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Journal articles on the topic 'Streaming technology (Telecommunications) Data mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Manasvi Gawande, Manjiri Pise. "Application of Data Mining in Telecommunication Industry." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 1 (2019): 05–08. http://dx.doi.org/10.17762/ijritcc.v7i1.5218.

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Data Mining is a logical procedure intended to investigate data (normally a lot of data - commonly business or market related - otherwise called "enormous data") looking for predictable examples as well as methodical connections amongst factors, and after that to approve the discoveries by applying the recognized examples to new subsets of data. The telecommunications industry inside the division of data and correspondence technology is comprised of all Telecommunications/telephone companies and web access suppliers and assumes the urgent part in the development of versatile interchanges and t

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Du, Jiang Yi, and Yi Meng Chen. "Applicatiions and Research of Data Mining in Teaching." Applied Mechanics and Materials 58-60 (June 2011): 2659–63. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2659.

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Data mining technology has been widely used in the retail, finance, telecommunications and many other industries. With the promotion of education informationiation, useing the data mining technology in network education , finding useful knowledge in large education data to guide education and develop education become a necessary research. Based on the descriptionof the concept，characteristics, methods, and implement process of data mining, this paper introduces its several applications in teaching and the positive effect of teaching.

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Kelly, M. G. (Peggy), and James H. Wiebe. "Teaching Mathematics with Technology: Mining Mathematics on the Internet." Arithmetic Teacher 41, no. 5 (1994): 276–81. http://dx.doi.org/10.5951/at.41.5.0276.

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Throughout the Curriculum and Evaluation Standards for School Mathematics (NCTM 1989) the notion of students and teachers as critical thinkers, information seekers, and problem solvers is a priority. The Internet, an electronic highway connected by gateways from one computer network to another, furnishes a telecommunications link around the world. The Internet enables students and teachers to access authentic, real-time data for critical analysis. With access to such Internet service providers as a university computer network or a commercial service like Compuserve, Prodigy, or Applelink, stud

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Tang, De Huai. "Data Mining’s Network Traffic Data Analysis in Android Mobile Terminal." Applied Mechanics and Materials 644-650 (September 2014): 2055–58. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2055.

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With the rapid development of communication industry in China from 2G to 4G networks, operators’ competition is intense in data flow business. Android mobile terminal is now widely used by people. Network traffic analysis is the premise to improve network speed and real needs of customers, excavate valuable information in vast amounts of data, and an important work for network providers analyzing flow rate and value. This paper mainly introduced the relevant contents of data mining, and data mining’s network traffic data analysis in Android mobile terminal.With the development of computer tech

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Khalaf, Osamah Ibrahim, Ghaida Muttashar Abdulsahib, Hamed Daei Kasmaei, and Kingsley A. Ogudo. "A New Algorithm on Application of Blockchain Technology in Live Stream Video Transmissions and Telecommunications." International Journal of e-Collaboration 16, no. 1 (2020): 16–32. http://dx.doi.org/10.4018/ijec.2020010102.

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This article develops and defines Blockchain technology in its classic format. New suggested proposed algorithms are then analyzed in order to introduce new and modified versions of Blockchain technology. After that, fundamental infrastructure is presented in order to represent its application in new generation of telecommunications. In addition, this article interrogates these algorithms and their efficiency to make secure connections that transfer data packets in any format (boxes or packets of information) in a secure and encrypted method at which sender and receiver of information remain a

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Borodo, Salisu Musa, Siti Mariyam Shamsuddin, and Shafaatunnur Hasan. "Big Data Platforms and Techniques." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 1 (2016): 191. http://dx.doi.org/10.11591/ijeecs.v1.i1.pp191-200.

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Data is growing at unprecedented rate and has led to huge volume generated; the data sources include mobile, internet and sensors. This voluminous data is generated and updated at high velocity by batch and streaming platforms. This data is also varied along structured and unstructured types. This volume, velocity and variety of data led to the term big data. Big data has been premised to contain untapped knowledge, its exploration and exploitation is termed big data analytics. This literature reviewed platforms such as batch processing, real time processing and interactive analytics used in b

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Chen, Chih Ping, Ju-Yin Weng, Chin-Sheng Yang, and Fan-Mei Tseng. "Employing a data mining approach for identification of mobile opinion leaders and their content usage patterns in large telecommunications datasets." Technological Forecasting and Social Change 130 (May 2018): 88–98. http://dx.doi.org/10.1016/j.techfore.2018.01.014.

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Guo, Kaiyuan, and Wenbo Wang. "Research on Telecom Fraud Detection Model Based on Cellular Network Data." Journal of Networking and Telecommunications 2, no. 1 (2020): 12. http://dx.doi.org/10.18282/jnt.v2i1.835.

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<p align="justify">With the rapid development of wireless communication technology, the use of mobile phones and other means of communication for telecommunications fraud has become a major problem that endangers user security. Aiming at this problem, this paper constructs a telecom fraud user detection model by in-depth analysis and mining of cellular network data. The model includes data processing, CNNcombine algorithm and model evaluation. First, in the data processing part, the data set is subjected to feature screening, coding, sampling, and the like. Secondly, the CNNcombine algor

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Srivani, B., N. Sandhya, and B. Padmaja Rani. "Literature review and analysis on big data stream classification techniques." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 3 (2020): 205–15. http://dx.doi.org/10.3233/kes-200042.

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Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodo

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Brzustewicz, Paweł, and Anupam Singh. "Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA." Energies 14, no. 18 (2021): 5787. http://dx.doi.org/10.3390/en14185787.

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By using text mining techniques, this study identifies the topics of sustainable consumption that are important during the COVID-19 pandemic. An Application Programming Interface (API) streaming method was used to extract the data from Twitter. A total of 14,591 tweets were collected using Twitter streaming API. However, after data cleaning, 13,635 tweets were considered for analysis. The objectives of the study are to identify (1) the topics users tweet about sustainable consumption and (2) to detect the emotion-based sentiments in the tweets. The study used Latent Dirichlet Allocation (LDA)

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Fong, Simon, Justin Liang, Iztok Fister, Iztok Fister, and Sabah Mohammed. "Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm." Journal of Sensors 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/205707.

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Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately

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Rodríguez-Quintero, Jorge-Félix, Alexander Sánchez-Díaz, Leonel Iriarte-Navarro, Alejandro Maté, Manuel Marco-Such, and Juan Trujillo. "Fraud Audit Based on Visual Analysis: A Process Mining Approach." Applied Sciences 11, no. 11 (2021): 4751. http://dx.doi.org/10.3390/app11114751.

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Among the knowledge areas in which process mining has had an impact, the audit domain is particularly striking. Traditionally, audits seek evidence in a data sample that allows making inferences about a population. Mistakes are usually committed when generalizing the results and anomalies; therefore, they appear in unprocessed sets; however, there are some efforts to address these limitations using process-mining-based approaches for fraud detection. To the best of our knowledge, no fraud audit method exists that combines process mining techniques and visual analytics to identify relevant patt

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Gajowniczek, Krzysztof, Marcin Bator, Tomasz Ząbkowski, Arkadiusz Orłowski, and Chu Kiong Loo. "Simulation Study on the Electricity Data Streams Time Series Clustering." Energies 13, no. 4 (2020): 924. http://dx.doi.org/10.3390/en13040924.

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Currently, thanks to the rapid development of wireless sensor networks and network traffic monitoring, the data stream is gradually becoming one of the most popular data generating processes. The data stream is different from traditional static data. Cluster analysis is an important technology for data mining, which is why many researchers pay attention to grouping streaming data. In the literature, there are many data stream clustering techniques, unfortunately, very few of them try to solve the problem of clustering data streams coming from multiple sources. In this article, we present an al

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14

Pal, Debajyoti, and Tuul Triyason. "A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective." International Journal of Digital Multimedia Broadcasting 2018 (May 27, 2018): 1–25. http://dx.doi.org/10.1155/2018/1391724.

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Over the past few years there has been an exponential increase in the amount of multimedia data being streamed over the Internet. At the same time, we are also witnessing a change in the way quality of any particular service is interpreted, with more emphasis being given to the end-users. Thus, silently there has been a paradigm shift from the traditional Quality of Service approach (QoS) towards a Quality of Experience (QoE) model while evaluating the service quality. A lot of work that tries to evaluate the quality of audio, video, and multimedia services over the Internet has been done. At

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15

Ellul, C., V. Coors, S. Zlatanova, R. Laurini, and M. Rumor. "PREFACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W11 (September 20, 2018): 1–2. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w11-1-2018.

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<p><strong>Abstract.</strong> Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, deci

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Ellul, C., V. Coors, S. Zlatanova, R. Laurini, and M. Rumor. "PREFACE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W7 (September 20, 2018): 1–2. http://dx.doi.org/10.5194/isprs-annals-iv-4-w7-1-2018.

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<p><strong>Abstract.</strong> Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, deci

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Coors, V., P. Rodrigues, C. Ellul, S. Zlatanova, R. Laurini, and M. Rumor. "PREFACE." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences VIII-4/W1-2021 (September 3, 2021): 1–2. http://dx.doi.org/10.5194/isprs-annals-viii-4-w1-2021-1-2021.

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Abstract. Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, decision making relating to education and

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18

Coors, V., P. Rodrigues, C. Ellul, S. Zlatanova, R. Laurini, and M. Rumor. "PREFACE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W1-2021 (September 3, 2021): 1–2. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w1-2021-1-2021.

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Abstract. Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, decision making relating to education and

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19

Nayyar, Anand, Pijush Kanti Dutta Pramankit, and Rajni Mohana. "Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions." Scalable Computing: Practice and Experience 21, no. 3 (2020): 347–48. http://dx.doi.org/10.12694/scpe.v21i3.1568.

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Internet of Things (IoT) is regarded as a next-generation wave of Information Technology (IT) after the widespread emergence of the Internet and mobile communication technologies. IoT supports information exchange and networked interaction of appliances, vehicles and other objects, making sensing and actuation possible in a low-cost and smart manner.  On the other hand, cyber-physical systems (CPS) are described as the engineered systems which are built upon the tight integration of the cyber entities (e.g., computation, communication, and control) and the physical things (natural

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20

"Data Stream Clustering Algorithms: Challenges and Future Directions." International Journal of Innovative Technology and Exploring Engineering 8, no. 11 (2019): 3676–81. http://dx.doi.org/10.35940/ijitee.k1990.0981119.

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In the fast growing world applications are generating data in enormous volumes called data streams. Data stream is imaginably large, continual, rapid flow of information and in data mining the important tool is called clustering, hence data stream clustering (DSC) can be said as active research area. Recent attention of data stream clustering is through the applications that contain large amounts of streaming data. Data stream clustering is used in many areas such as weather forecasting, financial transactions, website analysis, sensor network monitoring, e-business, telephone records and tele

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21

Karale, Ankita. "Outlier Detection Methods and the Challenges for their Implementation with Streaming Data." Journal of Mobile Multimedia, November 28, 2020. http://dx.doi.org/10.13052/jmm1550-4646.1635.

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Outlier detection has been a generally examined issue and highly used in a varied range of spaces. For example, transaction fraud, certain rise and fall in share market, sudden changes in weather, interruption detection for digital security, and fraud detection in security design patterns in data. Data mining is the rule of dealing with big amounts of data and choosing the important. Outlier detection is data mining procedures that identify uncommon occasions and special cases. This paper discusses fundamental concepts of outlier detection, the outlier types and the challenges in their detecti

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Liu, Cong, Yong Chen, and Li Zhao. "An adaptive prediction method based on data stream mining for future driving cycle of vehicle." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, November 23, 2020, 095440702097315. http://dx.doi.org/10.1177/0954407020973152.

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Due to complex and changeable driving cycles in urban roads, it is a challenging task for most of the current control strategies utilized in vehicles to adapt to the driving environment. At the same time, hardware requirements for storing and processing a massive amount of streaming data are increasing, which lead to excessive accumulated errors and high computational cost. To deal with this problem, an innovative prediction method, which is based on Markov chain and data stream mining, is proposed to predict the future driving cycle of vehicles. State transition probability matrix is updated

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23

"Customer Perception towards Networked Streaming Service Providers with Reference to Amazon Prime and Netflix." International Journal of Recent Technology and Engineering 9, no. 1 (2020): 513–17. http://dx.doi.org/10.35940/ijrte.a1911.059120.

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Online video streaming services like Amazon Prime and Netflix have redefined the concept of movie watching and entertainment. They have become hugely popular worldwide. Their subscribers are increasing day by day. There is also intense competition between the two for getting viewer attention. In this context, the study aims at understanding the viewer perception about the two online service providers through viewer opinions collected from different social media platforms like Instagram, Facebook, Twitter, blogs, discussion forums, etc. Viewer opinions expressed online becomes electronic word-o

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"Enhancing the Health Care Data Security through Blockchain." International Journal of Engineering and Advanced Technology 8, no. 6 (2019): 549–54. http://dx.doi.org/10.35940/ijeat.f8057.088619.

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As the Internet technology is improving, large amount of data is generated. To deal with this huge amount of data many applications try to store the data on cloud networks. So before storing the data, security of data should be taken into consideration. Many classical existing approaches have already provided ACID properties with transaction management for consistent data provision. Some multi cloud environment systems also support to provide a consistent streaming data to end users even in high network. The blockchain is an important technique that provide the security for transactional datas

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"Random Forest para identificar los factores sociodemográficos asociados al uso de Internet en el Perú." Revista ECIPeru, December 13, 2018, 54–61. http://dx.doi.org/10.33017/reveciperu2016.0008/.

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Random Forest para identificar los factores sociodemográficos asociados al uso de Internet en el Perú Random Forest to identify sociodemographic factors associated with the use of Internet in Perú Jorge Brian Alarcón Flores y María Estela Ponce Aruneri Facultad de Ciencias Matemáticas, Universidad Nacional Mayor de San Marcos, Lima, Perú. E-mail: jbrianaf@gmail.com, mepa@unmsm.edu.pe Recibido el 10 de julio del 2016; revisado el 14 de noviembre del 2016 y aceptado el 15 de diciembre del 2016 DOI: https://doi.org/10.33017/RevECIPeru2016.0008/ Resumen La sociedad de hoy en día se encuentra vivi

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Pace, Steven. "Revisiting Mackay Online." M/C Journal 22, no. 3 (2019). http://dx.doi.org/10.5204/mcj.1527.

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IntroductionIn July 1997, the Mackay campus of Central Queensland University hosted a conference with the theme Regional Australia: Visions of Mackay. It was the first academic conference to be held at the young campus, and its aim was to provide an opportunity for academics, business people, government officials, and other interested parties to discuss their visions for the development of Mackay, a regional community of 75,000 people situated on the Central Queensland coast (Danaher). I delivered a presentation at that conference and authored a chapter in the book that emerged from its procee

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Maddox, Alexia, and Luke J. Heemsbergen. "Digging in Crypto-Communities’ Future-Making." M/C Journal 24, no. 2 (2021). http://dx.doi.org/10.5204/mcj.2755.

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Introduction This article situates the dark as a liminal and creative space of experimentation where tensions are generative and people tinker with emerging technologies to create alternative futures. Darkness need not mean chaos and fear of violence – it can mean privacy and protection. We define dark as an experimental space based upon uncertainties rather than computational knowns (Bridle) and then demonstrate via a case study of cryptocurrencies the contribution of dark and liminal social spaces to future(s)-making. Cryptocurrencies are digital cash systems that use decentralised (peer-to-

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Cinque, Toija. "A Study in Anxiety of the Dark." M/C Journal 24, no. 2 (2021). http://dx.doi.org/10.5204/mcj.2759.

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Introduction This article is a study in anxiety with regard to social online spaces (SOS) conceived of as dark. There are two possible ways to define ‘dark’ in this context. The first is that communication is dark because it either has limited distribution, is not open to all users (closed groups are a case example) or hidden. The second definition, linked as a result of the first, is the way that communication via these means is interpreted and understood. Dark social spaces disrupt the accepted top-down flow by the ‘gazing elite’ (data aggregators including social media), but anxious users m

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