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Artykuły w czasopismach na temat „Twitter stream analysis”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

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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Punia, Sanjeev Kumar, Manoj Kumar, Thompson Stephan, Ganesh Gopal Deverajan, and Rizwan Patan. "Performance Analysis of Machine Learning Algorithms for Big Data Classification." International Journal of E-Health and Medical Communications 12, no. 4 (2021): 60–75. http://dx.doi.org/10.4018/ijehmc.20210701.oa4.

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In broad, three machine learning classification algorithms are used to discover correlations, hidden patterns, and other useful information from different data sets known as big data. Today, Twitter, Facebook, Instagram, and many other social media networks are used to collect the unstructured data. The conversion of unstructured data into structured data or meaningful information is a very tedious task. The different machine learning classification algorithms are used to convert unstructured data into structured data. In this paper, the authors first collect the unstructured research data fro

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Weng, Jianshu, and Bu-Sung Lee. "Event Detection in Twitter." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (2021): 401–8. http://dx.doi.org/10.1609/icwsm.v5i1.14102.

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Twitter, as a form of social media, is fast emerging in recent years. Users are using Twitter to report real-life events. This paper focuses on detecting those events by analyzing the text stream in Twitter. Although event detection has long been a research topic, the characteristics of Twitter make it a non-trivial task. Tweets reporting such events are usually overwhelmed by high flood of meaningless “babbles”. Moreover, event detection algorithm needs to be scalable given the sheer amount of tweets. This paper attempts to tackle these challenges with EDCoW (Event Detection with Clustering o

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Karuna, G., Pavuluri Anvesh, Chiranji Sharath Singh, Kommula Ruthvik Reddy, Praveen Kumar Shah, and S. Siva Shankar. "Feasible Sentiment Analysis of Real Time Twitter Data." E3S Web of Conferences 430 (2023): 01045. http://dx.doi.org/10.1051/e3sconf/202343001045.

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Sentiment analysis plays a significant role in understanding public opinion, trends, and sentiments expressed on social media platforms. In this paper, we focus on performing sentiment analysis on real-time Twitter data to gain insights into the sentiments related to specific topics or events, we collect a stream of tweets based on predefined keywords or hashtags. The collected tweets undergo pre-processing steps to clean and standardize the text for sentiment analysis. We employ machine learning classify the sentiments expressed in tweets, utilizing sentiment lexicons and training data as ref

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M., MANIDEEP, and MALIREDDY VENKATA. "SENTIMENT ANALYSIS OF DATA MINING TECHNIQUES FOR SOCIAL NETWORKS." JournalNX - A Multidisciplinary Peer Reviewed Journal 4, no. 8 (2018): 27–31. https://doi.org/10.5281/zenodo.1472709.

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 Sentiment analysis is defined as the task of finding and analyzing the opinions of authors about specific entities or topic. Social network has increased astounding consideration in the most recent decade. Getting to Social network destinations, for example, Twitter, Facebook LinkedIn and Google+ through the web and the web 2.0 innovations has turned out to be more moderate. Data mining gives an extensive variety of methods for identifying helpful learning from enormous datasets like pat- terns, examples and tenets. Data mining methods are utilized for data recovery, measurable displayin

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Lanagan, James, and Alan Smeaton. "Using Twitter to Detect and Tag Important Events in Sports Media." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (2021): 542–45. http://dx.doi.org/10.1609/icwsm.v5i1.14170.

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In this paper we examine the effectiveness of using a filtered stream of tweets from Twitter to automatically identify events of interest within the video of live sports transmissions. We show that using just the volume of tweets generated at any moment of a game actually provides a very accurate means of event detection, as well as an automatic method for tagging events with representative words from the tweet stream. We compare this method with an alternative approach that uses complex audio-visual content analysis of the video, showing that it provides near-equivalent accuracy for major eve

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Gera, Suruchi, and Adwitiya Sinha. "A machine learning-based malicious bot detection framework for trend-centric twitter stream." Journal of Discrete Mathematical Sciences and Cryptography 24, no. 5 (2021): 1337–48. http://dx.doi.org/10.1080/09720529.2021.1932923.

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Chierichetti, Flavio, Jon Kleinberg, Ravi Kumar, Mohammad Mahdian, and Sandeep Pandey. "Event Detection via Communication Pattern Analysis." Proceedings of the International AAAI Conference on Web and Social Media 8, no. 1 (2014): 51–60. http://dx.doi.org/10.1609/icwsm.v8i1.14536.

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Social media applications such as Twitter provide a powerful medium through which users can communicate their observations with friends and with the world at large. We have witnessed live reporting of many events, from soccer games in Johannesburg to revolutions in Cairo and Tunis, and these reports have in many ways rivaled the content provided by the official media. Tapping into this valuable resource is a challenge, due to the heterogeneity and noise inherent in realtime text, diversity of languages, and fast-evolving linguistic norms. In this paper we seek to analyze a tweet stream to auto

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Hoeber, Orland, Larena Hoeber, Maha El Meseery, Kenneth Odoh, and Radhika Gopi. "Visual Twitter Analytics (Vista)." Online Information Review 40, no. 1 (2016): 25–41. http://dx.doi.org/10.1108/oir-02-2015-0067.

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Purpose – Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues. Design/methodology/approach – Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparkl

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Abdullahi, Habeeba Ibraheem, Muhammad Aminu Ahmad, and Khalid Haruna. "Twitter sentiment analysis for Hausa abbreviations and acronyms." Science World Journal 19, no. 1 (2024): 101–4. http://dx.doi.org/10.4314/swj.v19i1.13.

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The use of natural language processing, to identify, extract and organize sentiment from user generated texts in social networks, blogs or product review of text is known as sentiment analysis or opinion mining. Hausa language belongs to one of the major well-spoken languages in Africa and one of the three major Nigerian languages. Now investigating into such a language will have significant influence on social, economic business political and even educational services and settings. Some of these Hausa texts are abbreviated and some in acronym format which is a challenge to researchers as such

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Mr.P.Vijayaragavan, Associate.Prof S.Swetha student M.Selva Udhaya student. "USER BEHAVIOUR ANALYSIS USING SEQUENCE OF DOCUMENT ON INTERNET OF STREAM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 87–91. https://doi.org/10.5281/zenodo.496088.

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Textual documents designed and divided on the Internet are ever changing in various forms. The aim of this project is to characterize and detect personalized and abnormal behaviours of Internet users.It can be applied in many real-life scenarios, such as real-time monitoring on abnormal user behaviours. The existing system of our project works are devoted to topic modelling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. Hence the users activity monitoring doesn’t feasibly and effectively. We propose

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K.V.G.N., Naidu*1 &. P. Sireesha2. "TWITTER ANALYSISFOR IDENTIFICATION OF REAL-TIME TRAFFIC." INTERNATIONAL JOURNAL OF RESEARCH SCIENCE & MANAGEMENT 4, no. 5 (2017): 148–51. https://doi.org/10.5281/zenodo.573507.

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Social networks have been recently empployed as a source of information for event detection, with specific reference to road traffic activity congestion and accidents or earthquack reporting system. In our paper, we present a real-time detection of traffic from Twitter stream analysis. The system fetches tweets from Twitter as per a several search criteria; process tweets by applying text mining methods; lastly performs the classification of tweets. The aim is to assign suitable class label to every tweet, as related with an activity of traffic event or not. The traffic detection system or fra

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Aljebreen, Abdullah, Weiyi Meng, and Eduard Dragut. "Segmentation of Tweets with URLs and its Applications to Sentiment Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12480–88. http://dx.doi.org/10.1609/aaai.v35i14.17480.

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An important means for disseminating information in social media platforms is by including URLs that point to external sources in user posts. In Twitter, we estimate that about 21% of the daily stream of English-language tweets contain URLs. We notice that NLP tools make little attempt at understanding the relationship between the content of the URL and the text surrounding it in a tweet. In this work, we study the structure of tweets with URLs relative to the content of the Web documents pointed to by the URLs. We identify several segments classes that may appear in a tweet with URLs, such as

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Sailaja Kumar, K., D. Evangelin Geetha, and Pratap Rudra Sahoo. "A Methodology to Handle Heterogeneous Data Generated in Online Social Networks." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4098–102. http://dx.doi.org/10.1166/jctn.2020.9025.

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Analyzing the heterogeneous data generated by social networking sites is a research challenge. Twitter is a massive social networking site. In this paper, for processing the heterogeneous data, a methodology is devised, which helps in categorizing the data obtained from Twitter into different directories and understanding the text data explicitly. The methodology is implemented using Python programming language. Python’s tweepy package is used to download the Twitter stream data which includes images, videos and text data. Python’s Aylien API is used for analyzing the Twitter text data. Using

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Ranjan, Ayush. "Real-Time Twitter Trends Analysis Using Latent Dirichlet Allocation and Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem29198.

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As far as social media is concerned twitter has become a major source of public data in the form of tweets. Twitter is an emerging source of large textual data(big data).People can easily express their opinions , reviews, interests and tastes about a particular event, topic ,product etc., occurring worldwide. This makes twitter a good source of valuable data which can be further used to perform sentiment analysis, know trending topics of public intrests and public opinion on a particular product or event which can be beneficial for business growth and political parties to know public choices a

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Burns, John, Tom Kelsey, and Carl Donovan. "Extracting emerging events from social media: X/Twitter and the multilingual analysis of emerging geopolitical topics in near real time." Journal of Social Media Research 2, no. 1 (2025): 50–70. https://doi.org/10.29329/jsomer.14.

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This study uses multiple languages to investigate the emergence of geopolitical topics on X / Twitter across two different time intervals: daily and hourly. For the daily interval, we examined the emergence of topics from February 4th, 2023, to March 23rd, 2023, at random three-hour intervals, compiling the topic modeling results for each day into a time series. For the hourly interval, we considered two days of data, June 1st, 2023, and June 6th, 2023, where we tracked the growth of topics for those days. We collected our data through the X / Twitter Filtered Stream using key bigrams (two-wor

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Cherichi, Soumaya, and Rim Faiz. "Leveraging Temporal Markers to Detect Event from Microblogs." International Journal of Knowledge Society Research 8, no. 3 (2017): 54–67. http://dx.doi.org/10.4018/ijksr.2017070104.

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One of the marvels of our time is the unprecedented development and use of technologies that support social interaction. Social mediating technologies have engendered radically new ways of information and communication, particularly during events; in case of natural disaster like earthquakes tsunami and American presidential election. This paper is based on data obtained from Twitter because of its popularity and sheer data volume. This content can be combined and processed to detect events, entities and popular moods to feed various new large-scale data-analysis applications. On the downside,

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García-Méndez, Silvia, Arriba-Pérez Francisco de, Ana Barros-Vila, and Francisco J. González-Castaño. "Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages." Expert Systems with Applications 218 (January 23, 2023): 14. https://doi.org/10.1016/j.eswa.2023.119611.

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Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (TABEA) system that can individually discern the financial emotions (p

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SRISANKAR, M., and Dr K. P. LOCHANAMBAL. "THE SENTIMENTAL ANALYSIS USING DEEP LEARNING MODELS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27151.

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ABSTRACT:The tweets are brief and come in a steady stream. Emotions have a significant impact on feelings. People can express their ideas about anything and anything on social media. Public perception is divided into three categories: positive, negative, and neutral. In this study, Twitter hotel reviews are gathered and pre-processed before being analyzed using Python's Tweepy package. Re-tweets, tags, URLs, hash tag symbols, and duplicate entries are all eliminated as part of a screening procedure to remove any discrepancies in the data. Using Python's scikit-learn module, tweets are up-sampl

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Elmas, Tuğrulcan, Rebekah Overdorf, and Karl Aberer. "A Dataset of State-Censored Tweets." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 1009–15. http://dx.doi.org/10.1609/icwsm.v15i1.18124.

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Many governments impose traditional censorship methods on social media platforms. Instead of removing it completely, many social media companies, including Twitter, only withhold the content from the requesting country. This makes such content still accessible outside of the censored region, allowing for an excellent setting in which to study government censorship on social media. We mine such content using the Internet Archive's Twitter Stream Grab. We release a dataset of 583,437 tweets by 155,715 users that were censored between 2012-2020 July. We also release 4,301 accounts that were censo

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Aramburu, María José, Rafael Berlanga, and Indira Lanza. "Social Media Multidimensional Analysis for Intelligent Health Surveillance." International Journal of Environmental Research and Public Health 17, no. 7 (2020): 2289. http://dx.doi.org/10.3390/ijerph17072289.

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Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We pr

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Rodrigues, Anisha P., Roshan Fernandes, Aakash A, et al. "Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques." Computational Intelligence and Neuroscience 2022 (April 15, 2022): 1–14. http://dx.doi.org/10.1155/2022/5211949.

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In this modern world, we are accustomed to a constant stream of data. Major social media sites like Twitter, Facebook, or Quora face a huge dilemma as a lot of these sites fall victim to spam accounts. These accounts are made to trap unsuspecting genuine users by making them click on malicious links or keep posting redundant posts by using bots. This can greatly impact the experiences that users have on these sites. A lot of time and research has gone into effective ways to detect these forms of spam. Performing sentiment analysis on these posts can help us in solving this problem effectively.

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Schafer, Valérie, Gérôme Truc, Romain Badouard, Lucien Castex, and Francesca Musiani. "Paris and Nice terrorist attacks: Exploring Twitter and web archives." Media, War & Conflict 12, no. 2 (2019): 153–70. http://dx.doi.org/10.1177/1750635219839382.

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The attacks suffered by France in January and November 2015, and then in the course of 2016, especially the Nice attack, provoked intense online activity both during the events and in the months that followed. The digital traces left by this reactivity and reactions to events gave rise, from the very first days and even hours after the attacks, to a ‘real-time’ institutional archiving by the National Library of France ( Bibliothèque nationale de France, BnF) and the National Audio-visual Institute ( Institut national de l’audiovisuel, Ina). The results amount to millions of archived tweets and

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Rajeshri, R. Shelke. "Identification of User Aware Rare Sequential Pattern in Document Stream An Overview." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 1340–42. https://doi.org/10.5281/zenodo.3591065.

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Documents created and distributed on the Internet are ever changing in various forms. Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. In order to characterize and detect personalized and abnormal behaviours of Internet users, we propose Sequential Topic Patterns STPs and formulate the problem of mining User aware Rare Sequential Topic Patterns URSTPs in document streams on the Internet. They are rare on the whole but relatively frequent for specifi

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Kryvasheyeu, Yury, Haohui Chen, Nick Obradovich, et al. "Rapid assessment of disaster damage using social media activity." Science Advances 2, no. 3 (2016): e1500779. http://dx.doi.org/10.1126/sciadv.1500779.

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Could social media data aid in disaster response and damage assessment? Countries face both an increasing frequency and an increasing intensity of natural disasters resulting from climate change. During such events, citizens turn to social media platforms for disaster-related communication and information. Social media improves situational awareness, facilitates dissemination of emergency information, enables early warning systems, and helps coordinate relief efforts. In addition, the spatiotemporal distribution of disaster-related messages helps with the real-time monitoring and assessment of

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Pandey, Vidushi, Sumeet Gupta, and Manojit Chattopadhyay. "A framework for understanding citizens’ political participation in social media." Information Technology & People 33, no. 4 (2019): 1053–75. http://dx.doi.org/10.1108/itp-03-2018-0140.

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Purpose The purpose of this paper is to explore how the use of social media by citizens has impacted the traditional conceptualization and operationalization of political participation in the society. Design/methodology/approach This study is based on Teorell et al.’s (2007) classification of political participation which is modified to suit the current context of social media. The authors classified 15,460 tweets along three parameters suggested in the framework with help of supervised text classification algorithms. Findings The analysis reveals that Activism is the most prominent form of po

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Trezise, Bryoni. "Minor Representations: From Anne Frank to Bana Alabed – The Radically Performative Literacies of a Viral Child." Forum for Modern Language Studies 56, no. 2 (2020): 115–34. http://dx.doi.org/10.1093/fmls/cqaa006.

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Abstract In this article, I construct a comparative analysis of two forms of child-authored life-narrative: the famous Diary of a Young Girl written by Anne Frank and the contemporary Twitter stream authored by the Syrian child-writer Bana Alabed. My interest in these two textual practices is focused on how they each formulate notions and experiences of temporality that are central to how conceptions of the modern, innocent child and its most recent counterpart – a figure whom I term the viral child – function. Across this analysis, I observe how the textual utterances performed by each child-

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Caviggioli, Federico, Lucio Lamberti, Paolo Landoni, and Paolo Meola. "Technology adoption news and corporate reputation: sentiment analysis about the introduction of Bitcoin." Journal of Product & Brand Management 29, no. 7 (2020): 877–97. http://dx.doi.org/10.1108/jpbm-03-2018-1774.

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Purpose Evidence from previous literature indicates that adopting a new innovative technology has a positive impact on a company’s business performance. Much less work has been carried out into examining whether a technology adoption has impact on corporate reputation. This paper aims to examine the latter topic in a context where social media is the channel used to share news about the introduction of a new technology. The empirical setting of the study consists of five retail companies located in the USA that decided to include Bitcoin as a payment platform. Design/methodology/approach Twitt

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Salama, Mohamed, Hatem Abdul Kader, and Amira Abdelwahab. "An analytic framework for enhancing the performance of big heterogeneous data analysis." International Journal of Engineering Business Management 13 (January 1, 2021): 184797902199052. http://dx.doi.org/10.1177/1847979021990523.

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The use of social media networks is becoming a current phenomenon in the world today where people are sharing posts and tweets, connect with different groups, and share their opinions about things. This data is extremely heterogeneous and so it is hard to analyze and derive information from this data that is considered an indispensable source for decision-makers. New techniques are therefore needed to handle these huge amounts of data to find the hidden information thus improve the results of the analysis. We are developing a framework for the analysis of heterogeneous data using machine learn

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Ahmed, Wasim, and Sergej Lugovic. "Social media analytics: analysis and visualisation of news diffusion using NodeXL." Online Information Review 43, no. 1 (2019): 149–60. http://dx.doi.org/10.1108/oir-03-2018-0093.

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Purpose The purpose of this paper is to provide an overview of NodeXL in the context of news diffusion. Journalists often include a social media dimension in their stories but lack the tools to get digital photos of the virtual crowds about which they write. NodeXL is an easy to use tool for collecting, analysing, visualising and reporting on the patterns found in collections of connections in streams of social media. With a network map patterns emerge that highlight key people, groups, divisions and bridges, themes and related resources. Design/methodology/approach This study conducts a liter

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Yang, Kai-Cheng, Onur Varol, Pik-Mai Hui, and Filippo Menczer. "Scalable and Generalizable Social Bot Detection through Data Selection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1096–103. http://dx.doi.org/10.1609/aaai.v34i01.5460.

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Efficient and reliable social bot classification is crucial for detecting information manipulation on social media. Despite rapid development, state-of-the-art bot detection models still face generalization and scalability challenges, which greatly limit their applications. In this paper we propose a framework that uses minimal account metadata, enabling efficient analysis that scales up to handle the full stream of public tweets of Twitter in real time. To ensure model accuracy, we build a rich collection of labeled datasets for training and validation. We deploy a strict validation system so

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Atiqah Sia Abdullah, Nur, and Hamizah Binti Anuar. "Review of Data Visualization for Social Media Postings." International Journal of Engineering & Technology 7, no. 4.38 (2018): 939. http://dx.doi.org/10.14419/ijet.v7i4.38.27613.

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Facebook and Twitter are the most popular social media platforms among netizen. People are now more aggressive to express their opinions, perceptions, and emotions through social media platforms. These massive data provide great value for the data analyst to understand patterns and emotions related to a certain issue. Mining the data needs techniques and time, therefore data visualization becomes trending in representing these types of information. This paper aims to review data visualization studies that involved data from social media postings. Past literature used node-link diagram, node-li

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Enriquez-Gibson, Judith. "Following hushtag (#)MOOC." Proceedings of the International Conference on Networked Learning 9 (April 7, 2014): 111–20. http://dx.doi.org/10.54337/nlc.v9.8977.

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Electronic posts in social media sites have led to an interpersonal shift that allows discourse-search through the use of hashtags resulting in the emergence of ‘searchable talk' (Zappanigna, 2011) and the rise of database culture (Miller, 2008). The hashtags have referred to ‘trending topics' and have become linguistic markers for ‘findability' towards a new form of sociality that is not based on reciprocity or notion of virtual community. What connects users is not who or an ego-centric node, but what is pass along in a ‘stream' (ie. the movement of ideas, information and sentiments) of re-t

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Tao, Chunliang, Destiny Diaz, Zidian Xie, Long Chen, Dongmei Li, and Richard O’Connor. "Potential Impact of a Paper About COVID-19 and Smoking on Twitter Users’ Attitudes Toward Smoking: Observational Study." JMIR Formative Research 5, no. 6 (2021): e25010. http://dx.doi.org/10.2196/25010.

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Background A cross-sectional study (Miyara et al, 2020) conducted by French researchers showed that the rate of current daily smoking was significantly lower in patients with COVID-19 than in the French general population, implying a potentially protective effect of smoking. Objective We aimed to examine the dissemination of the Miyara et al study among Twitter users and whether a shift in their attitudes toward smoking occurred after its publication as preprint on April 21, 2020. Methods Twitter posts were crawled between April 14 and May 4, 2020, by the Tweepy stream application programming

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Panagiotou, Nikolaos, Antonia Saravanou, and Dimitrios Gunopulos. "News Monitor: A Framework for Exploring News in Real-Time." Data 7, no. 1 (2021): 3. http://dx.doi.org/10.3390/data7010003.

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News articles generated by online media are a major source of information. In this work, we present News Monitor, a framework that automatically collects news articles from a wide variety of online news portals and performs various analysis tasks. The framework initially identifies fresh news (first stories) and clusters articles about the same incidents. For every story, at first, it extracts all of the corresponding triples and, then, it creates a knowledge base (KB) using open information extraction techniques. This knowledge base is then used to create a summary for the user. News Monitor

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Shashwat Shukla., Srishti Sinha.,, Sohan Singh &. Anupam Lakhanpal. "Jarvis: Desktop Assistant." International Journal for Modern Trends in Science and Technology 7, no. 05 (2021): 178–83. http://dx.doi.org/10.46501/ijmtst0705030.

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“Jarvis” was main character of Tony’s Stark’s life assistant in Movies Iron Man. Unlike original comic in which Jarvis was Stark’s human butler, the movie version of Jarvis is an intelligent computer that converses with stark, monitors his household and help to build and program his superhero suit. In this Project Jarvis is Digital Life Assistant which uses mainly human communication means such Twitter, instant message and voice to create two way connections between human and his apartment, controlling lights and appliances, assist in cooking, notify him of breaking news, Facebook’s Notificati

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Baru Khan Bau. "Managing the E-commerce Data Deluge through Text Analytics and Web Management (Overview of Amazon.com)." International Journal of Information Technology and Computer Science Applications 2, no. 2 (2024): 17–24. http://dx.doi.org/10.58776/ijitcsa.v2i2.147.

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Today, more than 80% of the big data handled in the e-commerce industry is text and unstructured data. Text analytics is an automated process for analyzing text and extracting useful information from it. It can discover trends and relationships in data. Web analytics is the collection, processing, and analysis of data in order to draw conclusions to optimize usability on a website. Web analytics can be used to improve the usability of a site by analyzing user behavior patterns such as time spent on the site, abandonment rates, most frequently accessed products, click-through rates, etc. It can

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Baqi, Md Adnan, Mohammad Sarfraz, Mohammad Umar, Aquib Jawed, and Dr Anum Kamal. "A HYBRID TRANSFORMER BASED MODEL FOR DISASTER TWEET CLASSIFICATION USING BERT AND RoBERTa." Journal of Dynamics and Control 9, no. 5 (2025): 107–14. https://doi.org/10.71058/jodac.v9i5010.

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In the era of social media, platforms such as Twitter play an essential role in real-time disaster reporting, offering immediate access to firsthand information during emergencies. This research presents a novel hybrid deep learning model for classifying disaster related tweets by integrating two state-of-the-art transformer architectures: BERT and RoBERTa. Our approach leverages the complementary strengths of each model by independently encoding the same tweet using both architectures, and then fusing their mean pooled representations to generate a more robust feature set for final classifica

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Alok Soreng. "Empowering Public Trust in Vaccines for Effective Outbreak Response." Journal of Information Systems Engineering and Management 10, no. 19s (2025): 625–48. https://doi.org/10.52783/jisem.v10i19s.3104.

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Social media sites like Twitter act as vibrant centres of worldwide communication. highly influential due to the large number of users and the constant stream of information shared. This can be both positive and negative. On the negative side, social media can be a breeding ground for misinformation and can exacerbate public anxieties, especially during crisis situations like pandemics. This paper highlights Understanding how important it is to measure public opinion on social media sites is critical to surviving in today's digital environments, especially regarding concerns lingering after a

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Alok Soreng. "Empowering Public Trust in Vaccines for Effective Outbreak Response." Panamerican Mathematical Journal 35, no. 3s (2025): 251–72. https://doi.org/10.52783/pmj.v35.i3s.3890.

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Social media sites like Twitter act as vibrant centres of worldwide communication. highly influential due to the large number of users and the constant stream of information shared. This can be both positive and negative. On the negative side, social media can be a breeding ground for misinformation and can exacerbate public anxieties, especially during crisis situations like pandemics. This paper highlights Understanding how important it is to measure public opinion on social media sites is critical to surviving in today's digital environments, especially regarding concerns lingering after a

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Wongkoblap, Akkapon, Miguel A. Vadillo, and Vasa Curcin. "Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study." JMIR Mental Health 8, no. 8 (2021): e19824. http://dx.doi.org/10.2196/19824.

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Background Mental health problems are widely recognized as a major public health challenge worldwide. This concern highlights the need to develop effective tools for detecting mental health disorders in the population. Social networks are a promising source of data wherein patients publish rich personal information that can be mined to extract valuable psychological cues; however, these data come with their own set of challenges, such as the need to disambiguate between statements about oneself and third parties. Traditionally, natural language processing techniques for social media have looke

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