Добірка наукової літератури з теми "YouTube commenters"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "YouTube commenters".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "YouTube commenters"
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.
Повний текст джерелаPerrino, 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.
Повний текст джерелаBodrunova, 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.
Повний текст джерелаThelwall, 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.
Повний текст джерелаKoven, 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.
Повний текст джерелаTovares, 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.
Повний текст джерелаRaby, 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.
Повний текст джерелаTeng, 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.
Повний текст джерелаPihlaja, 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.
Повний текст джерелаBodrunova, 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.
Повний текст джерелаДисертації з теми "YouTube commenters"
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.
Повний текст джерелаSociala 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.
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.
Повний текст джерелаZhang, Leticia Tian. "Understanding danmu: interaction, learning and multimodality in fan video comments." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/669267.
Повний текст джерелаCuando 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.
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.
Повний текст джерелаThis 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.
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.
Повний текст джерелаThis 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.
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.
Повний текст джерелаSociala 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.
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.
Повний текст джерелаThe 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.
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.
Повний текст джерелаDenna 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.
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.
Повний текст джерелаApproved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-10-03T19:07:11Z (GMT) No. of bitstreams: 1 ALBERTO_Tulio_2017.pdf: 2422402 bytes, checksum: 127bff2089f3d274b1abaa58c3d32578 (MD5)
Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-10-03T19:07:27Z (GMT) No. of bitstreams: 1 ALBERTO_Tulio_2017.pdf: 2422402 bytes, checksum: 127bff2089f3d274b1abaa58c3d32578 (MD5)
Made available in DSpace on 2017-10-03T19:07:37Z (GMT). No. of bitstreams: 1 ALBERTO_Tulio_2017.pdf: 2422402 bytes, checksum: 127bff2089f3d274b1abaa58c3d32578 (MD5) Previous issue date: 2017-02-03
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.
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.
Повний текст джерелаКниги з теми "YouTube commenters"
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.
Повний текст джерелаЧастини книг з теми "YouTube commenters"
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.
Повний текст джерелаAlassad, 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.
Повний текст джерелаKsiazek, 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.
Повний текст джерелаMurthy, 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.
Повний текст джерелаMulholland, 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.
Повний текст джерелаGao, 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.
Повний текст джерелаDas, 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.
Повний текст джерелаSwain, 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.
Повний текст джерелаMaity, 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.
Повний текст джерелаCunha, 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.
Повний текст джерелаТези доповідей конференцій з теми "YouTube commenters"
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.
Повний текст джерелаAl-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.
Повний текст джерелаSarakit, 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.
Повний текст джерелаAnagnostou, 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.
Повний текст джерелаLoke, 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.
Повний текст джерелаZahir, 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.
Повний текст джерелаDivesh, 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.
Повний текст джерелаPoche, 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.
Повний текст джерелаSerbanoiu, 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.
Повний текст джерелаSavigny, 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.
Повний текст джерелаЗвіти організацій з теми "YouTube commenters"
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.
Повний текст джерела