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Academic literature on the topic 'TWITTER ANALYTICS'

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Published: 11 September 2023

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Journal articles on the topic "TWITTER ANALYTICS"

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Goonetilleke, Oshini, Timos Sellis, Xiuzhen Zhang, and Saket Sathe. "Twitter analytics." ACM SIGKDD Explorations Newsletter 16, no. 1 (September 25, 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 (February 8, 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 sparklines showing the distribution of the top terms, hashtags, user mentions, and authors in each of the positive, neutral, and negative classes. Interactive tools are provided to support sub-querying and the examination of emergent themes. Findings – A case study of using Vista to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France) is provided. The authors illustrate how emergent themes can be identified and isolated from the large collection of data, without the need to identify these a priori. Originality/value – Vista provides mechanisms that support the interactive exploration among Twitter data. By combining automatic data processing and machine learning methods with interactive visualization software, researchers are relieved of tedious data processing tasks, and can focus on the analysis of high-level features of the data. In particular, patterns of Twitter use can be identified, emergent themes can be isolated, and purposeful samples of the data can be selected by the researcher for further analysis.
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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 (April 30, 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 (January 27, 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 usage, such as prevalent topics or contents, type of users involved, as well as the user influence based on number of mentions and retweets. The article also presents the network interactions among the LIS associations on Twitter. A systematic Twitter analysis framework of descriptive analytics, content analytics, user analysis and network analytics with relevant metrics used in this study can be applied to other studies of Twitter use.
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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 (August 24, 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 (January 21, 2021): 49. http://dx.doi.org/10.3390/info12020049.

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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 concepts, and textual data extracted from social images, along with linked data. Moreover, we employed word embedding techniques for investigating the usage of latent semantic information towards the identification of similar Twitter images, thereby showcasing that hidden textual information can improve such information retrieval tasks. Finally, we offer an open enhanced version of the annotated dataset described in this study with the aim of further adoption by the research community.
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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 (August 2023): 45–52. http://dx.doi.org/10.1111/1746-692x.12403.

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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 the example of the Vegan Challenge to demonstrate how a combination of the manifold methods of Social Media Analytics can provide extensive insights into the public discourse on food topics. It shows that #veganuary communication on Twitter has a predominantly positive connotation in the discussion of all stakeholders involved. The Vegan Challenge can also be categorised as a strong marketing campaign with a competitive character. #veganuary is commonly discussed on Twitter in tweets related to topics, such as veganism and the climate crisis. We argue that Social Media Analytics usefully extends classical analytical tools of consumer research on emerging and spreading food trends, and offers opportunities for many research studies.
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Dissertations / Theses on the topic "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, thoughts and feelings of people’s daily lives, including those of patients suffering from mental disorders such as depression. Based on the characteristics and linguistic features of the tweets, it is possible to identify signs of depression among Twitter users. Moreover, the effect of antidepressant treatments can be linked to changes in the features of the tweets posted by depressive users. The analysis of this huge volume and diversity of data, the so-called “Big Data”, can provide relevant information about the course of mental disorders and the treatments these patients are receiving, which allows us to detect, monitor and predict depressive disorders. This thesis presents different studies carried out on Twitter data in the Spanish language, with the aim of detecting behavioral and linguistic patterns associated to depression, which can constitute the basis of new and complementary tools for the diagnose and follow-up of patients suffering from this disease
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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, correlation and interpretation of potentially useful information embedded in such text sources. In this scenario, this work presents two system for the analysis of government and social media data. One of the systems introduces a new visualization, based on the river metaphor, for the analysis of the temporal evolution of topics in Twitter in connection with political debates. For this purpose, the problem was initially modeled as a clustering problem and a domain-independent text segmentation method was adapted to associate (by clustering) Twitter content with parliamentary speeches. Moreover, a version of the MONIC framework for cluster transition detection was employed to track the temporal evolution of debates (or clusters) and to produce a set of time-stamped clusters. The other system, named ATR-Vis, combines visualization techniques with active retrieval strategies to involve the user in the retrieval of Twitters posts related to political debates and associate them to the specific debate they refer to. The framework proposed introduces four active retrieval strategies that make use of the Twitters structural information increasing retrieval accuracy while minimizing user involvement by keeping the number of labeling requests to a minimum. Evaluations through use cases and quantitative experiments, as well as qualitative analysis conducted with three domain experts, illustrates the effectiveness of ATR-Vis in the retrieval of relevant tweets. For the evaluation, two Twitter datasets were collected, related to parliamentary debates being held in Brazil and Canada, and a dataset comprising a set of top news stories that received great media attention at the time.
Mídias sociais como o Twitter e o Facebook atuam, em diversas situações, como canais de iniciativas que buscam ampliar as ações de cidadania. Por outro lado, certas ações e manifestações na mídia convencional por parte de instituições governamentais, ou de jornalistas e políticos como deputados e senadores, tendem a repercutir nas mídias sociais. Como resultado, gerase uma enorme quantidade de dados em formato textual que podem ser muito informativos sobre ações e políticas governamentais. No entanto, o público-alvo continua carente de boas ferramentas que ajudem a levantar, correlacionar e interpretar as informações potencialmente úteis associadas a esses textos. Neste contexto, este trabalho apresenta dois sistemas orientados à análise de dados governamentais e de mídias sociais. Um dos sistemas introduz uma nova visualização, baseada na metáfora do rio, para análise temporal da evolução de tópicos no Twitter em conexão com debates políticos. Para tanto, o problema foi inicialmente modelado como um problema de clusterização e um método de segmentação de texto independente de domínio foi adaptado para associar (por clusterização) tweets com discursos parlamentares. Uma versão do algorimo MONIC para detecção de transições entre agrupamentos foi empregada para rastrear a evolução temporal de debates (ou agrupamentos) e produzir um conjunto de agrupamentos com informação de tempo. O outro sistema, chamado ATR-Vis, combina técnicas de visualização com estratégias de recuperação ativa para envolver o usuário na recuperação de tweets relacionados a debates políticos e associa-os ao debate correspondente. O arcabouço proposto introduz quatro estratégias de recuperação ativa que utilizam informação estrutural do Twitter melhorando a acurácia do processo de recuperação e simultaneamente minimizando o número de pedidos de rotulação apresentados ao usuário. Avaliações por meio de casos de uso e experimentos quantitativos, assim como uma análise qualitativa conduzida com três especialistas ilustram a efetividade do ATR-Vis na recuperação de tweets relevantes. Para a avaliação, foram coletados dois conjuntos de tweets relacionados a debates parlamentares ocorridos no Brasil e no Canadá, e outro formado por um conjunto de notícias que receberam grande atenção da mídia no período da coleta.
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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. The factors comes down to time, specifically time and day. To reach a conclusion on this matter the scientific report at hand will use business analytics to analyze data from Twitter to reach this conclusion. The data used for this analyze is from five companies with a Swedish market and based on two months April 2015 and May 2015.
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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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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 of online discourse, such as news articles and blogs. It constantly evolves over time as users adopt popular hashtags to express their opinions. Thus, the vocabulary used by forecasting algorithms needs to be dynamic in nature and should capture emerging trends over time. This thesis proposes a novel unsupervised learning algorithm that builds a dynamic vocabulary using Probabilistic Soft Logic (PSL), a framework for probabilistic reasoning over relational domains. Using eight presidential elections from Latin America, we show how our query expansion methodology improves the performance of traditional election forecasting algorithms. Through this approach we demonstrate how we can achieve close to a two-fold increase in the number of tweets retrieved for predictions and a 36.90% reduction in prediction error.
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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
Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2017.
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 informações sobre possíveis surtos dessas doenças. Entretanto, a análise de grandes volumes de dados - inerente ao estudo em big-data - possui desafios, particularmente, devido à falta de escalabilidade dos algoritmos e à complexidade do gerenciamento dos mais diferentes tipos e estruturas dos dados envolvidos. O principal objetivo desse trabalho é apresentar uma implementação de técnicas de mineração de textos, em especial, aqueles oriundos de redes sociais, tais como o Twitter, aliadas à abordagem de análises em big-data e aprendizado de máquina, para monitorar a incidência das doenças da dengue, chikungunya e zika vírus, todas transmitidas pelo mosquito Aedes aegypti. Os resultados obtidos indicam que a implementação realizada, baseado na junção dos algoritmos de aprendizado de máquina K-Means e SVM, teve rendimento satisfatório para a amostra utilizada em comparação aos registros do Ministério da Saúde, indicando, assim, um potencial para utilização do seu propósito. Observa-se que a principal vantagem das análises em big-data está relacionada à possibilidade de empregar dados não estruturados os quais são obtidos em redes sociais, sites de e-commerce, dentre outros. Nesse sentido, dados que antes pareciam, de certo modo, de pouca importância, passam a ter grande potencial e relevância.
The use of the big-data allied to techniques of text mining has been growing every year in several areas of science, especially in the area of health, precision medicine, electronic medical records among others. The motivation from this work, is based on the hypothesis that it is possible to use big-data concepts to analyze large volumes of data about the dengue disease, chikungunya and zika virus, to monitor and anticipate information about possible outbreaks for diseases. However, the analysis of large volumes of data - inherent in the big-data study - has some challenges, particularly due to the lack of scalability of the algorithms and the complexity of managing the most different types and structures of the data involved. The main objective of this work is to present the implementation of text mining techniques - especially from social networks such as Twitter - allies to the approach of big-data and machine-learned analyzes to monitor the incidence of Dengue, Chikungunya and Zika virus, all transmissions by the mosquito Aedes aegypti. The results obtained indicate that the implementation made based on the combination of machine learning algorithms, K-Means and SVM, got a satisfactory yield for a sample used, if compared the publications of the records of the Ministry of Health, thus indicating a potential for the purpose. It is observed that a main advantage of the big-data analyzes is related to the possibility of employing unstructured data, e-commerce sites, among others. In this sense, data that once seemed, in a way, of little importance, have great potential and relevance.
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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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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 influence the decisions of others. Recruitment occurs in many contexts, and this research draws comparisons between college football and personnel recruiting. This work is one of the first large-scale studies of social media in college football recruiting, and uses a unique dataset that is both broad and deep, capturing information about 2,644 recruits, 682 schools, 764 coaches, and 2,397 current college football players and tracking offline and online behavior over six months. This dissertation comprises three case studies corresponding to the major decisions in the football recruiting cycle—the coach’s decision to make a scholarship offer, the athlete’s decision to commit, and the athlete’s decision to decommit. The first study investigates the relationship between a recruit’s social media use and his recruiting success. Informed by previous work on impression management in personnel recruitment, I construct logistic classifiers to identify self-promotion and ingratiation in 5.5 million tweets and use regression analysis to model the relationship between tweets and scholarship offers over time. The results indicate that tweet content predicts whether an athlete will receive a new offer in the next month. Furthermore, the level of Twitter activity is strongly related to recruiting success, suggesting that simply possessing a social media account may offer a significant advantage in terms of attracting coaches’ attention and earning scholarship offers. These findings underscore the critical role of social media in athletic recruitment and may benefit recruits by informing their branding and communication strategies. The second study examines whether a recruit’s social media activity presages his college preferences. I combine data on recruits’ college options, recruiting activities, Twitter connections, and Twitter content to construct a logistic classifier predicting which school a recruit will select out of those that have offered him a scholarship. My results highlight the value of social media data—especially the hashtags posted by the athlete and his online social network connections—for predicting his commitment decision. These findings may prove useful for college coaches seeking innovative methods to compete for elite talent, as well as assisting them in allocating recruiting resources. The third study focuses on athletic turnover, i.e., decommitments. I construct a logistic classifier to predict the occurrence of decommitments over time based on recruits’ college choices, recruiting activities, online social networks, and the decommitment behavior of their peers. The results further underscore the power of online social networks for predicting offline recruiting outcomes, giving coaches the tools to better identify vulnerable commitments.
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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 müssen. Zwar kann eine logische Reihenfolge definiert werden, aber weitere Anpassungen auf die Fragestellung und die verwendeten Daten können notwendig sein. Unterstützt wird dieser Prozess durch verschiedenen Visualisierungen mittels Histogrammen, Wortwolken und Kartendarstellungen. So kann neues Wissen entdeckt und nach und nach die Parametrisierung der Schritte gemäß den Prinzipien des Geovisual Analytics verfeinert werden. Für eine exemplarische Umsetzung wurde nach der Betrachtung verschiedener Softwareprodukte die für statistische Anwendungen optimierte Programmiersprache R ausgewählt. Abschließend wurden die Software mit Daten von Twitter und Flickr evaluiert
This Master's Thesis deals with the conception and exemplary implementation of a workflow for georeferenced Microblogging Content. Data from Twitter is used as an example and as a starting point to think about how to build that workflow. In the field of Data Mining and Text Mining, there was found a whole range of useful software modules that already exist. Mostly, they only need to get lined up to a process pipeline using appropriate preferences. Although a logical order can be defined, further adjustments according to the research question and the data are required. The process is supported by different forms of visualizations such as histograms, tag clouds and maps. This way new knowledge can be discovered and the options for the preparation can be improved. This way of knowledge discovery is already known as Geovisual Analytics. After a review of multiple existing software tools, the programming language R is used to implement the workflow as this language is optimized for solving statistical problems. Finally, the workflow has been tested using data from Twitter and Flickr
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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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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 depict the utilization of social networks in practice and highlight the benefits the individual companies gain and their costs. Case studies further discuss the use of social networks by traditional and less traditional means for small and larger-sized companies. The thesis is complemented by applied research of social network activities of companies and the analysis of the perception of their users, which is realized by questionnaire survey.
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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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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 a person or in this case in an organization. But if the communication is made by social media, how can one tell if the communication has built trust or created any positive response by the readers? By using Python, a search algorithm has been set up for mining Twitter and analyzing all data covering the area of biofuel and its participants. This data is then used to start an information feedback loop, where the analytical conclusions made from the retrieved information and activities can affect the communication forwarded from the sender. In an agile manner the user is to choose “sprint”-time as well as a time for retrospect, all to refine the analytical method and improve the process.
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Books on the topic "TWITTER ANALYTICS"

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Kumar, Shamanth, Fred Morstatter, and Huan Liu. Twitter Data Analytics. New York, NY: 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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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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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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Szpankowski, Wojciech, and Philippe Jacquet. Analytic Pattern Matching: From DNA to Twitter. Cambridge University Press, 2015.

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Book chapters on the topic "TWITTER ANALYTICS"

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Kumar, Shamanth, Fred Morstatter, and Huan Liu. "Crawling Twitter Data." In Twitter Data Analytics, 5–22. New York, NY: 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, 23–33. New York, NY: 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, 35–48. New York, NY: 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, 49–69. New York, NY: 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, 1–3. New York, NY: 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, 33–52. Cham: 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, 326–33. Cham: 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, 133–44. Cham: 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, 37–47. Cham: 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, 41–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09426-7_6.

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Conference papers on the topic "TWITTER ANALYTICS"

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Dhamankar, Robin, and Krishna Gade. "Realtime analytics @ twitter." In the fifth international workshop. New York, New York, USA: 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. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/carma2020.2020.11621.

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Abstract:
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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