Gotowe bibliografie tematyczne / X (formely Twitter)

Spis treści

  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Części książek
  4. Streszczenia konferencji
  5. Raporty organizacyjne

Gotowa bibliografia na temat „X (formely Twitter)”

Autor: Grafiati
Data publikacji: 11 stycznia 2025

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Artykuły w czasopismach na temat "X (formely Twitter)"

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Koloski, Natasha, Kerith Duncanson, Shanthi Ann Ramanathan, Melanie Rao, Gerald Holtmann, and Nicholas J. Talley. "What impact has the Centre of Research Excellence in Digestive Health made in the field of gastrointestinal health in Australia and internationally? Study protocol for impact evaluation using the FAIT framework." BMJ Open 14, no. 3 (2024): e076839. http://dx.doi.org/10.1136/bmjopen-2023-076839.

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IntroductionThe need for public research funding to be more accountable and demonstrate impact beyond typical academic outputs is increasing. This is particularly challenging and the science behind this form of research is in its infancy when applied to collaborative research funding such as that provided by the Australian National Health and Medical Research Council to the Centre for Research Excellence in Digestive Health (CRE-DH).Methods and analysisIn this paper, we describe the protocol for applying the Framework to Assess the Impact from Translational health research to the CRE-DH. The s
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Olanrewaju, Ayishat Sandra. "Audience’s Perception of X (Formerly Twitter) Trends as a Tool for the Nigerian Public Sphere." American Journal of Communication 6, no. 2 (2024): 42–64. http://dx.doi.org/10.47672/ajc.2216.

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Purpose: Unlike how we previously knew it to be, the public sphere has evolved to include digital spaces, making them metamorphose into digital public spheres. These digital spaces are more open, accessible, and void of certain bottlenecks. It is, therefore, now impossible to describe a public sphere without mentioning digital spaces. As such, this study aims to find out the audience's perception of X (formerly Twitter) trends as a tool for the Nigerian public sphere. Materials and Methods: The survey research method was used to obtain responses from 200 respondents, using the purposive sampli
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Kimani, Joshua, Anthony Karanjah, and Pius Kihara. "Sentiment Classification of Safaricom PLC Social Media Sentiments on X(Formerly Twitter)." Asian Journal of Probability and Statistics 26, no. 6 (2024): 31–40. http://dx.doi.org/10.9734/ajpas/2024/v26i6622.

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In today's digital era, social media plays a pivotal role in shaping public sentiment, particularly in the financial domain. This study focuses on sentiment analysis of social media discussions, specifically tweets discussing Safaricom PLC on X (formerly Twitter), leveraging Natural Language Processing (NLP) techniques. By meticulously collecting, cleaning, and analyzing data, valuable insights into the sentiment landscape surrounding Safaricom PLC during a significant period were obtained. The sentiment analysis, conducted using the VADER lexicon, categorized sentiments into positive, negativ
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Makaruke, Kumbirai, and Akim Munthali. "Analysis of X (Formerly Twitter) Users’ Perceptions towards the Election of Jolidee Matongo as Mayor of Johannesburg." International Journal of Research and Innovation in Social Science VIII, no. IV (2024): 502–9. http://dx.doi.org/10.47772/ijriss.2024.804039.

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This paper presents a study on the analysis of X (formerly Twitter) users’ perceptions towards the election of Jolidee Matongo as mayor of Johannesburg, South Africa in 2021. This election was significant because it was the first time that a person of Zimbabwean descent had been elected mayor of Johannesburg. Given the vast amount of user-generated content on Twitter, analysing tweets can serve as a valuable proxy for capturing public opinion. However, to date, there appears to be a notable research gap concerning the wide adoption of tweet analysis for understanding public opinion. The study
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Elmas, Tuğrulcan, Mathis Randl, and Youssef Attia. "#TeamFollowBack: Detection & Analysis of Follow Back Accounts on Social Media." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 381–93. http://dx.doi.org/10.1609/icwsm.v18i1.31321.

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Follow back accounts inflate their follower counts by engaging in reciprocal followings. Such accounts manipulate the public and the algorithms by appearing more popular than they really are. Despite their potential harm, no studies have analyzed such accounts at scale. In this study, we present the first large-scale analysis of follow back accounts. We formally define follow back accounts and employ a honeypot approach to collect a dataset of such accounts on X (formerly Twitter). We discover and describe 12 communities of follow back accounts from 12 different countries, some of which exhibi
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Arnett, Autumn A. "Is AI making faculty's jobs worse?" Enrollment Management Report 28, no. 8 (2024): 5–7. http://dx.doi.org/10.1002/emt.31310.

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Rua Mae Williams, an assistant professor in the User Experience Design program at Purdue University, recently shared on X (the network formerly known as Twitter) the ways implicit bias about who and what intelligence looks and sounds like inspires greater use of artificial intelligence platforms, such as ChatGPT, among students who don’t feel they measure up.
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Arnett, Autumn A. "Is AI making faculty's jobs worse?" Successful Registrar 24, no. 9 (2024): 5–7. http://dx.doi.org/10.1002/tsr.31367.

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Rua Mae Williams, an assistant professor in the User Experience Design program at Purdue University, recently shared on X (the network formerly known as Twitter) the ways implicit bias about who and what intelligence looks and sounds like inspires greater use of artificial intelligence platforms, such as ChatGPT, among students who don’t feel they measure up.
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Samalis, Alexandros, Alexandros Z. Spyropoulos, Georgios C. Makris, et al. "Data Journalism and Network Theory: A Study of Political Communication through X (Formerly Twitter) Interactions." Journalism and Media 4, no. 4 (2023): 1141–68. http://dx.doi.org/10.3390/journalmedia4040073.

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This study investigates the research questions: “How do political connections within Greece’s governing party evolve, and what underlying patterns and dynamics are revealed through a network analysis of interactions on X (formerly Twitter)?” To address these questions, data were collected from X, focusing on following, retweeting, and mentioning activities among the politicians within the governing party. The interactions were meticulously analysed using tools derived from Network Theory in mathematics, including in and out-strength centrality, hubs and authorities centralities, and in and out
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Jahanbin, Kia, Mohammad Jokar, and Vahid Rahmanian. "Using X Social Networks (formerly Twitter) and web news mining to predict the measles outbreak." Asian Pacific Journal of Tropical Medicine 17, no. 4 (2024): 188–90. http://dx.doi.org/10.4103/apjtm.apjtm_194_24.

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Yum, Seungil. "Capturing human response to Winter Storm Frankie based on X (formerly known as Twitter) data." Journal of Emergency Management 22, no. 6 (2024): 611–19. https://doi.org/10.5055/jem.0827.

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This study delves into how people responded to Winter Storm Frankie in the United States based on X (formerly known as Twitter®) data according to a multitude of regions, periods, sociodemographic characteristics, census regions, and geographical scales. This study finds that people actively respond to natural disasters on X during the winter storm week. Specifically, the highest number of keywords during the winter storm week is 1.6 times greater than the second-highest number of keywords during the prewinter storm week. Second, the spatial distribution of tweets exhibits significant fluctuat
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Rozprawy doktorskie na temat "X (formely Twitter)"

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Fraga, Netto Juliana Regina. "Rôle des signaux faibles dans la prise de forme des discours : étude du réseau social X (ex-Twitter)." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCH019.

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La présente recherche développe, à travers la plateforme numérique COCKTAIL, une analyse de la circulation des discours sur X (ex-Twitter) afin de détecter les tendances et les signaux faibles. La thèse s'appuie sur une méthode pluridisciplinaire, utilisant des algorithmes, pour construire un corpus de tweets. Le but des analyses quantitatives et qualitatives est de savoir comment les signaux faibles peuvent être identifiés dans la circulation des discours sur X. D’un point de vue théorique, le travail repose sur la question de la construction du sens et de savoir comment les signaux faibles é
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Części książek na temat "X (formely Twitter)"

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Akshay, B. R., Sini Raj Pulari, T. S. Murugesh, and Shriram K. Vasudevan. "Dive into X (formerly Twitter's) emotions using oneAPI." In Machine Learning. CRC Press, 2024. http://dx.doi.org/10.1201/9781032676685-21.

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Shah, Hurmat Ali, and Mowafa Househ. "Social and Web Data Framework for Understanding Loneliness." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240333.

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Loneliness can be studied through social media and web data to gain insights into the dynamic phenomenon. In this paper, we present our proposed framework through a case study on how to deploy various social media and online data sources to study loneliness comprehensively. The framework is important to understand loneliness from data perspective available online and to complement the theoretical and psychosocial understanding of loneliness. The data on loneliness is gathered through surveys and self-reporting mechanisms. This requires complementing the dynamic and vast data available on the web and through social media. Our results found that tools like Google Trend and News Analysis can give the starting point to explore loneliness in a particular region. Tools like X (formerly Twitter), Reddit, and other social media can give behavioral data on loneliness which can be analyzed through sentiment analysis and other social intelligence analysis. The framework’s utility lies in its potential to inform policies, interventions, and initiatives addressing loneliness through data. However, it is crucial to acknowledge limitations, such as data availability, biases in user-generated content, and ethical considerations.
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Ecker-Ehrhardt, Matthias. "The Digital Adaptation of International Bureaucracies." In The Oxford Handbook of Digital Diplomacy. Oxford University Press, 2024. http://dx.doi.org/10.1093/oxfordhb/9780192859198.013.19.

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Abstract International organizations’ bureaucracies are important facilitators and authorities of global governance that increasingly use digital means of communication—such as websites, email, social media, or mobile apps—to address their audiences directly. How do they deal with the opportunities and challenges of such ‘digital diplomacy’? What are the consequences of digital diplomacy regarding the content communicated by international bureaucracies and beyond? This chapter attempts to demonstrate the significance of recent research for providing conclusive answers to these questions as well as to demonstrate the need for further exploration. It first illustrates the current practices of international bureaucracies’ digital diplomacy by focusing on social media such as X (formerly known as Twitter) and Facebook in a quantitative perspective, illustrated with recent examples of their use. Second it discusses motivational factors and conducive conditions that help to explain why international bureaucracies have turned to, and increasingly employ, means of digital diplomacy. While the first and second parts of the chapter focus mainly on the opportunities of digital diplomacy, the third part goes full circle by turning to a couple of its challenges—organized hypocrisy, pluralization, acceleration, selectivity, personalization, and polarization—of which little is yet known, but that deserve more attention by scholars, practitioners, and stakeholders of global governance alike.
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De Pascale, Stefano, Edoardo Cavirani, and Stefania Marzo. "Homophobic hate speech in Italian tweets: Contextual cues of offensiveness." In An Investigation of Hate Speech in Italian: Use, Identification, and Perception. Helsinki University Press, 2024. https://doi.org/10.33134/hup-27-5.

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Social media channels have become omnipresent tools for communicating, sharing knowledge, and establishing communities, but also places where all sorts of hate speech comes to the surface. This chapter contributes to the body of work on online hate speech towards sexual orientation minorities and takes some initial steps towards a quantitative variationist sociolinguistic study of homophobic language in Italian social media. By exploring the different lexicalisations (i.e. near-synonyms) available in Italian for expressing or referring to the concept of "homosexual man" on X (formerly known as Twitter), this study provides both a descriptive and a methodological contribution. For this purpose, we created a dataset of 3000 manually annotated tweets in which at least one of the 33 lexicalisations of the concept "homosexual man" was recorded, and which we annotated for linguistic and stylistic factors that favour the perception of an expression as offensive. Our study shows that the interaction of lexical variation and explicit and implicit contextual cues, such as irony and dialect use, is significant in determining the degree of offensiveness of tweets. Furthermore, this study provides further evidence in favour of using social media as a laboratory for mapping language variation on a large scale, and for reflecting on the refinement of semi-automatic annotation of linguistic, stylistic, and social variables in written social media language.
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Rinceanu, Johanna. "The Changing Role of Internet Service Providers." In Criminalizing Intimate Image Abuse. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198877813.003.0018.

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Abstract This chapter addresses the regulatory framework that governs unlawful online content, including harassment, racism, extremism, and other illegal online content, with a special focus on cyberviolence and online ‘hate speech’—including non-consensual nudity sharing (commonly referred to as revenge porn)—against women. In addition to independent efforts undertaken by Internet Service Providers to combat online criminality by means of self-imposed Community Guidelines or Terms of Use, regulations have been introduced in order to more effectively combat hate and incitement as well as extremism on the Internet. In this respect, this chapter provides an overview on international conventions and action plans, recommendations, resolutions, EU directives, guidelines, and regulations––particularly on the recent enactment of the EU’s Digital Service Act (DSA)––as well as on Germany’s Act to Improve Enforcement of the Law in Social Networks (Network Enforcement Act, NetzDG), as an example of a national regulatory model. NetzDG requires Internet Service Providers with more than two million registered users in Germany (e.g. those on the scale of Google, Facebook, YouTube, X (formerly, Twitter), and Instagram) to set up a transparent complaints management system as private actors in order to efficiently handle complaints about illegal content as defined in NetzDG. The Act imposes on Internet Service Providers the responsibility of balancing human rights and fundamental freedoms against each other, thereby changing the role of these private actors—against their will—and making them gatekeepers at the threshold of fundamental and human rights.
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Streszczenia konferencji na temat "X (formely Twitter)"

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Omer Fatah, Yahya, Rebar Kuran Mustafa, and Baqr Dawd Hussein. "Employing Media Discourse in Developing Digital Diplomacy/Twitter tweets as an example: Descriptive Analytical Study." In Digital Media Effects on Society Security Under Domestic and International Laws. Sulaimani Polytechnic University, 2024. http://dx.doi.org/10.24017/dmedialaw24.06.

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This research aims to know the nature of the use of media discourse as an effective tool in developing digital diplomacy. This study is based on descriptive studies, using a survey approach in the form of a quantitative analytical survey to analyze the contents of media discourse on the pages of social media sites of the political elite in Iraq, which is represented by (published tweets By Iraqi personalities), as a deliberate, non-random sample in the period (10-1-2023) to (12-31-2023) on their official pages in the (X) network (formerly Twitter, which is used in digital diplomacy. The study
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Gupta, Vanshaj, Jaydeep Patel, Safa Shubbar, and Kambiz Ghazinour. "COVBERT: Enhancing Sentiment Analysis Accuracy in COVID-19 X Data through Customized BERT." In 4th International Conference on AI, Machine Learning and Applications. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140212.

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In a time when social media information is a valuable resource for gaining insights, the COVID-19 pandemic has released a flood of public sentiment, abundant with unstructured text data. This paper introduces CovBERT, a novel adaptation of the BERT model, specifically honed for the nuanced analysis of COVID-19-related discourse on X (formerly Twitter). CovBERT stands out by incorporating a bespoke vocabulary, meticulously curated from pandemic-centric tweets, resulting in a remarkable leap in sentiment analysis accuracy—from the baseline 72\% to an impressive 78.64\%. This paper not only prese
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Silva, Márcio, Marcelo M. R. Araújo, Carlos H. G. Ferreira, et al. "Campanha Eleitoral Legal: Detecção de Propaganda Eleitoral e Ações Coordenadas de Campanha." In Anais Estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/webmedia_estendido.2024.243924.

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Spreading electoral propaganda using Online Social Networks (OSNs) during elections is an important problem and novel approaches are necessary to mitigate its effects. The lack of automatic electoral propaganda detection supports candidates which makes true digital podiums have emerged for candidates to spread their ideas, fight opponents, and ask for votes during the pre-electoral period. In Brazil, it is prohibited by law to declare candidacy in a political election and to make any (explicit or implicit) request to vote ahead of time. In this context, this work presents a system named Campan
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Raporty organizacyjne na temat "X (formely Twitter)"

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Graham, Timothy, and Katherine M. FitzGerald. Bots, Fake News and Election Conspiracies: Disinformation During the Republican Primary Debate and the Trump Interview. Queensland University of Technology, 2023. http://dx.doi.org/10.5204/rep.eprints.242533.

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We used Alexandria Digital, a world leading disinformation detection technology, to analyse almost a million posts on X (formerly known as Twitter) and Reddit comments during the first Republican primary debate and counterprogrammed Tucker Carlson and Donald Trump interview on the 23rd of August. What we did: • Collected 949,259 posts from the platform X, formerly known as Twitter. These posts were collected if they contained one of 11 relevant hashtags or keywords and were posted between 8:45pm and 11:15pm EST on 23rd August 2023. • Collected 20,549 comments from two separate Reddit threads.
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