Relevant bibliographies by topics / Twitter stream analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Twitter stream analysis'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Twitter stream analysis"

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D'Andrea, Eleonora, Pietro Ducange, Beatrice Lazzerini, and Francesco Marcelloni. "Real-Time Detection of Traffic From Twitter Stream Analysis." IEEE Transactions on Intelligent Transportation Systems 16, no. 4 (2015): 2269–83. http://dx.doi.org/10.1109/tits.2015.2404431.

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Srivastava, Ritesh, and M. P. S. Bhatia. "Real-Time Unspecified Major Sub-Events Detection in the Twitter Data Stream That Cause the Change in the Sentiment Score of the Targeted Event." International Journal of Information Technology and Web Engineering 12, no. 4 (2017): 1–21. http://dx.doi.org/10.4018/ijitwe.2017100101.

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Twitter behaves as a social sensor of the world. The tweets provided by the Twitter Firehose reveal the properties of big data (i.e. volume, variety, and velocity). With millions of users on Twitter, the Twitter's virtual communities are now replicating the real-world communities. Consequently, the discussions of real world events are also very often on Twitter. This work has performed the real-time analysis of the tweets related to a targeted event (e.g. election) to identify those potential sub-events that occurred in the real world, discussed over Twitter and cause the significant change in
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Anbu Durai, Srinaath. "Resale HDB Price Prediction Considering Covid-19 through Sentiment Analysis." European Conference on Social Media 10, no. 1 (2023): 276–85. http://dx.doi.org/10.34190/ecsm.10.1.1020.

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is de
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Smetanin, Sergey. "RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian." PeerJ Computer Science 8 (July 19, 2022): e1039. http://dx.doi.org/10.7717/peerj-cs.1039.

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The Russian language is still not as well-resourced as English, especially in the field of sentiment analysis of Twitter content. Though several sentiment analysis datasets of tweets in Russia exist, they all are either automatically annotated or manually annotated by one annotator. Thus, there is no inter-annotator agreement, or annotation may be focused on a specific domain. In this article, we present RuSentiTweet, a new sentiment analysis dataset of general domain tweets in Russian. RuSentiTweet is currently the largest in its class for Russian, with 13,392 tweets manually annotated with m
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Rasul, Hakar Mohammed, and Alaa Khalil Jumaa. "Real-Time Twitter Data Analysis: A Survey." UHD Journal of Science and Technology 6, no. 2 (2022): 147–55. http://dx.doi.org/10.21928/uhdjst.v6n2y2022.pp147-155.

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Internet users are used to a steady stream of facts in the contemporary world. Numerous social media platforms, including Twitter, Facebook, and Quora, are plagued with spam accounts, posing a significant problem. These accounts are created to trick unwary real users into clicking on dangerous links or to continue publishing repetitious messages using automated software. This may significantly affect the user experiences on these websites. Effective methods for detecting certain types of spam have been intensively researched and developed. Effectively resolving this issue might be aided by doi
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Sarawagi, Ankit, Rajeev Pandey, Raju Barskar, and S. P. "A Real Time Stream Data Processing and Analysis Model and Catchments over Twitter Stream Data." International Journal of Computer Applications 179, no. 1 (2017): 22–33. http://dx.doi.org/10.5120/ijca2017915663.

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Mamo, Nicholas, Joel Azzopardi, and Colin Layfield. "An Automatic Participant Detection Framework for Event Tracking on Twitter." Algorithms 14, no. 3 (2021): 92. http://dx.doi.org/10.3390/a14030092.

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Topic Detection and Tracking (TDT) on Twitter emulates human identifying developments in events from a stream of tweets, but while event participants are important for humans to understand what happens during events, machines have no knowledge of them. Our evaluation on football matches and basketball games shows that identifying event participants from tweets is a difficult problem exacerbated by Twitter’s noise and bias. As a result, traditional Named Entity Recognition (NER) approaches struggle to identify participants from the pre-event Twitter stream. To overcome these challenges, we desc
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Rahmat, Al Fauzi, and M. Rafi. "Social Media Network Analysis on Twitter Users Network to the Pension Plan Policy." Communicare : Journal of Communication Studies 8, no. 1 (2022): 62. http://dx.doi.org/10.37535/101009120225.

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This article scrutinizes the network of Twitter users on the dissemination of tweets on the Old-Age Guarantee policy in Indonesia. A qualitative method with social media network analysis approach was used. Then, data sources were obtained from Twitter social media through #JHT_JokowiHarusTurun, #jaminanharitua, and #JHT. Furthermore, to manage data source, NVivo 12 plus software was used to analyze qualitative data from Twitter social media – including dissemination rate of tweets, followed by geographical map tweet stream, Twitter user’ network pattern, sentiment proportion, as well as words
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Domade, Ashwini S. "Twitter sentiment Analysis Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41623.

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abstract on page 1 With the development and expansion of web technology, a vast amount of data is generated and available to internet users, and the internet has evolved into a platform for online learning, idea exchange, and opinion sharing. Because they enable people to share and express their opinions on various topics, engage in discussions with various communities, or post messages globally, social networking sites like Facebook, Google, and Twitter are quickly becoming more and more popular. A lot of work has been done in the field of sentiment analysis of Twitter data, which is useful f
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Kim, Erin Hea-Jin, Yoo Kyung Jeong, Yuyoung Kim, Keun Young Kang, and Min Song. "Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news." Journal of Information Science 42, no. 6 (2016): 763–81. http://dx.doi.org/10.1177/0165551515608733.

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The present study investigates topic coverage and sentiment dynamics of two different media sources, Twitter and news publications, on the hot health issue of Ebola. We conduct content and sentiment analysis by: (1) applying vocabulary control to collected datasets; (2) employing the n-gram LDA topic modeling technique; (3) adopting entity extraction and entity network; and (4) introducing the concept of topic-based sentiment scores. With the query term ‘Ebola’ or ‘Ebola virus’, we collected 16,189 news articles from 1006 different publications and 7,106,297 tweets with the Twitter stream API.
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Dissertations / Theses on the topic "Twitter stream analysis"

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RezaeiDivkolaei, Pouya. "DETECTION, CLASSIFICATION, AND LOCATION IDENTIFICATION OF TRAFFIC CONGESTION FROM TWITTER STREAM ANALYSIS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2257.

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Social media today is an important source of information about various events happening around the world. Among various social networking platforms, microtext based ones such as Twitter are of special interest as they are also a rich source of real-time events. In this thesis, our goal is to study the effectiveness of using Twitter as a social sensor for obtaining real-time information on road traffic conditions. Specifically, we focus on: i) identifying tweets that contain traffic event related information, ii) classify such tweets into six main groups of accident, fire, road construction, po
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Santos, Augusto Dias Pereira dos. "Descobrindo eventos locais utilizando análise de séries temporais nos dados do Twitter." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/71953.

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O crescente uso de redes sociais gera quantidades enormes de dados que podem ser empregados em vários tipos de análises. Alguns desses dados têm informação temporal e geográfica, as quais podem ser usadas para posicionar precisamente a informação no tempo e no espaço. Nesse contexto, neste trabalho é proposto um novo método para a análise do volume massivo de mensagens disponível no Twitter, com o objetivo de identificar eventos como programas de TV, mudanças climáticas, desastres e eventos esportivos que estejam ocorrendo em regiões específicas do globo. A abordagem proposta é baseada no uso
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Balasuriya, Lakshika. "Finding Street Gang Member Profiles on Twitter." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1516054679956178.

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Song, Le. "Multimodal Interactional Practices in Live Streams on Twitter." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT019.

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En tant que forme émergente d'interaction médiatisée, la diffusion en direct (live streaming) est devenue une pratique en pleine expansion qui combine les caractéristiques techniques et interactionnelles de l'interaction vidéo-médiatisée et du chat multi-participants. Le live streaming à l'aide d'appareils mobiles sur plusieurs plateformes est donc une pratique dans laquelle les diffuseurs (streamers) et les spectateurs interagissent sous des formes hautement asymétriques: l'affichage vidéo du diffuseur et le texte écrit du spectateur. Cette thèse de doctorat s'intéresse au live streaming en t
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Haldenwang, Nils. "Reliable General Purpose Sentiment Analysis of the Public Twitter Stream." Doctoral thesis, 2017. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017092716282.

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General purpose Twitter sentiment analysis is a novel field that is closely related to traditional Twitter sentiment analysis but slightly differs in some key aspects. The main difference lies in the fact that the novel approach considers the unfiltered public Twitter stream while most of the previous approaches often applied various filtering steps which are not feasible for many applications. Another goal is to yield more reliable results by only classifying a tweet as positive or negative if it distinctly consists of the respective sentiment and mark the remaining messages as uncertain. Tra
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Books on the topic "Twitter stream analysis"

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Elzankalony, Mohamed, Mahmoud Hanafy, and Islam Khalil. Stock Market Social Network Analysis: Social Analysis for Twitter/Stocktwits Stream. Independently Published, 2020.

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Book chapters on the topic "Twitter stream analysis"

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Afzaal, Maryam, Nazifa Nazir, Khadija Akbar, et al. "Real Time Traffic Incident Detection by Using Twitter Stream Analysis." In Human Systems Engineering and Design. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02053-8_95.

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Arora, Shruti, and Rinkle Rani. "A Novel Framework for Distributed Stream Processing and Analysis of Twitter Data." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5113-0_11.

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Kiliroor, Cinu C., and C. Valliyammai. "Binary and Continuous Feature Engineering Analysis on Twitter Data Stream for Classification of Spam Messages." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0829-5_55.

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Ibrahim, Rania, Ahmed Elbagoury, Khaled Ammar, Mohamed S. Kamel, and Fakhri Karray. "Real-Time Detection of Topics in Twitter Streams." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_110157-1.

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Ibrahim, Rania, Ahmed Elbagoury, Khaled Ammar, Mohamed S. Kamel, and Fakhri Karray. "Real-Time Detection of Topics in Twitter Streams." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_110157.

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Liang, Yuzhi, Pengcheng Yin, and S. M. Yiu. "New Word Detection and Tagging on Chinese Twitter Stream." In Big Data Analytics and Knowledge Discovery. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22729-0_24.

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D’Auria, Luca, and Vincenzo Convertito. "Real-Time Mapping of Earthquake Perception Areas in the Italian Region from Twitter Streams Analysis." In Earthquakes and Their Impact on Society. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21753-6_26.

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Luber, Mattias, Christoph Weisser, Benjamin Säfken, Alexander Silbersdorff, Thomas Kneib, and Krisztina Kis-Katos. "Identifying Topical Shifts in Twitter Streams: An Integration of Non-negative Matrix Factorisation, Sentiment Analysis and Structural Break Models for Large Scale Data." In Disinformation in Open Online Media. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87031-7_3.

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De Falco, Ciro Clemente, Noemi Crescentini, and Marco Ferracci. "The Spatial Dimension in Social Media Analysis." In Handbook of Research on Advanced Research Methodologies for a Digital Society. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8473-6.ch029.

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In the data revolution era, the availability of “voluntary” and “derived from social media” geographic information allowed the spatial dimension to gain attention in digital and web studies. The purpose of this work is to recognize the impact of this research stream on some methodological and theoretical issues. The first regards “critical algorithm studies” in order to understand what algorithms are used. The second concerns how these works conceive the space. The last two issues concern the disciplinary areas in which these researches take place and which are the ecological units taken into
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Priya, Kani, Krishnaveni R., Krishnamurthy M., and Bairavel S. "Analyzing Social Emotions in Social Network Using Graph Based Co-Ranking Algorithm." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch018.

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Twitter has become exceedingly popular, with hundreds of millions of tweets being posted every day on a wide variety of topics. This has helped make real-time search applications possible with leading search engines routinely displaying relevant tweets in response to user queries. Recent research has shown that a considerable fraction of these tweets are about “events,” and the detection of novel events in the tweet-stream has attracted a lot of research interest. However, very little research has focused on properly displaying this real-time information about events. For instance, the leading
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Conference papers on the topic "Twitter stream analysis"

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Kumar, Praveen, Tanupriya Choudhury, Seema Rawat, and Shobhna Jayaraman. "Expression of Concern for: Analysis of Various Machine Learning Algorithms for Enhanced Opinion Mining Using Twitter Data Streams." In 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE). IEEE, 2016. http://dx.doi.org/10.1109/icmete38202.2016.10702624.

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R-Moreno, María D., Álvaro Cuesta, and David F. Barrero. "Twitter stream analysis in Spanish." In the 3rd International Conference. ACM Press, 2013. http://dx.doi.org/10.1145/2479787.2479819.

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Lancieri, Luigi, and Romain Giovanetti. "Multilevel exploration in Twitter social stream." In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2016. http://dx.doi.org/10.1109/asonam.2016.7752317.

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Rehman, N. U., S. Mansmann, A. Weiler, and M. H. Scholl. "Building a Data Warehouse for Twitter Stream Exploration." In 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE, 2012. http://dx.doi.org/10.1109/asonam.2012.230.

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Lyebyedyev, Yehor, and Mykola Makhortykh. "#Euromaidan: Quantitative Analysis of Multilingual Framing 2013–2014 Ukrainian Protests on Twitter." In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2018. http://dx.doi.org/10.1109/dsmp.2018.8478462.

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Hakdagli, Ozlem, Caner Ozcan, and Iskender Ulgen Ogul. "Stream text data analysis on twitter using apache spark streaming." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404540.

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Gokulakrishnan, Balakrishnan, Pavalanathan Priyanthan, Thiruchittampalam Ragavan, Nadarajah Prasath, and AShehan Perera. "Opinion mining and sentiment analysis on a Twitter data stream." In 2012 International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2012. http://dx.doi.org/10.1109/icter.2012.6423033.

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Wadera, Mohit, Mukul Mathur, and Dinesh Kumar Vishwakarma. "Sentiment Analysis of Tweets- A Comparison of Classifiers on Live Stream of Twitter." In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2020. http://dx.doi.org/10.1109/iciccs48265.2020.9121166.

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Hao, Ming, Christian Rohrdantz, Halldor Janetzko, et al. "Visual sentiment analysis on twitter data streams." In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2011. http://dx.doi.org/10.1109/vast.2011.6102472.

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Balasuriya, Lakshika, Sanjaya Wijeratne, Derek Doran, and Amit Sheth. "Finding street gang members on Twitter." In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2016. http://dx.doi.org/10.1109/asonam.2016.7752311.

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