the 2020 Protests." Social Media + Society 7, no. 4 (October 2021): 205630512110634. http://dx.doi.org/10.1177/20563051211063464.

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YouTube-based discussions are a growing area of academic attention. However, we still lack knowledge on whether YouTube provides for forming critical publics in countries with no established democratic tradition. To address this question, we study commenting to Belarusian oppositional YouTube blogs in advance of the major wave of Belarusian post-election protests of 2020. Based on the crawled data of the whole year of 2018 for six Belarusian political videoblogs, we define the structure of the commenters’ community, detect the core commenters, and assess their discourse for aggression, orientation of dialogue, direction of criticism, and antagonism/agonism. We show that, on Belarusian YouTube, the commenters represented a genuine adversarial self-critical public with cumulative patterns of solidarity formation and find markers of readiness for the protest spillover.
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Thelwall, Mike. "Can museums find male or female audiences online with YouTube?" Aslib Journal of Information Management 70, no. 5 (September 17, 2018): 481–97. http://dx.doi.org/10.1108/ajim-06-2018-0146.

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Purpose The purpose of this paper is to investigates if and why audience gender ratios vary between museum YouTube channels, including for museums of the same type. Design/methodology/approach Gender ratios were examined for public comments on YouTube videos from 50 popular museums in English-speaking nations. Terms that were more frequently used by males or females in comments were also examined for gender differences. Findings The ratio of female to male YouTube commenters varies almost a hundredfold between museums. Some of the difference could be explained by gendered interests in museum themes (e.g. military, art) but others were due to the topics chosen for online content and could address a gender minority audience. Practical implications Museums can attract new audiences online with YouTube videos that target outside their expected demographics. Originality/value This is the first analysis of YouTube audience gender for museums.
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Koven, Michèle, and Isabelle Simões Marques. "Performing and evaluating (non)modernities of Portuguese migrant figures on YouTube: The case of Antonio de Carglouch." Language in Society 44, no. 2 (April 2015): 213–42. http://dx.doi.org/10.1017/s0047404515000056.

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AbstractWe analyze how France-based YouTube comedic performers Ro et Cut enact the stylized figure of a Portuguese migrant in France, Antonio, in their most popular video,Carglouch. We then examine how commenters respond to the enactment. Specifically, we apply Irvine and Gal's notion of semiotic differentiation to study how participants use language to construct and apply polycentric notions of modernity/nonmodernity. This allows us to analyze how YouTube performers and commenters produce their own and Antonio's relative (non)modernity, as they orient to different versions of a modern/nonmodern axis of differentiation, situated in multiple ‘centers’: France, Portugal, or Europe. That is, participants either interpret Antonio as a nonmodern immigrant in France, a nonmodern emigrant from Portugal, or an international representative of Portugal, spreading nonmodern images of Portugal abroad. We then consider how participants bring these differently centered images of the nonmodern Other into dialogue with each Other, with different outcomes for variously positioned participants. (Migration, heteroglossia, YouTube, transnationalism, Luso-descendants, (non)modernity, France, Portugal)*
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Tovares, Alla V. "Negotiating “thick” identities through “light” practices: YouTube metalinguistic comments about language in Ukraine." Multilingua 38, no. 4 (July 26, 2019): 459–84. http://dx.doi.org/10.1515/multi-2018-0038.

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Abstract This article investigates YouTube metalinguistic comments about language varieties in Ukraine as a “light” practice to demonstrate how knowledge and identities are negotiated online against the backdrop of larger sociopolitical discourses that circulate in and about Ukraine. This work adds to our understanding of online, or “light”, identity construction by suggesting that taking up epistemic stances and overtly asserting epistemic statuses are often a part of such identity work. Furthermore, deliberate linguistic choices not only serve to index identities but also create (dis)affiliations and thus can be deployed as a means of inclusion or exclusion from a particular online group, often shifting between (and integrating) local and global themes and audiences. The analysis shows how by drawing on repetition, deixis, pronouns, and lexical choices, YouTube commenters police, reify, and contest the extant language practices and underlying ideologies and in so doing create a foundation for grassroots ideological and political mobilization.
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Raby, Rebecca, and Mary-Beth Raddon. "Is She a Pawn, Prodigy or Person with a Message? Public Responses to a Child’s Political Speech." Canadian Journal of Sociology 40, no. 2 (April 27, 2015): 163–88. http://dx.doi.org/10.29173/cjs21758.

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The 2012 appearance on YouTube of a speech about banking reform prompted mainstream news coverage and hundreds of online comments, dwelling less on the content of the speech than on the speaker, Victoria Grant, a twelve year-old girl. A qualitative content analysis of over 600 comments revealed disagreement about children’s capacities as participants in political and economic discussions. Commenters’ mixed beliefs were linked to dominant, frequently contradictory, discourses of childhood. Victoria Grant was positioned as embedded in educational processes, as competent but often exceptional, as incompetent, and as innocent and therefore vulnerable. These conflicting yet emotionally charged narratives of childhood illustrate the concept’s rhetorical elasticity and flexibility. Despite advances in the cause of children’s social participation in recent years, most of these adult-centered narratives undermine the idea of children as legitimate contributors to economic analysis and political debate.
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Teng, Shasha, Kok Wei Khong, Saeed Pahlevan Sharif, and Amr Ahmed. "YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis." JMIR Public Health and Surveillance 6, no. 4 (October 1, 2020): e19618. http://dx.doi.org/10.2196/19618.

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Background Poor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities. Objective The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters’ perceptions and sentiments of healthy eating through text mining techniques. Methods This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure. Results With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily. Conclusions This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.
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Pihlaja, Stephen. "“Are You Religious or are You Saved?”." Fieldwork in Religion 6, no. 1 (January 20, 2012): 47–63. http://dx.doi.org/10.1558/firn.v6i1.47.

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Using membership categorization analysis, this article investigates membership categories in a YouTube video made by an Evangelical Christian in which he differentiates between “saved” and “religious” users. Analysis will take a discourse-centred, multimodal approach grounded in longitudinal observation, using analysis of video discourse to instruct analysis of video images and user comments. Findings will show that categorization is accomplished by using recognized categories with ambiguous descriptions of category-bound activities that include metaphors, such as “being hungry for God” and not “hanging out with atheists.” These categories are recognized by commenters on the video, but the category bound activities applied to the category members are disputed. Findings will also show that scriptural reference plays an important role in categorization in the video, drawing on direct Bible quotes as well as paraphrases of key passages.
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Bodrunova, Svetlana S., Anna Litvinenko, Ivan Blekanov, and Dmitry Nepiyushchikh. "Constructive Aggression? Multiple Roles of Aggressive Content in Political Discourse on Russian YouTube." Media and Communication 9, no. 1 (February 3, 2021): 181–94. http://dx.doi.org/10.17645/mac.v9i1.3469.

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Today, aggressive verbal behavior is generally perceived as a threat to integrity and democratic quality of public discussions, including those online. However, we argue that, in more restrictive political regimes, communicative aggression may play constructive roles in both discussion dynamics and empowerment of political groups. This might be especially true for restrictive political and legal environments like Russia, where obscene speech is prohibited by law in registered media and the political environment does not give much space for voicing discontent. Taking Russian YouTube as an example, we explore the roles of two under-researched types of communicative aggression—obscene speech and politically motivated hate speech—within the publics of video commenters. For that, we use the case of the Moscow protests of 2019 against non-admission of independent and oppositional candidates to run for the Moscow city parliament. The sample of over 77,000 comments for 13 videos of more than 100,000 views has undergone pre-processing and vocabulary-based detection of aggression. To assess the impact of hate speech upon the dynamics of the discussions, we have used Granger tests and assessment of discussion histograms; we have also assessed the selected groups of posts in an exploratory manner. Our findings demonstrate that communicative aggression helps to express immediate support and solidarity. It also contextualizes the criticism towards both the authorities and regime challengers, as well as demarcates the counter-public.
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Dissertations / Theses on the topic "YouTube commenters"

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Lorentz, Isac, and Gurjiwan Singh. "Sentiment Analysis on Youtube Comments to Predict Youtube Video Like Proportions." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301779.

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Social media websites are some of the world’s most popular websites and allow all users to have a voice and express opinions and emotions. Using sentiment analysis, these users’ opinions and emotions can be extracted and quantified. This study examines sentiment analysis on Youtube comments and how well the number of comments classified as positive, neutral and negative can be useful in predicting the like proportion of a Youtube video. Four different prediction formulas were tested that utilize neutral comments in different ways. Five different classifiers were examined with pretraining on Youtube comments, tweets, and a combination of tweets and comments. Some positive correlation between the predicted and actual like proportion was found. The best performing configuration was a logistic regression classifier trained only on Youtube comments with a prediction that attributes all comments classified as positive and neutral to likes. However, the errors of this type of prediction are so large that it likely has little real-world application. Possible method improvements include filtering out spam comments and include emoji sentiment.
Sociala medier är några av världens mest populära webbplatser och låter alla användare ha en röst och uttrycka sina åsikter och känslor. Med sentimentanalys kan dessa åsikter och känslor extraheras och kvantifieras. Denna studie undersöker sentimentanalys på Youtube-kommentarer och hur användbara antalet positivt, neutralt och negativt klassificerade kommentarer kan vara i förutsägelsen av like-proportionen på en Youtube-video. Fyra olika formler för förutsägelsen som använder neutrala kommentarer på olika sätt undersöktes. Fem olika klassificerare undersöktes med förhandsträning på Youtubekommentarer, tweets och en kombination av dessa. Ett positivt samband mellan faktisk och estimerad like-proportion hittades. Den bäst presterande konfigurationen var en logistisk regression klassificerare där alla neutrala och positiva kommentarer tillskrivs till likes och träning endast sker på Youtubekommentarer. Felen för dessa förutsägelser är så stora att förutsägelserna troligen har begränsad nytta i verkligheten. Möjliga förbättringar av metoden inkluderar att filtrera ut spamkommentarer och ta med emoji-sentiment.
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Kvedaraite, Indre. "Sentiment Analysis of YouTube Public Videos based on their Comments." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105754.

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With the rise of social media and publicly available data, opinion mining is more accessible than ever. It is valuable for content creators, companies and advertisers to gain insights into what users think and feel. This work examines comments on YouTube videos, and builds a deep learning classifier to automatically determine their sentiment. Four Long Short-Term Memory-based models are trained and evaluated. Experiments are performed to determine which deep learning model performs with the best accuracy, recall, precision, F1 score and ROC curve on a labelled YouTube Comment dataset. The results indicate that a BiLSTM-based model has the overall best performance, with the accuracy of 89%. Furthermore, the four LSTM-based models are evaluated on an IMDB movie review dataset, achieving an average accuracy of 87%, showing that the models can predict the sentiment of different textual data. Finally, a statistical analysis is performed on the YouTube videos, revealing that videos with positive sentiment have a statistically higher number of upvotes and views. However, the number of downvotes is not significantly higher in videos with negative sentiment.
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Zhang, Leticia Tian. "Understanding danmu: interaction, learning and multimodality in fan video comments." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/669267.

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When watching television, many people use social media to share real-time opinions and feelings. The mediated co-viewing is widely studied under the name of “second screen” or “social TV”. In Japan and China, a technology integrates social media—the comments section—into the video screen, creating an unprecedented form of participation called danmu or danmaku (“barrage”). This research aims to describe the characteristics of this emergent discursive genre. Using content and discourse analysis, we analyzed danmu from 1) a television series and 2) a thread of “funny danmu” from popular video sharing sites. The results revealed that users showed diverse interests (plot, language, culture), appropriated multimodal resources (color, position, symbols) to make fun, while constructing meaning using unconventional discursive strategies. The study indicates the potential of danmu as a space for informal learning, semiotic creativity, and (para)social interaction, and calls for future research into video sharing practices beyond YouTube.
Cuando ven la televisión, muchas personas usan las redes sociales para compartir opiniones y emociones en tiempo real. La covisualización mediada se estudia ampliamente bajo la denominación de “segunda pantalla” o “televisión social”. En Japón y China, una reciente tecnología permite incrustar las redes sociales (la sección de comentarios) dinámicamente en las secuencias del video, creando una forma de participación sin precedentes llamada danmu o danmaku (“barrera de fuego”). Este trabajo se propone describir las características de este género discurso emergente. Utilizando el análisis del contenido y del discurso, analizamos danmu de: 1) una serie de televisión, y 2) un hilo de “danmu graciosos” de sitios populares de repositorios de videos. Nuestros resultados revelan que los usuarios tienen diversos intereses (trama, lenguaje, cultura), se apropian de recursos multimodales (color, posición, símbolos) para hacer humor y construyen significados usando estrategias discursivas originales. Este estudio muestra el potencial del danmu como un espacio para el aprendizaje informal, la creatividad semiótica y la interacción (para)social, además de motivar futuras investigaciones sobre las prácticas de compartir videos más allá de YouTube.
Quan veuen la televisió, moltes persones fan servir les xarxes socials per compartir opinions i emocions en temps real. La covisualització mediada s’estudia àmpliament sota la denominació de “segona pantalla” o “televisió social”. Al Japó i a la Xina, una tecnologia recent permet incrustar les xarxes socials (la secció de comentaris) dinàmicament a les seqüències de vídeo, creant una forma de participació sense precedents anomenada danmu o danmaku (“barrera de foc”). Aquest treball es proposa descriure les característiques d’aquest gènere discursiu emergent. Utilitzant l’anàlisi del contingut i del discurs, estudiem danmu de: 1) una sèrie de televisió, i 2) un fil de “danmu graciosos” de llocs populars de repositoris de vídeos. Els nostres resultats revelen que els usuaris tenen diversos interessos (trama, llenguatge, cultura), s’apropien dels recursos multimodals (color, posició, símbols) per fer humor i construeixen significats utilitzant estratègies discursives originals. Aquest estudi mostra el potencial del danmu com a espai per a l’aprenentatge informal, la creativitat semiòtica i la interacció (para)social, a més de motivar futures investigacions sobre les pràctiques de compartir vídeos més enllà de YouTube.
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Karlsson, Katarina, and Jenny Andersén. "Anonymitet och YouTube : Konsekvenser för YouTube-användares kommentarer i och med minskad anonymitet." Thesis, Södertörns högskola, Institutionen för naturvetenskap, miljö och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-18524.

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Denna studie undersöker hur minskad anonymitet kan förändra sättet människor skriver kommentarer på YouTube-videor. YouTube begärde nyligen att användarna skulle ange sina fullständiga namn. Användare är ännu inte skyldiga att ändra sitt användarnamn, men om de inte gör det ombes de att svara på varför de väljer att ha kvar sitt alias. För att undersöka detta lät vi först användare svara på en enkät för att se vad de tycker om att vara anonym kontra att ge ut sitt riktiga namn, och om att ge sitt riktiga namn gör dem mer uppmärksamma kring deras sätt att kommentera på YouTube. Sedan, för att se vad som verkligen händer i kommentarsfälten, gick vi igenom ett antal kommentarer för att hitta mönster i hur ett alias kontra ett riktigt namn relaterar till processen att skriva negativa kontra positiva kommentarer. Kommentarerna analyserades med hjälp av en modell som mäter olika grader av negativitet och positivitet i en kommentar. Våra studier visar att ändringen från ett alias till riktigt namn, påverkar hur kommentarer skrivs och att ett högre antal negativa kommentarer kommer från användare som använder ett alias. Resultatet är en indikation på hur minskning av anonymitet kan leda till mindre negativitet i YouTubes kommentarsfält.
This study examines how reducing anonymity options can change the way people write comments on YouTube-videos. YouTube recently requested users to enter their real names as a username. Users are not yet obligated to do so, but if they do not, they are asked to answer why they chose remain their alias. To investigate this we first let users answer a survey to see what they felt about being anonymous versus giving their real name, and if providing their real name made them more cautious about their way of interacting on YouTube. Then, to see what really is going on in the comment sections, we went through a group of comments to spot patterns in how an alias or a real name is related to the process of writing negative versus positive comments. Comments were analyzed with help from a model that measures the stages of negativity and positivity in a comment. Our studies indicate that the change from alias to real name, make an impact in how comments are made and that much higher rate of negative comments come from users using an alias. The result is an indication that less anonymity may lead to less negativity in YouTube’s comment sections.
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Rodriguez, Gabriel R. "The Enregisterment of Dialects in Japanese YouTube Comments| A Comparative Analysis." Thesis, Georgetown University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10788816.

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This study contextualizes the explosive valorization and commodification of dialect in Japan since the 1980s, known as the “dialect boom”, in terms of Japanese social and economic issues and the growing public interest in diversity within Japan. While the dialect boom has been widely studied in sociolinguistics, little work has related it to the growing valorization of diversity, and most recent work has focused primarily on the Kansai dialect. To these ends, I analyze the enregisterment of six Japanese dialects, those of Osaka, Hakata, Nagoya, Aomori, Okinawa, and K?sh?. I analyze a corpus of YouTube comments responding to videos of dialect usage, using stance (DuBois 2007) to break down the social acts that produce enregisterment (Agha 2003). I draw on the theories of indexicality (Johnstone and Kiesling 2008, Eckert 2008) and the discourse analytic concept of dialect performance (Schilling-Estes 1998, Coupland 2007) as guides to interpreting the micro-social interactions I observe, connecting them to a macro-social context through the theories of Standard Language Ideology (Lippi-Green 1997), identity construction (Bucholtz & Hall 2005), and folklorization (Fishman 1987).

I examine evaluations of dialect based on attractiveness, humorousness, intelligibility, folklorization, and country-ness, evaluate their relative prestige by investigating the willingness of speakers to debate dialect performances’ fidelity, and finally examine the political conflicts dialects are implicated in by looking at how they are related to questions of diversity and nationalism. The similarities between evaluations of the dialects of Okinawa and Aomori, particularly in the category of folklorization, suggest that the dialects of Aomori have accrued affective traits of an Indigenous language (such as nostalgia or sentimentality) despite being spoken by members of the ethnic majority. However, the conflicts that arise over the cases of Okinawa and Osaka suggest that the use of dialect as a marker of regional identity is now being integrated into a nationalist Japanese self-image as a country with rich internal diversity. This provides a means by which Japan can engage with the discourses of liberal multiculturalism and diversity without seriously threatening the hegemony of Japanese ethno-nationalism, suggesting a need to reevaluate the past focus on nihonjinron in building critiques of Japanese nationalist ideology.

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Hyberg, Martin, and Teodor Isaacs. "Predicting like-ratio on YouTube videos using sentiment analysis on comments." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229775.

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Social media is huge today. It allows anyone with an internet connection to voice their opinion all over the world with a single click. Many of the biggest social media platforms such as Facebook and Twitter do not allow users to leave negative feedback on posts. YouTube is different, it allows users to leave negative feedback in the form of a dislike with the click of a button. Each video also has a comment field with comments regarding the video. With the data that YouTube videos provide and sentiment analysis, the research question for this paper is: Can the comments on a YouTube video be used to determine what ratio of the viewers liked or disliked the video using sentiment analysis?. The results from this work showed that there is a weak correlation between the percentage of likes and the percentage of positive comments on a video. Because the fluctuation in the data and results, it is not accurate enough to be applied to any comment field on the internet, but it could be used as an indication. Many areas of the method could be improved for better results.
Sociala medier är enorma idag. De gör det möjligt för vem som helst med en internetuppkoppling att uttrycka sin åsikt över hela världen med ett enda klick. Men många av de största sociala medieplattformarna tillåter inte användaren att lämna negativ feedback på inlägg. Men YouTube är annorlunda, de tillåter användarna att lämna negativ feedback i form av en ogillning med ett enkelt klick. Varje video har även ett kommentarsfält med kommentarer på videon. Med de data som YouTube videor tillhandahåller och sentimentsanalys lyder forskningsfrågan för denna rapport: Kan kommentarerna på en YouTube-video användas för att avgöra vilket förhållande tittarna tyckte om eller ogillade videon med hjälp av sentimentsanalys?. Resultaten från detta arbete visade att det finns en svag korrelation mellan andelen gillningar och procentandelen positiva kommentarer på en video. På grund av fluktuationen i data och resultat är det inte tillräckligt för att tillämpas på alla kommentarsfält på internet, men det kan användas som en indikation. Många delar av metoden kan förbättras för ett bättre resultat.
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Epstein, Melker. "Matematiska samtal på Youtube : Olika matematikdiskurser i Youtubevideor och deraskommentarsfält." Thesis, Mittuniversitetet, Avdelningen för matematik och ämnesdidaktik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-34099.

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Allt fler människor använder sig av Youtube och andra öppna internetmiljöer för att lära sig matematik. Enligt tidigare studier finns det en stor spridning när det gäller bostadsort och ålder bland deltagarna i dessa miljöer. Min undersökning visar att ett urval om 10 Youtubevideor om ekvationslösning och deras kommentarsfält också kännetecknas av en stor spridning av matematikdiskurser bland deltagarna. En studie av 85 dialoger i kommentarsfälten visar att i omkring hälften av dialogerna uttrycker sig olika deltagare på sätt som kännetecknar olika matematikdiskurser, medan i den andra hälften alla deltagare uttrycker sig på sätt som kännetecknar samma matematikdiskurs. De förra dialogerna slutar mer sällan med att deltagarna upplever förståelse, medan lärandet i de senare dialogerna begränsas av vad som går att uttrycka inom ramen för den rådande diskursen. Genom att utveckla begrepp och kategorier för att beskriva matematikdiskurserna lägger studien grunden för fortsatt forskning, både kvalitativa med intervjuer av deltagare och kvantitativa med större material.
The use of Youtube and other open internet environments for learning mathematics is becoming increasingly common. According to earlier studies there is a great diversity of ages and nationalities among participants in these environments. My investigation shows that a selection of 10 Youtube videos about equation solving and their comments sections also are characterized by a diversity of mathematics discourses. A study of 85 dialogues in the comments sections shows that in approximately half of the dialogues different participants express themselves in manners characteristic of different mathematics discourses, while in the other half all the participants express themselves in manners characteristic of the same mathematics discourse. The former dialogues more seldom end with experiences of understanding, while learning in the latter group of dialogues is limited by what is possible to express within the dominant discourse. By developing terms and categories for describing the mathematics discourses this study lays the foundation for further research, both qualitative research comprising interviews with participants and quantitative research on greater amounts of data.
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Pärlbåge, Madeleine. "You are soooo cuteee!!!! : A critical discourse analysis of gender ideologies among YouTube comments." Thesis, Karlstads universitet, Institutionen för språk, litteratur och interkultur (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-78558.

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This essay examines YouTube comments to videos with male and female streaming players of the online game League of Legends. The research carries out a critical discourse analysis with the aim to find gender relevant language in order to analyze ideologies about gender stereotypes among viewers of streamers. The results showed that comments concerning players’ bodies and appearance were more common in comments on female than male players. There was also a higher expectation for female players to match sexual stereotypes close to the imagery of avatars than for male players to do so. Female players received more comments with sexual implications. Both male and female players received offers of sexual acts, but violent acts were only aimed against women. Written comments defending female players showed that there are ideological power struggles between viewers in this specific genre and that some viewers defend the female players’ place in the scene of streamed gaming.
Denna studie undersöker kommentarer på YouTube-videos med spelare av ett online-spel vid namn League of Legends. Undersökningen för en kritisk diskursanalys med syfte att hitta diskurs relevant för kön för att utreda vilka ideologier om kön och sexuella stereotyper som återfinns bland konsumenter av sådana videos. Resultatet visar att kommentarer som nämner spelarens kropp och utseende är betydligt vanligare för kvinnliga än manliga spelare. Resultatet visade också att det fanns en högre förväntan att kvinnliga spelare skulle matcha sexuella stereotyper som liknar bilden av kvinnliga avatarer än att manliga spelare skulle göra det. Kvinnliga spelare fick fler kommentarer av sexuell karaktär. Både manliga och kvinnliga spelare fick erbjudanden om sexuella handlingar, men våldsamma sexuella handlingar var bara riktade mot kvinnor. Svar på kommentarer som försvarar kvinnliga spelare visar på en kraftmätning mellan de som tittar på manliga och kvinnliga spelare inom denna specifika genre och att kvinnliga spelares plats inom denna sfär försvaras av vissa tittare.
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Alberto, Túlio Casagrande. "TubeSpam: Filtragem Automática de Comentários Indesejados Postados no YouTube." Universidade Federal de São Carlos, 2017. https://repositorio.ufscar.br/handle/ufscar/9137.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
YouTube has become an important video sharing platform. Several users regularly produce video content and make this task their main livelihood. However, such success is also drawing the attention of malicious users propagating undesired comments and videos, looking for self-promotion or disseminating malicious links which may have malwares and viruses. Since YouTube offers limited tools for blocking spam, the volume of such messages is shockingly increasing and harming users and channels owners. In addition to the problem being naturally online, comment spam filtering on YouTube is different than the traditional email spam filtering, since the messages are very short and often rife with spelling errors, slangs, symbols and abbreviations. This manuscript presents a performance evaluation of traditional online classification methods, aided by lexical normalization and semantic indexing techniques when applied to automatic filter YouTube comment spam. It was also evaluated the performance of MDLText, a promising text classification method based on the minimum description length principle. The statistical analysis of the results indicates that MDLText, Passive-Aggressive, Naïve Bayes, MDL and Online Gradient Descent obtained statistically equivalent performances. The results also indicate that the lexical normalization and semantic indexing techniques are effective to be applied to the problem. Based on the results, it is proposed and designed TubeSpam, an online tool to automatic filter undesired comments posted on YouTube.
O YouTube tem se tornado uma importante plataforma de compartilhamento de vídeos. Muitos usuários produzem regularmente conteúdo em vídeo e fazem desta tarefa seu principal meio de vida. Contudo, esse sucesso também vem despertando a atenção de usuários mal-intencionados, que propagam comentários e vídeos indesejados para se autopromoverem ou para disseminar links maliciosos que podem conter vírus e malwares. Visto que o YouTube atualmente oferece recursos limitados para bloquear spam, o volume dessas mensagens está impactando muitos usuários e proprietários de canais. Além da característica inerentemente online do problema, filtrar spam nos comentários do YouTube é uma tarefa que difere-se da tradicional filtragem de spam em emails, pois as mensagens costumam ser muito mais curtas e repletas de erros de digitação, gírias, símbolos e abreviações que podem dificultar a tarefa de classificação. Assim, nesta dissertação é apresentada a avaliação de desempenho obtido por métodos tradicionais de classificação online auxiliados por técnicas de normalização léxica e indexação semântica, quando aplicados na filtragem automática de comentários indesejados postados no YouTube. Foi avaliado também o desempenho do MDLText, um promissor método de classificação de texto baseado no princípio da descrição mais simples. A análise estatística dos resultados indica que os métodos MDLText, Passivo-Agressivo, Naïve Bayes, MDL e Gradiente Descendente Online obtiveram desempenhos equivalentes. Além disso, os resultados também indicam que o uso de técnicas de normalização léxica e indexação semântica são eficazes para atenuar os problemas de representação de texto e, consequentemente, aumentar o poder de predição dos métodos de classificação. Baseado nos resultados dos experimentos, foi proposto e desenvolvido o TubeSpam, uma ferramenta online para filtrar automaticamente comentários indesejados postados no YouTube.
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Källback, Winter William, and Tove Backman. "”I really hope you guys are enjoying this. Thank you so much for watching!” : En kvalitativ och kvantitativ studie av interaktionen mellan YouTubare och deras publik." Thesis, Umeå universitet, Institutionen för kultur- och medievetenskaper, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-119040.

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The overall purpose of this essay, “‘I really hope you guys are enjoying this. Thank you so much for watching!’ - a qualitative and quantitative study of the interaction between YouTubers and their audience", is to study how YouTubers that play and comment video games interact with their audience, what kind of response these YouTubers receive and the interaction between viewers and fans in these YouTubers comment sections. This essay also studies if there is a difference between the response female and male YouTubers receive. The study is based on theories about fan culture, participation culture, collective intelligence, feminism and gender. A quantitative content analysis has been used to analyze 600 comments on six YouTube videos uploaded by six different YouTubers. The purpose of the quantitative analysis was to see what content of the comments most often occur as well as if the comment showed a positive, negative or neutral view of the YouTuber. The result of the quantitative analysis was used as a base for a qualitative critical discourse analysis, which also studied how the YouTubers behaved in the videos. The results of this study showed that the YouTubers mostly received positive comments about their personalities and their YouTube channel. They mostly received negative comments about the way they play the game. The female YouTubers received more negative comments than the male YouTubers, who in turn received more positive comments. The study also showed that YouTubers interact by talking directly to the audience and looking into the camera, by using the word “we” when talking about how they play the game as if they are playing with the audience, by asking the audience questions and by answering comments that the YouTubers have received. The YouTubers engage their audience by using strong expressions and by playing the game during a livestream. The YouTubers’ fans engage and interact by showing appreciation of the YouTubers and defend the YouTubers when they receive negative comments in the comment section of their videos. These ways which YouTubers interact with and engage their audience can be seen as part of a discourse about interaction and engagement online.
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Books on the topic "YouTube commenters"

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Aguirre, Alwin. Qualitative Sentiment Analysis of YouTube Comments on Selected News Reports of a Unilateral Abrogation of a Peace Pact Between Two State Institutions. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529607925.

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Book chapters on the topic "YouTube commenters"

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Inwood, Olivia, and Michele Zappavigna. "The Marriages of Celebrity Politicians: A Social Semiotic Approach to How Commenters Affiliate Around YouTube Gossip Videos." In A Gossip Politic, 133–53. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15119-4_9.

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Alassad, Mustafa, Nitin Agarwal, and Muhammad Nihal Hussain. "Examining Intensive Groups in YouTube Commenter Networks." In Social, Cultural, and Behavioral Modeling, 224–33. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21741-9_23.

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Ksiazek, Thomas B., and Limor Peer. "User Comments And Civility On Youtube." In The Routledge Companion to Digital Journalism Studies, 244–52. London ; New York : Routledge, 2017.: Routledge, 2016. http://dx.doi.org/10.4324/9781315713793-25.

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Murthy, A. Amrita, Aman Abhay Choudhary, and R. Anita. "Ranking YouTube Videos Based on Comments Sentiment." In Data Intelligence and Cognitive Informatics, 517–29. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6460-1_40.

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Mulholland, Eleanor, Paul Mc Kevitt, Tom Lunney, and Karl-Michael Schneider. "Analysing Emotional Sentiment in People’s YouTube Channel Comments." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 181–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55834-9_21.

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Gao, Jiahui, Qijin Cheng, and Philip L. H. Yu. "Detecting Comments Showing Risk for Suicide in YouTube." In Proceedings of the Future Technologies Conference (FTC) 2018, 385–400. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02686-8_30.

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Das, Rama Krushna, Sweta Shree Dash, Kaberi Das, and Manisha Panda. "Detection of Spam in YouTube Comments Using Different Classifiers." In Advances in Intelligent Systems and Computing, 201–14. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1081-6_17.

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Swain, Debabrata, Monika Verma, Sayali Phadke, Shraddha Mantri, and Anirudha Kulkarni. "Video Categorization Based on Sentiment Analysis of YouTube Comments." In Machine Learning and Information Processing, 59–67. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4859-2_6.

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Maity, Devdeep, and Margot Racat. "The Role of Audience Comments in YouTube Vlogs: An Abstract." In Developments in Marketing Science: Proceedings of the Academy of Marketing Science, 551. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99181-8_178.

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Cunha, Alexandre Ashade Lassance, Melissa Carvalho Costa, and Marco Aurélio C. Pacheco. "Sentiment Analysis of YouTube Video Comments Using Deep Neural Networks." In Artificial Intelligence and Soft Computing, 561–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20912-4_51.

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Conference papers on the topic "YouTube commenters"

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Pradhan, Rahul. "Extracting Sentiments from YouTube Comments." In 2021 Sixth International Conference on Image Information Processing (ICIIP). IEEE, 2021. http://dx.doi.org/10.1109/iciip53038.2021.9702561.

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Al-Tamimi, Abdel-Karim, Ali Shatnawi, and Esraa Bani-Issa. "Arabic sentiment analysis of YouTube comments." In 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). IEEE, 2017. http://dx.doi.org/10.1109/aeect.2017.8257766.

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Sarakit, Phakhawat, Thanaruk Theeramunkong, Choochart Haruechaiyasak, and Manabu Okumura. "Classifying emotion in Thai youtube comments." In 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES). IEEE, 2015. http://dx.doi.org/10.1109/ictemsys.2015.7110808.

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Anagnostou, Antonios, Ioannis Mollas, and Grigorios Tsoumakas. "Hatebusters: A Web Application for Actively Reporting YouTube Hate Speech." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/841.

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Hatebusters is a web application for actively reporting YouTube hate speech, aiming to establish an online community of volunteer citizens. Hatebusters searches YouTube for videos with potentially hateful comments, scores their comments with a classifier trained on human-annotated data and presents users those comments with the highest probability of being hate speech. It also employs gamification elements, such as achievements and leaderboards, to drive user engagement.
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Loke, Cliff, and Anjan Pal. "DO COMMENTS ON YOUTUBE DIFFER ACROSS GENRE?" In International Conference On Interfaces and Human Computer Interaction 2019. IADIS Press, 2019. http://dx.doi.org/10.33965/ihci2019_201906l012.

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Zahir, Jihad, Youssef Mehdi Oukaja, and Hajar Mousannif. "Author Gender Identification from Arabic Youtube Comments." In 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, 2019. http://dx.doi.org/10.1109/sitis.2019.00109.

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Divesh, Gaurav Prasad, Gaurav Sharma, and Dinesh Kumar Vishwakarma. "Sentiment Analysis on Cryptocurrency using Youtube Comments." In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2022. http://dx.doi.org/10.1109/iccmc53470.2022.9753723.

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Poche, Elizabeth, Nishant Jha, Grant Williams, Jazmine Staten, Miles Vesper, and Anas Mahmoud. "Analyzing User Comments on YouTube Coding Tutorial Videos." In 2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC). IEEE, 2017. http://dx.doi.org/10.1109/icpc.2017.26.

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Serbanoiu, Andrei, and Traian Rebedea. "Relevance-Based Ranking of Video Comments on YouTube." In 2013 19th International Conference on Control Systems and Computer Science (CSCS). IEEE, 2013. http://dx.doi.org/10.1109/cscs.2013.87.

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Savigny, Julio, and Ayu Purwarianti. "Emotion classification on youtube comments using word embedding." In 2017 International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA). IEEE, 2017. http://dx.doi.org/10.1109/icaicta.2017.8090986.

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Reports on the topic "YouTube commenters"

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Shkodin, Andrey. Sets of exercises for education workers in the Far North. Science and Innovation Center Publishing House, April 2021. http://dx.doi.org/10.12731/shkodin.0418.15042021.

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Complexes of exercises for educational workers of the Far North is an electronic resource developed specifically for pedagogical workers living in the Far North. The selection and description of exercise complexes was developed on the basis of a study of the peculiarities of living in the Far North, common diseases characteristic of the inhabitants of the Far North and the peculiarities of the profession of a teacher. Access to the electronic resource is free, hosted on the google cloud service and youtube video hosting, contains video resources and comments on use. Available through a browser, no additional software required.
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