Relevant bibliographies by topics / TWITTER DATASET

Academic literature on the topic 'TWITTER DATASET'

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Published: 11 September 2023

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Journal articles on the topic "TWITTER DATASET"

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Meier, Florian. "TWikiL – the Twitter Wikipedia Link Dataset." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 1292–301. http://dx.doi.org/10.1609/icwsm.v16i1.19381.

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Recent research has shown how strongly Wikipedia and other web services or platforms are connected. For example, search engines rely heavily on surfacing Wikipedia links to satisfy their users' information needs and volunteer-created Wikipedia content frequently gets re-used on other social media platforms like Reddit. However, publicly accessible datasets that enable researchers to study the interrelationship between Wikipedia and other platforms are sparse. In addition to that, most studies only focus on certain points in time and don't consider the historical perspective. To begin solving these problems we developed TWikiL, the Twitter Wikipedia Link Dataset, which contains all Wikipedia links posted on Twitter in the period 2006 to January 2021. We extract Wikipedia links from Tweets and enrich the referenced articles with their respective Wikidata identifiers and Wikipedia topic categories, which will make this dataset immediately useful for a large range of scholarly use cases. In this paper, we describe the data collection process, perform an initial exploratory analysis and present a comprehensive overview of how this dataset can be useful for the research community.
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Almalki, Jameel. "A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets." PeerJ Computer Science 8 (July 26, 2022): e1047. http://dx.doi.org/10.7717/peerj-cs.1047.

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Social media platforms such as Twitter, YouTube, Instagram and Facebook are leading sources of large datasets nowadays. Twitter’s data is one of the most reliable due to its privacy policy. Tweets have been used for sentiment analysis and to identify meaningful information within the dataset. Our study focused on the distance learning domain in Saudi Arabia by analyzing Arabic tweets about distance learning. This work proposes a model for analyzing people’s feedback using a Twitter dataset in the distance learning domain. The proposed model is based on the Apache Spark product to manage the large dataset. The proposed model uses the Twitter API to get the tweets as raw data. These tweets were stored in the Apache Spark server. A regex-based technique for preprocessing removed retweets, links, hashtags, English words and numbers, usernames, and emojis from the dataset. After that, a Logistic-based Regression model was trained on the pre-processed data. This Logistic Regression model, from the field of machine learning, was used to predict the sentiment inside the tweets. Finally, a Flask application was built for sentiment analysis of the Arabic tweets. The proposed model gives better results when compared to various applied techniques. The proposed model is evaluated on test data to calculate Accuracy, F1 Score, Precision, and Recall, obtaining scores of 91%, 90%, 90%, and 89%, respectively.
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Dar, Momna, Faiza Iqbal, Rabia Latif, Ayesha Altaf, and Nor Shahida Mohd Jamail. "Policy-Based Spam Detection of Tweets Dataset." Electronics 12, no. 12 (June 14, 2023): 2662. http://dx.doi.org/10.3390/electronics12122662.

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Spam communications from spam ads and social media platforms such as Facebook, Twitter, and Instagram are increasing, making spam detection more popular. Many languages are used for spam review identification, including Chinese, Urdu, Roman Urdu, English, Turkish, etc.; however, there are fewer high-quality datasets available for Urdu. This is mainly because Urdu is less extensively used on social media networks such as Twitter, making it harder to collect huge volumes of relevant data. This paper investigates policy-based Urdu tweet spam detection. This study aims to collect over 1,100,000 real-time tweets from multiple users. The dataset is carefully filtered to comply with Twitter’s 100-tweet-per-hour limit. For data collection, the snscrape library is utilized, which is equipped with an API for accessing various attributes such as username, URL, and tweet content. Then, a machine learning pipeline consisting of TF-IDF, Count Vectorizer, and the following machine learning classifiers: multinomial naïve Bayes, support vector classifier RBF, logical regression, and BERT, are developed. Based on Twitter policy standards, feature extraction is performed, and the dataset is separated into training and testing sets for spam analysis. Experimental results show that the logistic regression classifier has achieved the highest accuracy, with an F1-score of 0.70 and an accuracy of 99.55%. The findings of the study show the effectiveness of policy-based spam detection in Urdu tweets using machine learning and BERT layer models and contribute to the development of a robust Urdu language social media spam detection method.
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Ferragina, Paolo, Francesco Piccinno, and Roberto Santoro. "On Analyzing Hashtags in Twitter." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (August 3, 2021): 110–19. http://dx.doi.org/10.1609/icwsm.v9i1.14584.

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Hashtags, originally introduced in Twitter, are now becoming the most used way to tag short messages in social networks since this facilitates subsequent search, classification and clustering over those messages. However, extracting information from hashtags is difficult because their composition is not constrained by any (linguistic) rule and they usually appear in short and poorly written messages which are difficult to analyze with classic IR techniques. In this paper we address two challenging problems regarding the meaning of hashtags — namely, hashtag relatedness and hashtag classification - and we provide two main contributions. First we build a novel graph upon hashtags and (Wikipedia) entities drawn from the tweets by means of topic annotators (such as TagME); this graph will allow us to model in an efficacious way not only classic co-occurrences but also semantic relatedness among hashtags and entities, or between entities themselves. Based on this graph, we design algorithms that significantly improve state-of-the-art results upon known publicly available datasets. The second contribution is the construction and the public release to the research community of two new datasets: the former is a new dataset for hashtag relatedness, the latter is a dataset for hashtag classification that is up to two orders of magnitude larger than the existing ones. These datasets will be used to show the robustness and efficacy of our approaches, showing improvements in F1 up to two-digits in percentage (absolute).
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Thakur, Nirmalya. "MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions." Infectious Disease Reports 14, no. 6 (November 14, 2022): 855–83. http://dx.doi.org/10.3390/idr14060087.

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The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Furthermore, no prior work has focused on performing a comprehensive analysis of Tweets about this ongoing outbreak. To address these challenges, this work makes three scientific contributions to this field. First, it presents an open-access dataset of 556,427 Tweets about monkeypox that have been posted on Twitter since the first detected case of this outbreak. A comparative study is also presented that compares this dataset with 36 prior works in this field that focused on the development of Twitter datasets to further uphold the novelty, relevance, and usefulness of this dataset. Second, the paper reports the results of a comprehensive analysis of the Tweets of this dataset. This analysis presents several novel findings; for instance, out of all the 34 languages supported by Twitter, English has been the most used language to post Tweets about monkeypox, about 40,000 Tweets related to monkeypox were posted on the day WHO declared monkeypox as a GPHE, a total of 5470 distinct hashtags have been used on Twitter about this outbreak out of which #monkeypox is the most used hashtag, and Twitter for iPhone has been the leading source of Tweets about the outbreak. The sentiment analysis of the Tweets was also performed, and the results show that despite a lot of discussions, debate, opinions, information, and misinformation, on Twitter on various topics in this regard, such as monkeypox and the LGBTQI+ community, monkeypox and COVID-19, vaccines for monkeypox, etc., “neutral” sentiment was present in most of the Tweets. It was followed by “negative” and “positive” sentiments, respectively. Finally, to support research and development in this field, the paper presents a list of 50 open research questions related to the outbreak in the areas of Big Data, Data Mining, Natural Language Processing, and Machine Learning that may be investigated based on this dataset.
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Gamal, Donia, Marco Alfonse, El-Sayed M.El-Horbaty, and Abdel-Badeeh M.Salem. "Twitter Benchmark Dataset for Arabic Sentiment Analysis." International Journal of Modern Education and Computer Science 11, no. 1 (January 8, 2019): 33–38. http://dx.doi.org/10.5815/ijmecs.2019.01.04.

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Aguilar-Gallegos, Norman, Leticia Elizabeth Romero-García, Enrique Genaro Martínez-González, Edgar Iván García-Sánchez, and Jorge Aguilar-Ávila. "Dataset on dynamics of Coronavirus on Twitter." Data in Brief 30 (June 2020): 105684. http://dx.doi.org/10.1016/j.dib.2020.105684.

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Guo, Xiaobo, and Soroush Vosoughi. "A Large-Scale Longitudinal Multimodal Dataset of State-Backed Information Operations on Twitter." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 1245–50. http://dx.doi.org/10.1609/icwsm.v16i1.19375.

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This paper proposes a large-scale and comprehensive dataset of 28 sub-datasets of state-backed tweets and accounts affiliated with 14 different countries, spanning more than 3 years, and a corresponding "negative" dataset of background tweets from the same time period and on similar topics. To our knowledge, this is the first dataset that contains both state-sponsored propaganda tweets and carefully collected corresponding negative tweet datasets for so many countries spanning such a long period of time.
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Nia, Zahra Movahedi, Ali Ahmadi, Bruce Mellado, Jianhong Wu, James Orbinski, Ali Asgary, and Jude D. Kong. "Twitter-based gender recognition using transformers." Mathematical Biosciences and Engineering 20, no. 9 (2023): 15957–77. http://dx.doi.org/10.3934/mbe.2023711.

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<abstract> <p>Social media contains useful information about people and society that could help advance research in many different areas of health (e.g. by applying opinion mining, emotion/sentiment analysis and statistical analysis) such as mental health, health surveillance, socio-economic inequality and gender vulnerability. User demographics provide rich information that could help study the subject further. However, user demographics such as gender are considered private and are not freely available. In this study, we propose a model based on transformers to predict the user's gender from their images and tweets. The image-based classification model is trained in two different methods: using the profile image of the user and using various image contents posted by the user on Twitter. For the first method a Twitter gender recognition dataset, publicly available on Kaggle and for the second method the PAN-18 dataset is used. Several transformer models, i.e. vision transformers (ViT), LeViT and Swin Transformer are fine-tuned for both of the image datasets and then compared. Next, different transformer models, namely, bidirectional encoders representations from transformers (BERT), RoBERTa and ELECTRA are fine-tuned to recognize the user's gender by their tweets. This is highly beneficial, because not all users provide an image that indicates their gender. The gender of such users could be detected from their tweets. The significance of the image and text classification models were evaluated using the Mann-Whitney U test. Finally, the combination model improved the accuracy of image and text classification models by 11.73 and 5.26% for the Kaggle dataset and by 8.55 and 9.8% for the PAN-18 dataset, respectively. This shows that the image and text classification models are capable of complementing each other by providing additional information to one another. Our overall multimodal method has an accuracy of 88.11% for the Kaggle and 89.24% for the PAN-18 dataset and outperforms state-of-the-art models. Our work benefits research that critically require user demographic information such as gender to further analyze and study social media content for health-related issues.</p> </abstract>
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Sagarika, Namasani, Bommadi Sreenija Reddy, Vanka Varshitha, Kodavati Geetanjali, N. V. Ganapathi Raju, and Latha Kunaparaju. "Sarcasm Discernment on Social Media Platform." E3S Web of Conferences 309 (2021): 01037. http://dx.doi.org/10.1051/e3sconf/202130901037.

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Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. To overcome the limitations related to noise in Twitter datasets, this News Headlines dataset for Sarcasm Detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events and we collected all the headlines from News in Brief and News in Photos categories (which are sarcastic). We collect real (and non-sarcastic) news headlines from Huff Post. Sarcasm Detection on social media platform. The dataset is collected from two news websites, theonion.com and huffingtonpost.com. Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings. Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, we get high-quality labels with much less noise as compared to Twitter datasets. Unlike tweets that reply to other tweets, the news headlines obtained are self-contained.
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Dissertations / Theses on the topic "TWITTER DATASET"

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YADAV, DEEPIKA. "SENTIMENT ANALYSIS ON TWITTER DATA." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18821.

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Prior to purchasing an item, individuals for the most part go to different shops in the market, question about the item, cost, and guarantee, and afterward at long last purchase the item dependent on the feelings they got on cost and nature of administration. This procedure is tedious and the odds of being cheated by the merchant are more as there is no one to direct regarding where the purchaser can get valid item and with legitimate expense. Be that as it may, presently a-days a decent number of people rely upon the upon line showcase for purchasing their necessary items. This is on the grounds that the data about the items is accessible from numerous sources; in this manner, it is relatively modest and furthermore has the office of home conveyance. Once more, before experiencing the way toward setting request for any item, clients all the time allude to the remarks or audits of the current clients of the item, which assist them with taking choice about the nature of the item just as the administration gave by the dealer. Like putting request for items, it is seen that there are many experts in the field of films, who experience the film and afterward at long last give a remark about the nature of the film, i.e., to watch the film or not or in five-star rating. These audits are basically in the content arrangement and at times extreme to comprehend. In this manner, these reports should be prepared suitably to get some important data. Order of these audits is one of the ways to deal with extricate information about the surveys. In this theory, distinctive AI procedures are utilized to characterize the audits. Reproduction and trials are done to assess the exhibition of the proposed grouping strategies. It is seen that a decent number of scientists have frequently thought to be two distinctive survey datasets for conclusion grouping to be specific ascension and Polarity dataset. The IMDb dataset is separated into preparing and testing information. Accordingly, preparing information are utilized for preparing the AI calculations and testing information are utilized to test the information dependent on the preparation data. Then again, extremity dataset doesn't have separate information for preparing and testing. In this way, k-crease cross approval procedure is utilized to order the surveys. Four diverse AI strategies (MLTs) viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Linear Discriminant Analysis (LDA) are utilized for the order of these film audits. Diverse execution assessment boundaries are utilized to assess the presentation of the AI strategies. It is seen that among the over four AI calculations, RF method yields the grouping result, with more precision. Also, n-gram based characterization of surveys is completed on the ascension dataset. v The distinctive n-gram procedures utilized are unigram, bigram, trigram, unigram bigram, bigram + trigram, unigram + bigram + trigram. Four distinctive AI strategies, for example, Naive Bayes (NB), Maximum Entropy (ME), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD) methods are utilized to arrange the film surveys dependent on the n-gram strategy as referenced before. Diverse execution assessment boundaries are utilized to assess the presentation of these AI methods. The SVM method with unigram + bigram approach has demonstrated more exact outcome among every different methodologies. Thirdly, SVM-based element determination strategy is utilized to choose best highlights from the arrangement everything being equal. These chose highlights are then considered as contribution to Artificial Neural Network (ANN) to characterize the surveys information. For this situation, two distinctive audit datasets i.e., IMDb and Polarity dataset are considered for grouping. In this technique, each expression of these surveys is considered as a component, and the assumption estimation of each word is determined. The component choice is done dependent on the opinion estimations of the expression. The words having higher assumption esteems are chosen. These words at that point go about as a contribution to ANN based on which the film audits are ordered. At last, Genetic Algorithm (GA) is utilized to speak to the film surveys as chromosomes. Various activities of GA are completed to get the last arrangement result. Alongside this, the GA is likewise utilized as highlight choice to choose the best highlights from the arrangement of all highlights which in the end are given as contribution to ANN to acquire the last grouping outcome. Distinctive execution assessment boundaries are utilized to assess the presentation of GA and half breed of GA with ANN. Feeling examination regularly manages investigation of surveys, remarks about any item, which are for the most part printed in nature and need legitimate preparing to got any significant data. In this postulation, various methodologies have been proposed to arrange the audits into particular extremity gatherings, i.e., positive and negative. Distinctive MLTs are utilized in this theory to play out the errand of arrangement and execution of every strategy is assessed by utilizing various boundaries, viz., exactness, review, f-measure and precision. The outcomes acquired by the proposed approaches are seen as better than the outcomes as announced by different creators in writing utilizing same dataset and approaches.
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Кохан, Василь Володимир Богданович, and Vasyl Volodymyr Kokhan. "Алгоритмічне та програмне забезпечення систем автоматизованого оцінювання емоційного нахилу статей про Україну." Master's thesis, Тернопільський національний технічний університет імені Івана Пулюя, 2021. http://elartu.tntu.edu.ua/handle/lib/36642.

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Метою роботи є дослідження та розробка алгоритму та програмного забезпечення для проведення автоматизованої оцінки емоційного нахилу статей з соціальної мережі мікроблогів Твіттер. В розділі 1 розглянуто алгоритми для визначення емоційного нахилу текстів, наявні онлайн інструменти аналізу та підходи до збору даних для аналізу. В розділі 2 описано процес отримання доступу до Твіттер АПІ, підходи до збору даних для аналізу та проведено аналіз існуючих алгоритмів оцінки емоційного нахилу текстів. В розділі 3 проведено збір даних для оцінки через Твіттер АПІ, розроблено та реалізовано у коді алгоритм очистки даних від зайвих елементів, розроблено та реалізовано алгоритм аналізу емоційного нахилу текстів. В розділі 4 описано основні вимоги роботи та експлуатації програмної реалізації алгоритму автоматизованого оцінювання емоційного нахилу статей про Україну, відповідно до державних санітарних правил і норм роботи з візуальними дисплейними терміналами електронно-обчислювальних машин ДСанПІН 3.3.2.007-98.
The aim of the work is to research and develop an algorithm and software for automated sentiment analysis of articles from the social microblogs network Twitter. Section 1 describes algorithms for determining the sentiment of texts, analysis tools available online, and approaches to collecting data for analysis. Section 2 describes the process of gaining access to the Twitter API, approaches to data collection for analysis, and analyzes existing algorithms for sentiment analysis of texts. In section 3, data was collected for evaluation via Twitter API, was developed an algorithm for cleaning data from redundant elements and implemented in the code, and an algorithm for sentiment analysis of texts was developed and implemented as well. Section 4 describes the main requirements for the operation of the software implementation of the algorithm for automated sentiment analysis of articles about Ukraine, in accordance with state sanitary rules and regulations for working with visual display terminals of electronic computers DSanPIN 3.3.2.007-98.
ВСТУП 9 РОЗДІЛ 1 АНАЛІЗ ПРЕДМЕТНОЇ ОБЛАСТІ ТА ОГЛЯД АЛГОРИТМІВ ДЛЯ ВИЗНАЧЕННЯ ЕМОЦІЙНОГО НАХИЛУ ТЕКСТІВ 11 1.1. Оцінка емоційного нахилу текстів 11 1.2. Аналіз інструментів та публікацій на тему аналізу емоційного нахилу 14 1.3. Підходи до збору даних для аналізу емоційного нахилу текстів 18 1.4. Висновки розділу 1 23 РОЗДІЛ 2 РОЗРОБКА АЛГОРИТМУ ОЦІНКИ ЕМОЦІЙНОГО НАХИЛУ СТАТТЕЙ НОВИН 25 2.1. Процес отримання доступу до Твіттер АПІ 25 2.2. Звернення до Твіттер АПІ 28 2.3. Аналіз даних 32 2.4. Висновки розділу 2 33 РОЗДІЛ 3 ПРАКТИЧНА РЕАЛІЗАЦІЯ АЛГОРИТМУ ТА ДОСЛІДЖЕННЯ АНАЛІЗУ ЕМОЦІЙНОГО НАХИЛУ ТЕКСТУ 34 3.1. Процес збору даних 34 3.2. Підготовка даних до аналізу 36 3.3. Оцінювання емоційного нахилу текстів 40 3.4. Висновки розділу 3 46 РОЗДІЛ 4 ОХОРОНА ПРАЦІ ТА БЕЗПЕКА В НАДЗВИЧАЙНИХ СИТУАЦІЯХ 47 4.1. Охорона праці 47 4.2. Забезпечення безпеки життєдіяльності при роботі з ПК 49 ВИСНОВКИ 52 ПЕРЕЛІК ПОСИЛАНЬ 53 ДОДАТОК А 57 ДОДАТОК Б 64 ДОДАТОК В 65
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Björck, Olof. "Creating Interactive Visualizations for Twitter Datasets using D3." Thesis, Uppsala universitet, Matematiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-351802.

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Project Meme Evolution Programme (Project MEP) is a research program directed by Raazesh Sainudiin, Uppsala University, Sweden, that collects and analyzes datasets from Twitter. Twitter can be used to understand how ideas spread in social media. This project aims to produce interactive visualizations for datasets collected in Project MEP. Such interactive visualizations will facilitate exploratory data analysis in Project MEP. Several technologies had to be learned to produce the visualizations, most notably JavaScript, D3, and Scala. Three interactive visualizations were produced; one that allows for exploration of a Twitter user timeline and two that allows for exploration and understanding of a Twitter retweet network. The interactive visualizations are accessible as Scala functions and in a website developed in this project and uploaded to GitHub. The interactive visulizations contain some known bugs but they still allow for useful exploratory data analysis of Project MEP datasets and the project goal is therefore considered met.
Project Meme Evolution Programme
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Books on the topic "TWITTER DATASET"

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Shi, Feng. Learn About Encodings in R With Data From How ISIS Uses Twitter Dataset (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526488633.

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Shi, Feng. Learn About Encodings in Python With Data From How ISIS Uses Twitter Dataset (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526497857.

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Shi, Feng. Learn About Regular Expressions in R With Data From How ISIS Uses Twitter Dataset (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526488824.

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Shi, Feng. Learn About Regular Expressions in Python With Data From How ISIS Uses Twitter Dataset (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526497871.

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Shi, Feng. Learn About Text Pre-Processing in R With Data From How ISIS Uses Twitter Dataset (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526488909.

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Shi, Feng. Learn About Text Pre-Processing in Python With Data From How ISIS Uses Twitter Dataset (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526497864.

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Shi, Feng. Learn About Basic Concepts in Text Analysis in R With Data From How ISIS Uses Twitter Dataset (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526488626.

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Shi, Feng. Learn About Basic Concepts in Text Analysis in Python With Data From How ISIS Uses Twitter Dataset (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526497796.

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Shi, Feng. Learn About Term Frequency–Inverse Document Frequency in Text Analysis in R With Data From How ISIS Uses Twitter Dataset (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526489012.

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Shi, Feng. Learn About Term Frequency–Inverse Document Frequency in Text Analysis in Python With Data From How ISIS Uses Twitter Dataset (2016). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526498038.

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Book chapters on the topic "TWITTER DATASET"

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Mukhopadhyay, Debajyoti, Kirti Mishra, Kriti Mishra, and Laxmi Tiwari. "Cyber Bullying Detection Based on Twitter Dataset." In Machine Learning for Predictive Analysis, 87–94. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_9.

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Nandy, Hiran, and Rajeswari Sridhar. "Filtering-Based Text Sentiment Analysis for Twitter Dataset." In Advances in Intelligent Systems and Computing, 1035–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3514-7_77.

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Kumar, Yogesh, Sameeka Saini, Harendra Sharma, Ritu Payal, and Arpit Mishra. "Feedback Investigation on Twitter Dataset Using Classification Approaches." In Proceedings of International Conference on Recent Trends in Computing, 251–62. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7118-0_22.

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Llewellyn, Clare, Claire Grover, Beatrice Alex, Jon Oberlander, and Richard Tobin. "Extracting a Topic Specific Dataset from a Twitter Archive." In Research and Advanced Technology for Digital Libraries, 364–67. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24592-8_36.

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Thorburn, Joshua, Javier Torregrosa, and Ángel Panizo. "Measuring Extremism: Validating an Alt-Right Twitter Accounts Dataset." In Intelligent Data Engineering and Automated Learning – IDEAL 2018, 9–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03496-2_2.

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Gupta, Shelley, Archana Singh, and Jayanthi Ranjan. "An Online Document Emoji-Based Classification Using Twitter Dataset." In Proceedings of Data Analytics and Management, 409–17. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6285-0_33.

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Wijesiriwardene, Thilini, Hale Inan, Ugur Kursuncu, Manas Gaur, Valerie L. Shalin, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "ALONE: A Dataset for Toxic Behavior Among Adolescents on Twitter." In Lecture Notes in Computer Science, 427–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60975-7_31.

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Nair, Vinita, and Jyoti Pareek. "Evaluation of Supervised Classifiers for Fake News Detection Using Twitter Dataset." In Springer Proceedings in Mathematics & Statistics, 435–46. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15175-0_36.

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Mathews, Deepa Mary, and Sajimon Abraham. "Twitter Data Sentiment Analysis on a Malayalam Dataset Using Rule-Based Approach." In Emerging Research in Computing, Information, Communication and Applications, 407–15. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6001-5_33.

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Uniyal, Deepak, and Amit Agarwal. "IRLCov19: A Large COVID-19 Multilingual Twitter Dataset of Indian Regional Languages." In Communications in Computer and Information Science, 309–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93733-1_22.

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Conference papers on the topic "TWITTER DATASET"

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Saputri, Mei Silviana, Rahmad Mahendra, and Mirna Adriani. "Emotion Classification on Indonesian Twitter Dataset." In 2018 International Conference on Asian Language Processing (IALP). IEEE, 2018. http://dx.doi.org/10.1109/ialp.2018.8629262.

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Jonker, Richard Adolph Aires, Roshan Poudel, Olga Fajarda, Sérgio Matos, José Luís Oliveira, and Rui Pedro Lopes. "Portuguese Twitter Dataset on COVID-19." In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2022. http://dx.doi.org/10.1109/asonam55673.2022.10068592.

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Putra, Oddy Virgantara, Fathin Muhammad Wasmanson, Triana Harmini, and Shoffin Nahwa Utama. "Sundanese Twitter Dataset for Emotion Classification." In 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM). IEEE, 2020. http://dx.doi.org/10.1109/cenim51130.2020.9297929.

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Rahutomo, Reza, Arif Budiarto, Kartika Purwandari, Anzaludin Samsinga Perbangsa, Tjeng Wawan Cenggoro, and Bens Pardamean. "Ten-Year Compilation of #SaveKPK Twitter Dataset." In 2020 International Conference on Information Management and Technology (ICIMTech). IEEE, 2020. http://dx.doi.org/10.1109/icimtech50083.2020.9211246.

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Salem, Marwa S., Sally S. Ismail, and Mostafa Aref. "Personality Traits for Egyptian Twitter Users Dataset." In ICSIE '19: 2019 8th International Conference on Software and Information Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3328833.3328851.

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Wagh, Rasika, and Payal Punde. "Survey on Sentiment Analysis using Twitter Dataset." In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2018. http://dx.doi.org/10.1109/iceca.2018.8474783.

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Squire, Megan. "Apache-affiliated Twitter screen names: A dataset." In 2013 10th IEEE Working Conference on Mining Software Repositories (MSR 2013). IEEE, 2013. http://dx.doi.org/10.1109/msr.2013.6624043.

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Sahu, Lokesh, and Bhavesh Shah. "An Emotion based Sentiment Analysis on Twitter Dataset." In 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET). IEEE, 2022. http://dx.doi.org/10.1109/ccet56606.2022.10079995.

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Zumma, Md Thoufiq, Jerin Akther Munia, Dipankar Halder, and Md Sadekur Rahman. "Personality Prediction from Twitter Dataset using Machine Learning." In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2022. http://dx.doi.org/10.1109/icccnt54827.2022.9984495.

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Manolescu, Mihai, and Çağrı Çöltekin. "ROFF - A Romanian Twitter Dataset for Offensive Language." In International Conference Recent Advances in Natural Language Processing. INCOMA Ltd. Shoumen, BULGARIA, 2021. http://dx.doi.org/10.26615/978-954-452-072-4_102.

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Reports on the topic "TWITTER DATASET"

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Warin, Thierry. The World Health Organization in a Post-COVID-19 Era: An Exploration of Public Engagement on Twitter. CIRANO, June 2022. http://dx.doi.org/10.54932/ehuh4224.

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Abstract:
This article analyses the conversations on Twitter related to the World Health Organization (WHO). We collect the text of the discussions as well as the metadata associated with each tweet. Our dataset is exhaustive as it includes all the tweets produced by WHO. Likes, retweets, and replies capture the level of engagement. The goal is to quantify the balance of likes, retweets, and replies, also known as “ratios”, and study their dynamics as proxy for the collective engagement in response to WHO’s communications. Our results demonstrate a higher engagement of the public receiving the information pushed by WHO. This engagement translates into a more balanced reaction with still a more likely favorable opinion vis-à-vis WHO, but with also more challenges. This protocol based on quantitative measures to serve as a proxy to the legitimacy concept seems to hold its promises. In particular, we also perform a simple sentiment analysis to check the robustness of our conclusions.
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