Showing 1 - 20 results of 288 for search '"Twitter"', query time: 0.07s Refine Results
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    An empirical evaluation of electronic annotation tools for Twitter data by Davy Weissenbacher, Karen O'Connor, Aiko T. Hiraki, Jin-Dong Kim, Graciela Gonzalez-Hernandez

    Published 2020-06-01
    “…Despite a growing number of natural language processing shared-tasks dedicated to the use of Twitter data, there is currently no ad-hoc annotation tool for the purpose. …”
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    Discursos e redes anticorrupção no Twitter: “Bolsonaro incorruptível” by Marina Alvarenga Botelho, Mariane Motta de Campos, Mayra Regina Coimbra, Deborah Luísa Vieira dos Santos

    Published 2022-11-01
    “…Diante dos desdobramentos da “CPI da Covid”, em 2021, a presente investigação visa debruçar-se sobre as conversações de usuários da rede Twitter acerca do tema anticorrupção, a partir da #bolsonaroincorruptível, hashtag que surgiu como reação a depoimentos que ligavam a figura de Jair Bolsonaro à corrupção no caso da compra de vacinas conta a Covid-19. …”
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    Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks by Kamaran H. Manguri, Rebaz N. Ramadhan, Pshko R. Mohammed Amin

    Published 2020-05-01
    Subjects: “…Sentiment analysis, COVID-19, Coronavirus, Social Media, Twitter, Python, Text Blob.…”
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    Election Prediction on Twitter: A Systematic Mapping Study by Asif Khan, Huaping Zhang, Nada Boudjellal, Arshad Ahmad, Jianyun Shang, Lin Dai, Bashir Hayat

    Published 2021-01-01
    “…Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. …”
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    Topic Detection and Tracking Techniques on Twitter: A Systematic Review by Meysam Asgari-Chenaghlu, Mohammad-Reza Feizi-Derakhshi, Leili Farzinvash, Mohammad-Ali Balafar, Cina Motamed

    Published 2021-01-01
    “…The importance of Twitter in this scope lies in its content and the behavior of its users. …”
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    Utilizing Structural Network Positions to Diversify People Recommendations on Twitter by Ekaterina Olshannikova, Erjon Skenderi, Thomas Olsson, Sami Koivunen, Jukka Huhtamäki

    Published 2022-01-01
    “…Social recommender systems, such as “Who to follow” on Twitter, utilize approaches that recommend friends of a friend or interest-wise similar people. …”
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    Twitter (X) and the Commercial Determinants of Health: Characterizing the Most Amplified, Influential, and Connected Voices Driving Twitter Discourse About Tobacco Regulatory Policy From September 2019 to July 2021 by Nathan A. Silver, Miao Feng, Elexis C. Kierstead, Hy Tran, Steven Binns, Sherry Emery, Barbara A. Schillo

    Published 2025-01-01
    “…Tobacco content on Twitter (X) generally opposes regulation. Although a near real-time data source of the public’s response to prominent events heightens the allure of extrapolating public sentiment from Twitter content, tobacco policy sentiment on the platform may be more indicative of industry-affiliated top users. …”
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    ANALISIS SENTIMEN DATA TWITTER TERKAIT CHATGPT MENGGUNAKAN ORANGE DATA MINING by Tri Yuli Pahtoni, Handaru Jati

    Published 2024-08-01
    “…Penelitian ini memberikan ilustrasi peran Twitter dalam menampung postingan pengguna Twitter terkait chatGPT. …”
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    Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload by Pei Li, Wei Li, Hui Wang, Xin Zhang

    Published 2014-01-01
    “…Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. …”
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    Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study by Ashwin Rao, Nazanin Sabri, Siyi Guo, Louiqa Raschid, Kristina Lerman

    Published 2025-02-01
    “…MethodsFor this observational study, we gathered a dataset of >539,000 original posts or reposts from 489 PHEs and 356 pseudoexperts on Twitter (subsequently rebranded X) from January 2020 to January 2021, along with the replies to the original posts from the PHEs. …”
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