Bibliografías temáticas / Twitter data analytics

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Literatura académica sobre el tema "Twitter data analytics"

Autor: Grafiati
Publicado: 10 de marzo de 2023
Última modificación: 28 de julio de 2025

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Artículos de revistas sobre el tema "Twitter data analytics"

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Gukanesh, A. V., G. Karthick Kumar, and K. Karthik Raja Kumar N. Saranya. "Twitter Data Analytics – Sentiment Analysis of An Election." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1600–1603. http://dx.doi.org/10.31142/ijtsrd11457.

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Negara, Edi Surya, Ria Andryani, and Prihambodo Hendro Saksono. "Analisis Data Twitter: Ekstraksi dan Analisis Data G eospasial." Jurnal INKOM 10, no. 1 (2016): 27. http://dx.doi.org/10.14203/j.inkom.433.

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Data geospasial pada media sosial Twitter dapat dimanfaatkan untuk mengetahui informasi spasial (lokasi) yang merupakan lokasi sumber munculnya persepsi publik terhadap sebuah isu di media sosial. Besarnya produksi data geospasial yang dihasilkan oleh Twitter memberikan peluang besar untuk dapat dimanfaatkan oleh berbagai pihak sehingga menghasilkan informasi yang lebih bernilai melalui proses Twitter Data Analytics. Proses pemanfaatan data geospasial Twitter dimulai dengan melakukan proses ekstraksi terhadap informasi spatial berupa titik koordinat pengguna Twitter. Titik koordinat pengguna T
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Hoeber, Orland, Larena Hoeber, Maha El Meseery, Kenneth Odoh, and Radhika Gopi. "Visual Twitter Analytics (Vista)." Online Information Review 40, no. 1 (2016): 25–41. http://dx.doi.org/10.1108/oir-02-2015-0067.

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Purpose – Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues. Design/methodology/approach – Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparkl
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Neetu, Anand *1 Tapas Kumar 2. "TWITTER ANALYTICS AND VISUALIZATION USING R." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 496–501. https://doi.org/10.5281/zenodo.829749.

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It is an era of Internet technology various social networking sites are gain popularity worldwide. The data generated from these sites are growing day by day .Social media plays a very important role in most of the people's life. People communicate freely and share their comments, ideas on numerous events on any of the various social media like Twitter, Google+, Facebook, etc. Their views are useful for generating solutions and creating awareness about several problems .Twitter is the micro blogging site that has become very well-liked all over the world. These days, there is a continuing tren
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A., V. Gukanesh, Karthick Kumar G., Karthik Raja Kumar K., and Saranya N. "Twitter Data Analytics - Sentiment Analysis of An Election." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 1600–1603. https://doi.org/10.31142/ijtsrd11457.

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Social media is used to analyze political campaigns, stock market, movies, medicines, agriculture etc. Twitter a microblogging website where users read and write millions of tweets on a variety of topics daily. This project attempts to analyze the sentiment of the people for the election candidates based upon the live opinions and emotions. The focus of our project is to assign the polarity to each tweet that is whether the user expresses a positive or negative opinion. With the tweets that are extracted, we try to find how frequent their emotions change. We are also trying to classify and dif
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P., Seetha*1 &. I. Sivaprasad Manivannan2. "INFERENCE ATTACK TWITTER USERS USING PUBLIC CLICK ANALYTICS AND TWITTER META DATA." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 4 (2019): 1–11. https://doi.org/10.5281/zenodo.2631120.

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<strong>Twitter is one of the most popular microblogging services, which is generally used to share news and updates through short messages restricted to 280 characters. However, its open nature and&nbsp; large&nbsp; user&nbsp; base&nbsp; are frequently exploited&nbsp; by&nbsp; automated spammers,&nbsp;&nbsp; content polluters,&nbsp; and other ill-intended users to commit&nbsp; various&nbsp; cyber crimes, such as cyberbullying, trolling, rumor dissemination, and stalking. Accordingly, a number of approaches have been proposed by researchers to Address these problems. However, most of these app
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Singh, Ravindra Kumar. "Effective Information Retrieval Framework for Twitter Data Analytics." International Journal of Information Retrieval Research 13, no. 1 (2023): 1–21. http://dx.doi.org/10.4018/ijirr.325798.

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The widespread adoption of opinion mining and sentiment analysis in higher cognitive processes encourages the need for real time processing of social media data to capture the insights about user's sentiment polarity, user's opinions, and current trends. In recent years, lots of studies were conducted around the processing of data to achieve higher accuracy. But reducing the time of processing still remained challenging. Later, big data technologies came into existence to solve these challenges but those have its own set of complexities along with having hardware deadweight on the system. The
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Abdullah, Yusuf, Asep Miftahuddin, Rispiaga Rispiaga, Deni Mulyana, and Isbandi Isbandi. "Leveraging Social Media Data Analytics for Tourism Marketing Policies in West Java, Indonesia." Jurnal Manajemen Pelayanan Publik 7, no. 1 (2023): 112. http://dx.doi.org/10.24198/jmpp.v7i1.45229.

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After the government failed to release updated data on the handling of COVID-19, the status of the pandemic in Indonesia has been declared to have ended and transitioned into an endemic disease. This policy has received various responses from the public, including the tourism sector. Therefore, the objective of this research is to gain a comprehensive understanding of the public perception regarding the issue of tourism during the endemic period by examining how people communicate through social media, particularly Twitter. The research method employed is the collection of Twitter conversation
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Et. al., Vedant Karmalkar,. "Twego Trending: Data Analytics Based Search Engine Using Elasticsearch." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 246–51. http://dx.doi.org/10.17762/turcomat.v12i1s.1764.

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Twitter monitoring enables firms to consider their market, stay on track of what is being said regarding their company and contenders, and uncover emerging market trends. Twego Trending is a platform where data will be viewed and structured by an automated procedure of analyzing and processing tweets data and classifying it into various hash statistics and visualizations. Implementing Twego forecasting analysis on Twitter data using various technologies may help businesses know how consumers talk about their product. Twitter has more than 340 million active users and almost 500 millions tweets
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Al-Ibrahim, Fatimah, and Zakarya A. Alzamil. "Big Data Contextual Analytics Study on Arabic Tweets Summarization." International Journal of Knowledge and Systems Science 10, no. 4 (2019): 18–34. http://dx.doi.org/10.4018/ijkss.2019100102.

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Twitter represents a source of information as well as a free space for people to express their opinions on diverse topics. The use of twitter is rapidly increasing and generates a massive amount of data from several types and forms, in which searching for relevant tweets in a specific topic is hard manually due to irrelevant tweets. There has been much research on English tweets for understanding context; however, in spite of the fact that the Twitter active Arabic users are over hundreds of millions, there are very limited studies that have investigated Arabic tweets to produce an automatic s
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Tesis sobre el tema "Twitter data analytics"

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Leis, Machín Angela 1974. "Studying depression through big data analytics on Twitter." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/671365.

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Mental disorders have become a major concern in public health, since they are one of the main causes of the overall disease burden worldwide. Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Language is one of the main tools on which mental health professionals base their understanding of human beings and their feelings, as it provides essential information for diagnosing and monitoring patients suffering from mental disorders. In parallel, social media platforms such as Twitter, allow us to observe the activity, though
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Carvalho, Eder José de. "Visual analytics of topics in twitter in connection with political debates." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-11092017-140904/.

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Social media channels such as Twitter and Facebook often contribute to disseminate initiatives that seek to inform and empower citizens concerned with government actions. On the other hand, certain actions and statements by governmental institutions, or parliament members and political journalists that appear on the conventional media tend to reverberate on the social media. This scenario produces a lot of textual data that can reveal relevant information on governmental actions and policies. Nonetheless, the target audience still lacks appropriate tools capable of supporting the acquisition,
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Carlos, Marcelo Aparecido. "Análise de surtos de doenças transmitidas pelo mosquito Aedes aegypti utilizando Big-Data Analytics e mensagens do Twitter." reponame:Repositório Institucional da UFABC, 2017.

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Orientador: Prof. Dr. Filipe Ieda Fazanaro<br>Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2017.<br>O uso do big-data aliado a técnicas de mineração de textos vem crescendo a cada ano em diversas áreas da ciência, especialmente na área da saúde, na medicina de precisão, em prontuários eletrônicos entre outros. A motivação desse trabalho parte da hipótese de que é possível usar conceitos de big-data para analisar grandes quantidades de dados sobre as doenças da dengue, chikungunya e zika vírus, para monitorar e antecipar informaçõe
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Gröbe, Mathias. "Konzeption und Entwicklung eines automatisierten Workflows zur geovisuellen Analyse von georeferenzierten Textdaten(strömen) / Microblogging Content." Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-210672.

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Die vorliegende Masterarbeit behandelt den Entwurf und die exemplarische Umsetzung eines Arbeitsablaufs zur Aufbereitung von georeferenziertem Microblogging Content. Als beispielhafte Datenquelle wurde Twitter herangezogen. Darauf basierend, wurden Überlegungen angestellt, welche Arbeitsschritte nötig und mit welchen Mitteln sie am besten realisiert werden können. Dabei zeigte sich, dass eine ganze Reihe von Bausteinen aus dem Bereich des Data Mining und des Text Mining für eine Pipeline bereits vorhanden sind und diese zum Teil nur noch mit den richtigen Einstellungen aneinandergereiht werden
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Hammoud, Khodor. "Trust in online data : privacy in text, and semantic-based author verification in micro-messages." Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5203.

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De nombreux problèmes émanent de la diffusion et l'utilisation des données sur les réseaux sociaux. Il est nécessaire de promouvoir la confiance sur les plateformes sociales, quant au partage et l’utilisation des données. Les données en ligne sont principalement sous forme textuelle, ce qui pose des problèmes aux solutions d'automatisation en raison de la richesse du langage naturel. De plus, l'utilisation des micro-messages comme principal moyen de communication sur les médias sociaux rend le problème beaucoup plus difficile en raison de la rareté des fonctionnalités à analyser par corps de t
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Willis, Margaret Mary. "Interpreting "Big Data": Rock Star Expertise, Analytical Distance, and Self-Quantification." Thesis, Boston College, 2015. http://hdl.handle.net/2345/bc-ir:104932.

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Thesis advisor: Natalia Sarkisian<br>The recent proliferation of technologies to collect and analyze “Big Data” has changed the research landscape, making it easier for some to use unprecedented amounts of real-time data to guide decisions and build ‘knowledge.’ In the three articles of this dissertation, I examine what these changes reveal about the nature of expertise and the position of the researcher. In the first article, “Monopoly or Generosity? ‘Rock Stars’ of Big Data, Data Democrats, and the Role of Technologies in Systems of Expertise,” I challenge the claims of recent scholarship, w
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SHARMA, HARSHITA. "SOFT COMPUTING FOR RUMOUR ANALYTICS ON BENCHMARK TWITTER DATA." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16701.

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As social media is a fertile ground for origin and spread of rumours, it is imperative to detect and deter rumours. Various computational models that encompass elements of learning have been studied on benchmark datasets for rumour resolution with four individual tasks, namely rumour detection, tracking, stance and veracity classification. Quick rumour detection during initial propagation phase is desirable for subsequent veracity and stance assessment. This research presents the use of adaptive and heuristic optimization to select a near-optimal set of input variables that would m
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Mullassery, Mohanan Shalini. "Social media data analytics for the NSW construction industry : a study on Twitter." Thesis, 2022. http://hdl.handle.net/1959.7/uws:67977.

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The primary aim of this dissertation is to explore the social interaction and relationship of people within the NSW construction industry through social media data analytics. The research objective is to perform social media data analytics through Twitter and explore the social interactions between different stakeholders in the construction industry to understand the real-world situations better. The data analytics was performed on Twitter tweets, retweets, and hashtags that were collected from four clusters on construction stakeholders in NSW, namely construction workers, companies, media, an
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"Event Analytics on Social Media: Challenges and Solutions." Doctoral diss., 2014. http://hdl.handle.net/2286/R.I.27510.

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abstract: Social media platforms such as Twitter, Facebook, and blogs have emerged as valuable - in fact, the de facto - virtual town halls for people to discover, report, share and communicate with others about various types of events. These events range from widely-known events such as the U.S Presidential debate to smaller scale, local events such as a local Halloween block party. During these events, we often witness a large amount of commentary contributed by crowds on social media. This burst of social media responses surges with the "second-screen" behavior and greatly enriches th
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Zaza, Imad. "Ontological knowledge-base for railway control system and analytical data platform for Twitter." Doctoral thesis, 2018. http://hdl.handle.net/2158/1126141.

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The scope of this thesis is Railway signaling and Social Media Analysis. In particular, with regard to the first theme, it has been conducted an investigation into the domain of railway signaling, it has been defined the objectives of the research or the development and verification of an ontological model for the management of railway signaling and finally having discussed results and inefficiencies. As far as for SMA it have been studied and discussed the state of the art of SMA tools including Twitter Vigilance developed within the DISIT laboratory at the University of Florence. It has been
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Libros sobre el tema "Twitter data analytics"

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Kumar, Shamanth, Fred Morstatter, and Huan Liu. Twitter Data Analytics. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9372-3.

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Bruns, Axel. Twitter Data Analytics. Emerald Publishing Limited, 2014.

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Liu, Huan, Shamanth Kumar, and Fred Morstatter. Twitter Data Analytics. Springer, 2013.

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Twitter Data Analytics. Springer-Verlag New York Inc., 2013.

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Liu, Huan, Shamanth Kumar, and Fred Morstatter. Twitter Data Analytics. Springer London, Limited, 2013.

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Chatterjee, Siddhartha, and Michal Krystyanczuk. Python Social Media Analytics: Analyze and visualize data from Twitter, YouTube, GitHub, and more. Packt Publishing - ebooks Account, 2017.

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Capítulos de libros sobre el tema "Twitter data analytics"

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Kumar, Shamanth, Fred Morstatter, and Huan Liu. "Crawling Twitter Data." In Twitter Data Analytics. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9372-3_2.

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Kumar, Shamanth, Fred Morstatter, and Huan Liu. "Storing Twitter Data." In Twitter Data Analytics. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9372-3_3.

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Kumar, Shamanth, Fred Morstatter, and Huan Liu. "Analyzing Twitter Data." In Twitter Data Analytics. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9372-3_4.

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Kumar, Shamanth, Fred Morstatter, and Huan Liu. "Visualizing Twitter Data." In Twitter Data Analytics. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9372-3_5.

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Kumar, Shamanth, Fred Morstatter, and Huan Liu. "Introduction." In Twitter Data Analytics. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9372-3_1.

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Garg, Yogesh, and Niladri Chatterjee. "Sentiment Analysis of Twitter Feeds." In Big Data Analytics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13820-6_3.

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Mehndiratta, Pulkit, Shelly Sachdeva, Pankaj Sachdeva, and Yatin Sehgal. "Elections Again, Twitter May Help!!! A Large Scale Study for Predicting Election Results Using Twitter." In Big Data Analytics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13820-6_11.

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Pradyumn, Mudit, Akshat Kapoor, and Nasseh Tabrizi. "Big Data Analytics on Twitter." In Big Data – BigData 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94301-5_26.

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Bhargava, Mudit, Pulkit Mehndiratta, and Krishna Asawa. "Stylometric Analysis for Authorship Attribution on Twitter." In Big Data Analytics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03689-2_3.

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Jain, Mona, S. Rajyalakshmi, Rudra M. Tripathy, and Amitabha Bagchi. "Temporal Analysis of User Behavior and Topic Evolution on Twitter." In Big Data Analytics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03689-2_2.

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Actas de conferencias sobre el tema "Twitter data analytics"

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Atqa Adzkia Zaldi, Muhammad, Opim Salim Sitompul, Erna Budhiarti Nababan, and Andrian Crispin. "Twitter Data Extraction for Food and Beverage Business Analytics." In 2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA). IEEE, 2024. https://doi.org/10.1109/ictiia61827.2024.10761920.

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Sandhi, Mahammad Idrish I., M. Amina Begum, Rahul Jalinder Jadhav, Jnaneshwar Pai Maroor, Ankushavali MD, and Venkateshmurthy B. S. "DeepSentiment: Unlocking Insights from Twitter Data with Advanced Deep Learning Analytics." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10867110.

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Abishethvarman, Vadivel, Kuhaneswaran Banujan, Ashansa Wijeratne, Ravikumar Nirubikaa, Samantha Kumara, and Senthan Prasanth. "Unveiling Public Perception of ChatGPT Through Twitter Data and Machine Learning." In 2023 4th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2023. http://dx.doi.org/10.1109/icdabi60145.2023.10629351.

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Firmansyah, Muhammad Hanif, Niken Dwi Wahyu Cahyani, and Satria Akbar Mugitama. "Duplicate Account Detection on Instagram, Twitter, and YouTube Using Deep Learning." In 2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics (ICoABCD). IEEE, 2024. http://dx.doi.org/10.1109/icoabcd63526.2024.10704438.

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Kumar, Durgesh, and Rajendra Kumar Dwivedi. "Expression of Concern for: Big Data Analytics on Twitter Sentiments in Cloud Environment: A Review." In 2023 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2023. http://dx.doi.org/10.1109/icicat57735.2023.10703712.

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Tao, Ke, Claudia Hauff, Geert-Jan Houben, Fabian Abel, and Guido Wachsmuth. "Facilitating Twitter data analytics: Platform, language and functionality." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004259.

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Shah, Syed Attique, Sadok Ben Yahia, Keegan McBride, Akhtar Jamil, and Dirk Draheim. "Twitter Streaming Data Analytics for Disaster Alerts." In 2021 2nd International Informatics and Software Engineering Conference (IISEC). IEEE, 2021. http://dx.doi.org/10.1109/iisec54230.2021.9672370.

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Alzahrani, Sabah M. "Big Data Analytics Tools: Twitter API and Spark." In 2021 International Conference of Women in Data Science at Taif University (WiDSTaif ). IEEE, 2021. http://dx.doi.org/10.1109/widstaif52235.2021.9430205.

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Aziz, Khadija, Dounia Zaidouni, and Mostafa Bellafkih. "Social Network Analytics: Natural Disaster Analysis Through Twitter." In 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2019. http://dx.doi.org/10.1109/icds47004.2019.8942337.

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Hurayn, Safa, Aditya M. Shetty, Srujan Vasudevrao Deshpande, Vaibhav Gupta, U. Ananthanagu, and Assistant Professor. "Analysis of Mental Illness using Twitter Data." In 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS). IEEE, 2021. http://dx.doi.org/10.1109/fabs52071.2021.9702565.

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Informes sobre el tema "Twitter data analytics"

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Vizmanos, Jana Flor, Jose Ramon Albert, Mika Muñoz, et al. Addressing Data Gaps with Innovative Data Sources. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.55.

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With the advent of digital transformation, information and communications technology innovations have also led to a "data revolution" wherein more data is being captured, produced, stored, accessed, analyzed, archived, and reanalyzed at an exponential pace. An examination of new data sources, including big data and crowd-sourced data, can complement traditional sources of statistics and unlock insights that can ultimately lead to interventions for better outcomes by informing policies and actions toward attaining robust, sustainable, and inclusive development. This study will examine PIDS webs
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