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Добірка наукової літератури з теми "TWITTER ANALYTICS"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "TWITTER ANALYTICS"

1

Goonetilleke, Oshini, Timos Sellis, Xiuzhen Zhang, and Saket Sathe. "Twitter analytics." ACM SIGKDD Explorations Newsletter 16, no. 1 (2014): 11–20. http://dx.doi.org/10.1145/2674026.2674029.

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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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Анотація:

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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3

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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Zhang, Min, Feng-Ru Sheu, and Yin Zhang. "Understanding Twitter use by major LIS professional organisations in the United States." Journal of Information Science 44, no. 2 (2017): 165–83. http://dx.doi.org/10.1177/0165551516687701.

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Although Twitter has been widely adopted by professional organisations, there has been a lack of understanding and research on its utilisation. This article presents a study that looks into how five major library and information science (LIS) professional organisations in the United States use Twitter, including the American Library Association (ALA), Special Libraries Association (SLA), Association for Library and Information Science Education (ALISE), Association for Information Science and Technology (ASIS&T) and the iSchools. Specifically explored are the characteristics of Twitter usa

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Kota, Venkata Krishna, Venkateswarlu Naik B, and Vasudeva Rao Prasadula. "Smart City Service Monitoring Using Twitter Analytics." International Journal of Scientific and Research Publications (IJSRP) 9, no. 8 (2019): p92136. http://dx.doi.org/10.29322/ijsrp.9.08.2019.p92136.

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Razis, Gerasimos, Georgios Theofilou, and Ioannis Anagnostopoulos. "Latent Twitter Image Information for Social Analytics." Information 12, no. 2 (2021): 49. http://dx.doi.org/10.3390/info12020049.

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Анотація:

The appearance of images in social messages is continuously increasing, along with user engagement with that type of content. Analysis of social images can provide valuable latent information, often not present in the social posts. In that direction, a framework is proposed exploiting latent information from Twitter images, by leveraging the Google Cloud Vision API platform, aiming at enriching social analytics with semantics and hidden textual information. As validated by our experiments, social analytics can be further enriched by considering the combination of user-generated content, latent

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Alathur, Sreejith, and Rajesh Pai. "Social Media Games: Insights from Twitter Analytics." International Journal of Web Based Communities 16, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijwbc.2020.10026216.

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Pai, Rajesh R., and Sreejith Alathur. "Social media games: insights from Twitter analytics." International Journal of Web Based Communities 16, no. 1 (2020): 34. http://dx.doi.org/10.1504/ijwbc.2020.105127.

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Pai, Rajesh R., and Sreejith Alathur. "Assessing mobile health applications with twitter analytics." International Journal of Medical Informatics 113 (May 2018): 72–84. http://dx.doi.org/10.1016/j.ijmedinf.2018.02.016.

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Drescher, Larissa S., Carola Grebitus, and Jutta Roosen. "Exploring Food Consumption Trends on Twitter with Social Media Analytics: The Example of #Veganuary." EuroChoices 22, no. 2 (2023): 45–52. http://dx.doi.org/10.1111/1746-692x.12403.

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Анотація:

SummaryUsing the example of the hashtag #veganuary, a neologism of vegan and January, on Twitter with over 52,000 tweets from 2022, this article shows how Social Media Analytics can provide valuable insights into timing, volume and sentiment within any emerging (consumer) trend. Social Media Analytics is increasingly being used for the analysis of Social Media data. Whether consumers, politicians or entrepreneurs, all stakeholders in the food value chain are present on Social Media and talk about various trends in food and agriculture. In the form of an overview article, this contribution uses

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Дисертації з теми "TWITTER 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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3

Haraldsson, Daniel. "Marknadsföring på Twitter : Vilken dag och tidpunkt är optimal?" Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11517.

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Twitter and social media has a big impact on the modern society. Companies uses Twitter and other social Medias as marketing channels to reach multiple customers. The purpose of this research was to get an insight on what day and time that businesses get the most respondents on Twitter. The focus area is the social media Twitter and the way to use the 140 characters available. Efficiency and profitability are key factors for business firms so if they know when the prospective and existing customers are most receptive to a message you can obtain an advantage which may be important in the future

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Mahendiran, Aravindan. "Automated Vocabulary Building for Characterizing and Forecasting Elections using Social Media Analytics." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/25430.

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Анотація:

Twitter has become a popular data source in the recent decade and garnered a significant amount of attention as a surrogate data source for many important forecasting problems. Strong correlations have been observed between Twitter indicators and real-world trends spanning elections, stock markets, book sales, and flu outbreaks. A key ingredient to all methods that use Twitter for forecasting is to agree on a domain-specific vocabulary to track the pertinent tweets, which is typically provided by subject matter experts (SMEs). The language used in Twitter drastically differs from other forms o

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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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Bigsby, Kristina Gavin. "From hashtags to Heismans: social media and networks in college football recruiting." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6371.

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Анотація:

Social media has changed the way that we create, use, and disseminate information and presents an unparalleled opportunity to gather large-scale data on the networks, behaviors, and opinions of individuals. This dissertation focuses on the role of social media and social networks in recruitment, examining the complex interactions between offline recruiting activities, online social media, and recruiting outcomes. Specifically, it explores how the information college football recruits reveal about themselves online is related to their decisions as well as how this information can diffuse and in

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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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Анотація:

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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Dehghan, Ehsan. "Networked discursive alliances: Antagonism, agonism, and the dynamics of discursive struggles in the Australian Twittersphere." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/174604/1/Ehsan_Dehghan_Thesis.pdf.

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Анотація:

This project examines the complex inter-relationship between social media and democracy, by investigating the dynamics of economic, social, and political disagreements and struggles among Twitter users in Australia. The thesis looks for ways to transform polarisation and disagreements into conflictual togetherness.

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Vondrášek, Petr. "Komerční využití sociálních sítí." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-197442.

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This thesis analyses the possible utilizations of social networks by companies in order to obtain benefits in their field of business. This study is focused on the most popular social network on a global scope, i.e. Facebook, Twitter, LinkedIn, Google+ and YouTube. The theoretical section summarizes and provides an analysis of its suitability and possibility for applications in commercial companies. The possibilities discussed are in particular marketing, client communication, brand development and public relations. Moreover, the practical part of this thesis lists the main case studies that d

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Björnham, Alexandra. "Agile communication for a greener world." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-122117.

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Анотація:

As a research focused organization the problem with making the information easily read and interesting to the extent that the reader wants to share this information with its friends is a crucial one. To create the perfect communication, reaching and affecting the majority of society is an impossible task. If the focus instead lies on the thought that by building a serious and dependable reputation, using the ease of social media and trying to create a ripple effect to make change by networking communication, there is a possibility to influence. The art of persuasion starts by building trust in

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Книги з теми "TWITTER 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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Liu, Huan, Shamanth Kumar, and Fred Morstatter. Twitter Data Analytics. Springer London, Limited, 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, 2013.

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5

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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6

Szpankowski, Wojciech, and Philippe Jacquet. Analytic Pattern Matching: From DNA to Twitter. Cambridge University Press, 2015.

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7

Szpankowski, Wojciech, and Philippe Jacquet. Analytic Pattern Matching: From DNA to Twitter. Cambridge University Press, 2015.

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Szpankowski, Wojciech, and Philippe Jacquet. Analytic Pattern Matching: From DNA to Twitter. Cambridge University Press, 2015.

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Частини книг з теми "TWITTER 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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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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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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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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Haughton, Dominique, Mark-David McLaughlin, Kevin Mentzer, and Changan Zhang. "Can We Predict Oscars from Twitter and Movie Review Data?" In Movie Analytics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09426-7_6.

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Тези доповідей конференцій з теми "TWITTER ANALYTICS"

1

Dhamankar, Robin, and Krishna Gade. "Realtime analytics @ twitter." In the fifth international workshop. ACM Press, 2013. http://dx.doi.org/10.1145/2516588.2516593.

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Perera, Rohan D. W., S. Anand, K. P. Subbalakshmi, and R. Chandramouli. "Twitter analytics: Architecture, tools and analysis." In MILCOM 2010 - 2010 IEEE Military Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/milcom.2010.5680493.

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Feng, Yue, Hossein Fani, Ebrahim Bagheri, and Jelena Jovanovic. "Lexical Semantic Relatedness for Twitter Analytics." In 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2015. http://dx.doi.org/10.1109/ictai.2015.41.

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Vanitha, P. S., and Sreejith Alathur. "E-learning services: Insights from Twitter Analytics." In 2019 International Conference on Advances in Computing and Communication Engineering (ICACCE). IEEE, 2019. http://dx.doi.org/10.1109/icacce46606.2019.9080001.

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Gruzd, Anatoliy, and Nadia Conroy. "Learning Analytics Dashboard for Teaching with Twitter." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2020. http://dx.doi.org/10.24251/hicss.2020.330.

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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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Perez Cabañero, Carmen, Enrique Bigne, Carla Ruiz Mafe, and Antonio Carlos Cuenca. "Sentiment Analysis of Twitter in Tourism Destinations." In CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics. Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/carma2020.2020.11621.

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Given the importance of electronic word of mouth (eWOM), this paperanalyses the content of messages generated by users related to a touristdestination and shared through Twitter. We propose three research questionsregarding eWOM behaviour in Twitter focused on the expertise of thereviewer, sentiment analysis of a tweet and its content. In order to addressthose research questions we carry out text mining analysis by retrievingexisting information on Twitter (over 1500 tweets) regarding to Venice as atourist destination.

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Dussoye, Hirikesh, and Zarine Cadersaib. "Sentiment analytics framework integrating Twitter and Odoo ERP." In 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS). IEEE, 2017. http://dx.doi.org/10.1109/ictus.2017.8285994.

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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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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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