To see the other types of publications on this topic, follow the link: Social Networks - Data Processing.
Journal articles on the topic 'Social Networks - Data Processing'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Social Networks - Data Processing.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
KRYVENCHUK, Yurii, and Mykhailo-Yurii KHANAS. "ALGORITHM OF DATA MINING AND PROCESSING OF RELATED DATA IN SOCIAL NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 115–18. http://dx.doi.org/10.31891/2307-5732-2022-311-4-115-118.
Full textAbstract:
We live in a time of rapid growth of information technology, which is firmly entrenched in our daily lives. It is simply impossible to imagine a modern person without social networks, because they perform a communicative and informational function, namely: communication, information retrieval, news exchange, etc. Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. So, there is a lot of information available to the user, which is difficult to identify something specific and necessary in the usual way viewing. Accordingly, there is a need for technologies that can quickly process large amounts of data and highlight only the information that is useful to a particular user. This technology called recommender systems. It automatically suggest items to users that might be interesting for them. Due to the desire to unite people with common interests, it is relevant to develop a recommendation system based on social networks that help in personification of the user and compilation of his psychotype using his profile. The paper has description and results of the creation of recommendation system. The basis of this work is one of the algorithms used in recommendation systems – the recommendation system is based on content filtering. It analyzes users’ Twitter posts and calculates their interests. If we consider all the words, our model will not have good results and do not pay attention to what is important to use. Therefore, the most important step is always filtering data, so the number one task is to speed up the time of filtering text and retrieving data from the social network for further processing. The feature of this system is that this algorithm uses parallel calculations and frequency analysis of the text.
2
Yang, Qing, Tigang Jiang, Wenjia Li, Guangchi Liu, Danda B. Rawat, and Jun Wu. "Editorial: Multimedia and Social Data Processing in Vehicular Networks." Mobile Networks and Applications 25, no. 2 (December 14, 2019): 620–22. http://dx.doi.org/10.1007/s11036-019-01432-2.
Full text
3
MELNYK, K. "Processing and protection of personal data in social networks." INFORMATION AND LAW, no. 3(12) (December 23, 2014): 64–69. http://dx.doi.org/10.37750/2616-6798.2014.3(12).272566.
Full text
4
Zadeh, Lotfi A., Ali M. Abbasov, and Shahnaz N. Shahbazova. "Fuzzy-Based Techniques in Human-Like Processing of Social Network Data." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, Suppl. 1 (December 2015): 1–14. http://dx.doi.org/10.1142/s0218488515400012.
Full textAbstract:
Social networks have gained a lot attention. They are perceived as a vast source of information about their users. Variety of different methods and techniques has been proposed to analyze these networks in order to extract valuable information about the users – things they do and like/dislike. A lot of effort is put into improvement of analytical methods in order to grasp a more accurate and detailed image of users. Such information would have an impact on many aspects of everyday life of people – from politics, via professional life, to shopping and entertainment. The theory of fuzzy sets and systems, introduced in 1965, has the ability to handle imprecise and ambiguous information, and to cope with linguistic terms. The theory has evolved into such areas like possibility theory and computing with words. It is very suitable for processing data in a human-like way, and providing the results in a human-oriented manner. The paper presents a short survey of works that use fuzzy-based technologies for analysis of social networks. We pose an idea that fuzzy-based techniques allow for introduction of humancentric and human-like data analysis processes. We include here detailed descriptions of a few target areas of social network analysis that could benefit from applications of fuzzy sets and systems methods.
5
D’Ulizia, Arianna, Patrizia Grifoni, and Fernando Ferri. "Query Processing of Geosocial Data in Location-Based Social Networks." ISPRS International Journal of Geo-Information 11, no. 1 (December 30, 2021): 19. http://dx.doi.org/10.3390/ijgi11010019.
Full textAbstract:
The increasing use of social media and the recent advances in geo-positioning technologies have produced a great amount of geosocial data, consisting of spatial, textual, and social information, to be managed and queried. In this paper, we focus on the issue of query processing by providing a systematic literature review of geosocial data representations, query processing methods, and evaluation approaches published over the last two decades (2000–2020). The result of our analysis shows the categories of geosocial queries proposed by the surveyed studies, the query primitives and the kind of access method used to retrieve the result of the queries, the common evaluation metrics and datasets used to evaluate the performance of the query processing methods, and the main open challenges that should be faced in the near future. Due to the ongoing interest in this research topic, the results of this survey are valuable to many researchers and practitioners by gaining an in-depth understanding of the geosocial querying process and its applications and possible future perspectives.
6
Persico, Valerio, Antonio Pescapé, Antonio Picariello, and Giancarlo Sperlí. "Benchmarking big data architectures for social networks data processing using public cloud platforms." Future Generation Computer Systems 89 (December 2018): 98–109. http://dx.doi.org/10.1016/j.future.2018.05.068.
Full text
7
Bartolini, Ilaria, and Marco Patella. "Real-Time Stream Processing in Social Networks with RAM3S." Future Internet 11, no. 12 (November 29, 2019): 249. http://dx.doi.org/10.3390/fi11120249.
Full textAbstract:
The avalanche of (both user- and device-generated) multimedia data published in online social networks poses serious challenges to researchers seeking to analyze such data for many different tasks, like recommendation, event recognition, and so on. For some such tasks, the classical “batch” approach of big data analysis is not suitable, due to constraints of real-time or near-real-time processing. This led to the rise of stream processing big data platforms, like Storm and Flink, that are able to process data with a very low latency. However, this complicates the task of data analysis since any implementation has to deal with the technicalities of such platforms, like distributed processing, synchronization, node faults, etc. In this paper, we show how the RAM 3 S framework could be profitably used to easily implement a variety of applications (such as clothing recommendations, job suggestions, and alert generation for dangerous events), being independent of the particular stream processing big data platforms used. Indeed, by using RAM 3 S, researchers can concentrate on the development of their data analysis application, completely ignoring the details of the underlying platform.
8
JI, CHANGQING, YU LI, WENMING QIU, YINGWEI JIN, YUJIE XU, UCHECHUKWU AWADA, KEQIU LI, and WENYU QU. "BIG DATA PROCESSING: BIG CHALLENGES AND OPPORTUNITIES." Journal of Interconnection Networks 13, no. 03n04 (September 2012): 1250009. http://dx.doi.org/10.1142/s0219265912500090.
Full textAbstract:
With the rapid growth of emerging applications like social network, semantic web, sensor networks and LBS (Location Based Service) applications, a variety of data to be processed continues to witness a quick increase. Effective management and processing of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing techniques from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including definition of big data, big data management platform, big data service models, distributed file system, data storage, data virtualization platform and distributed applications. Following the MapReduce parallel processing framework, we introduce some MapReduce optimization strategies reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.
9
Zhang, Qian, Jingyao Li, Hongyao Zhao, Quanqing Xu, Wei Lu, Jinliang Xiao, Fusheng Han, Chuanhui Yang, and Xiaoyong Du. "Efficient Distributed Transaction Processing in Heterogeneous Networks." Proceedings of the VLDB Endowment 16, no. 6 (February 2023): 1372–85. http://dx.doi.org/10.14778/3583140.3583153.
Full textAbstract:
Countrywide and worldwide business, like gaming and social networks, drives the popularity of inter-data-center transactions. To support inter-data-center transaction processing and data center fault tolerance simultaneously, existing protocols suffer from significant performance degradation due to high-latency and unstable networks. In this paper, we propose RedT, a novel distributed transaction processing protocol that works in heterogeneous networks. In detail, nodes within a data center are inter-connected via the RDMA-capable network and nodes across data centers are inter-connected via TCP/IP networks. RedT extends two-phase commit (2PC) by decomposing transactions into sub-transactions in terms of the data center granularity, and proposing a pre-write-log mechanism that is able to reduce the number of inter-data-center round-trips from a maximal of 6 to 2. Extensive evaluation against state-of-the-art protocols shows that RedT can achieve up to 1.57× higher throughputs and 0.56× lower latency.
10
Pronichev, A. P. "Architecture of a distributed system for processing heterogeneous data from social networks." Informatization and communication 4 (November 2020): 97–100. http://dx.doi.org/10.34219/2078-8320-2020-11-4-97-100.
Full textAbstract:
The article discusses the architecture of a system for collecting and analyzing heterogeneous data from social networks. This architecture is a distributed system of subsystem modules, each of which is responsible for a separate task. The system also allows you to use external systems for data analysis, providing the necessary interface abstraction for connection. This allows for more flexible customization of the data analysis process and reduces development, implementation and support costs.
11
Chengar, O. V., V. I. Shevchenko, E. N. Maschenko, D. V. Moiseev, and A. S. Soina. "Strategy for primary processing of social networks data using hierarchy analysis method." Journal of Physics: Conference Series 1679 (November 2020): 022082. http://dx.doi.org/10.1088/1742-6596/1679/2/022082.
Full text
12
Kewen, Liu, and Gao Changyuan. "The Framework of Social Networks Big Data Processing Based on Cloud Computing." International Journal of Database Theory and Application 9, no. 10 (October 31, 2016): 189–98. http://dx.doi.org/10.14257/ijdta.2016.9.10.16.
Full text
13
Tanoli, Irfan Khan, Imran Amin, Faraz Junejo, and Nukman Yusoff. "Systematic Machine Translation of Social Network Data Privacy Policies." Applied Sciences 12, no. 20 (October 18, 2022): 10499. http://dx.doi.org/10.3390/app122010499.
Full textAbstract:
With the growing popularity of online social networks, one common desire of people is to use of social networking services for establishing social relations with others. The boom of social networking has transformed common users into content (data) contributors. People highly rely on social sites to share their ideas and interests and express opinions. Social network sites store all such activities in a data form and exploit the data for various purposes, e.g., marketing, advertisements, product delivery, product research, and even sentiment analysis, etc. Privacy policies primarily defined in Natural Language (NL) specify storage, usage, and sharing of the user’s data and describe authorization, obligation, or denial of specific actions under specific contextual conditions. Although these policies expressed in Natural Language (NL) allow users to read and understand the allowed (or obliged or denied) operations on their data, the described policies cannot undergo automatic control of the actual use of the data by the entities that operate on them. This paper proposes an approach to systematically translate privacy statements related to data from NL into a controlled natural one, i.e., CNL4DSA to improve the machine processing. The methodology discussed in this work is based on a combination of standard Natural Language Processing (NLP) techniques, logic programming, and ontologies. The proposed technique is demonstrated with a prototype implementation and tested with policy examples. The system is tested with a number of data privacy policies from five different social network service providers. Predominantly, this work primarily takes into account two key aspects: (i) The translation of social networks’ data privacy policy and (ii) the effectiveness and efficiency of the developed system. It is concluded that the proposed system can successfully and efficiently translate any common data policy based on an empirical analysis performed of the obtained results.
14
Mussina, A. B., S. S. Aubakirov, and P. Trigo. "Architecture for enduring knowledge-extraction from online social networks." BULLETIN of the L N Gumilyov Eurasian National University MATHEMATICS COMPUTER SCIENCE MECHANICS Series 140, no. 3 (2022): 23–32. http://dx.doi.org/10.32523/2616-7182/bulmathenu.2022/3.3.
Full textAbstract:
Nowadays social networks and media play significant role in daily life. All our life in the real world is recorded in the digital space as well. Scientists have enormous potential in researching issues such as social influence ontop news and top news influence on society. Its impact on daily life spans such diverse areas as digital marketing, publicopinion analysis, political monitoring and disaster notification. Any task of processing such a large data stream needs acoherent architecture that will fit the analyzed resource. In the presented work, we set ourselves the task of creating ahighly loaded, fault-tolerant, scalable system for extracting and processing data from various social networks and analyzing data in real time. The solution is architecture in the form of a set of modules. Modules have their own characteristics depending on the work performed, from collecting textual data to direct processing and extraction of knowledge.
15
Mukhin, A. S., I. A. Rytsarev, R. A. Paringer, A. V. Kupriyanov, and D. V. Kirsh. "Determining the proximity of groups in social networks based on text analysis using big data." Information Technology and Nanotechnology, no. 2416 (2019): 521–26. http://dx.doi.org/10.18287/1613-0073-2019-2416-521-526.
Full textAbstract:
The article is devoted to the definition of such groups in social networks. The object of the study was selected data social network Vk. Text data was collected, processed and analyzed. To solve the problem of obtaining the necessary information, research was conducted in the field of optimization of data collection of the social network Vk. A software tool that provides the collection and subsequent processing of the necessary data from the specified resources has been developed. The existing algorithms of text analysis, mainly of large volume, were investigated and applied.
16
Gawade, Shriya, Riya Sawant, Aakash Rathod, and Prof Chhaya Dhavale. "Psychological Analysis Using Social Media Data." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1936–41. http://dx.doi.org/10.22214/ijraset.2022.41510.
Full textAbstract:
Abstract: Mental Stress is an important aspect of our life that is given the least importance. We tend to ignore the fact that we need to be emotionally stable along with physical stability. To keep your mental state sound, we proposed this system where the psychological state of a person is being predicted. One such place where a person comes up and shares his/her thoughts, through texts is on social media with their friends. To detect such a state, we made use of NLP techniques accompanied by a reliable scale, the Perceived Stress Scale (PSS) developed by Cohen, Kamarck and Mermelstein. The huge texts were cleaned using text processing methods. In Machine Learning, there are many ways for sentimental analysis such: decision-based systems, Bayesian classifiers, support vector machines, neural networks and sample-based methods. We have performed sentimental analysis and in order to give the severity of the condition we made use of the Perceived Stress Scale (PSS). The model will be predicting whether the given text indicates stress or not and further classifies it as low, medium or high-level stress. Keywords: TF-IDF, Natural Language Processing (NLP), Stress, WordCloud, Perceived Stress Scale
17
Gawade, Shriya, Riya Sawant, Aakash Rathod, and Prof Chhaya Dhavale. "Psychological Analysis Using Social Media Data." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1936–41. http://dx.doi.org/10.22214/ijraset.2022.41510.
Full textAbstract:
Abstract: Mental Stress is an important aspect of our life that is given the least importance. We tend to ignore the fact that we need to be emotionally stable along with physical stability. To keep your mental state sound, we proposed this system where the psychological state of a person is being predicted. One such place where a person comes up and shares his/her thoughts, through texts is on social media with their friends. To detect such a state, we made use of NLP techniques accompanied by a reliable scale, the Perceived Stress Scale (PSS) developed by Cohen, Kamarck and Mermelstein. The huge texts were cleaned using text processing methods. In Machine Learning, there are many ways for sentimental analysis such: decision-based systems, Bayesian classifiers, support vector machines, neural networks and sample-based methods. We have performed sentimental analysis and in order to give the severity of the condition we made use of the Perceived Stress Scale (PSS). The model will be predicting whether the given text indicates stress or not and further classifies it as low, medium or high-level stress. Keywords: TF-IDF, Natural Language Processing (NLP), Stress, WordCloud, Perceived Stress Scale
18
Li, Zhong, Cheng Wang, Siqian Yang, Changjun Jiang, and Xiangyang Li. "LASS: Local-Activity and Social-Similarity Based Data Forwarding in Mobile Social Networks." IEEE Transactions on Parallel and Distributed Systems 26, no. 1 (January 1, 2015): 174–84. http://dx.doi.org/10.1109/tpds.2014.2308200.
Full text
19
Pari N., Sheba, and Dr Senthil Kumar K. "Framework for Cyber Threats in Social Networks." International Journal of Engineering and Advanced Technology 11, no. 6 (August 30, 2022): 128–33. http://dx.doi.org/10.35940/ijeat.f3762.0811622.
Full textAbstract:
Social networking is the most common way of communication nowadays. Maintaining the information’s confidentiality, integrity and availability becomes a very critical aspect. As the number of users on social media keep increasing, the amount of data about the users are available on the network is also increasing. Attacks on these networks are currently at an all-time high which can be by Phishing attacks, Botnets, Sybil Attack, Profile Cloning, Spam, Denial of service to name a few of them. There are a number of threats possible on social networks. Data in social networks must be protected from various types of cyber-attacks. The main requirement is providing security to such networks. Maintaining the information’s confidentiality, integrity and availability becomes a very critical aspect. As and when security is being provided to these networks, attacks are also evolving. Cyber-attacks are becoming complex which means that sometimes the threat for which the solution needs to be found is unknown. Threats are becoming automated, hence, using less efficient algorithms for cyber security is not the optimal solution. Hence, machine learning is used to support cyber security to social networks. A framework is built which comprises of the steps such as Data Collecting, Data Preparing, Applying Machine Learning Techniques, Post-processing by applying domain specific knowledge to build a secure system for social networks using machine learning techniques.
20
Chesnokov, V. O. "Software for Crawling and Analysis of Ego-Network Graphs from Social Networking Services." Mechanical Engineering and Computer Science, no. 8 (October 22, 2018): 34–44. http://dx.doi.org/10.24108/0818.0001427.
Full textAbstract:
Online social networks are one of the main platforms for arbitrary subjects of discussion. They are one of the main sources of data to analyse public opinion. For crawling and analysis of data from online social networks, are used data monitoring systems, which include a data collecting system. A typical system for collecting data from the Internet contains a crawler, parsers, a collection queue of tasks, a task scheduling subsystem, and a module for writing structured data to a storage system. The crawling from online social networks has a number of features. The paper considers methods of access to data from online social networks and a task planning subsystem. Formulates and underpins the requirements for a data collecting system to provide crawl results from online social networks, namely scalability, extensibility, and availability of a data storage subsystem and a queue of collection tasks.Describes main data accessing methods to have information from online social networks: API-based access, access through processing of HTML-pages and specialised interfaces for bots. Provides a description of main restrictions, which an online social network imposes, namely the need to register the application, the limited number of requests, the need to obtain user‘s permission to collect his (her) data. According to the analysis results, the anonymous download and processing of HTML pages were chosen, as a data access method.Formulates the task subsystem requirements, namely available types, hierarchy, and context of the task to be done. Describes the general architecture of the developed software system for crawling and analysis of data from online social networks, justifies its compliance with the earlier raised requirements.The problem of crawling and analysis of users’ ego-network graphs (sub-graphs of a social graph) are considered. Their collecting features are described and options of implementation are proposed depending on the amount of data collected.The results obtained can be used to build monitoring systems for online social networks and collect test data for experimentally estimated algorithms of social graphs analysis. Further development may be concerned with a detailed consideration of the problems of collecting other types of data from online social networks.
21
Lei, Tianliang, Lixin Ji, Gengrun Wang, Shuxin Liu, Lan Wu, and Fei Pan. "Transformer-Based User Alignment Model across Social Networks." Electronics 12, no. 7 (April 3, 2023): 1686. http://dx.doi.org/10.3390/electronics12071686.
Full textAbstract:
Cross-social network user identification refers to finding users with the same identity in multiple social networks, which is widely used in the cross-network recommendation, link prediction, personality recommendation, and data mining. At present, the traditional method is to obtain network structure information from neighboring nodes through graph convolution, and embed social networks into the low-dimensional vector space. However, as the network depth increases, the effect of the model will decrease. Therefore, in order to better obtain the network embedding representation, a Transformer-based user alignment model (TUAM) across social networks is proposed. This model converts the node information and network structure information from the graph data form into sequence data through a specific encoding method. Then, it inputs the data to the proposed model to learn the low-dimensional vector representation of the user. Finally, it maps the two social networks to the same feature space for alignment. Experiments on real datasets show that compared with GAT, TUAM improved ACC@10 indicators by 11.61% and 16.53% on Facebook–Twitter and Weibo–Douban datasets, respectively. This illustrates that the proposed model has a better performance compared to other user alignment models.
22
Khodijah, I., F. Lestari, T. Setiawan, S. N. Habibah, A. Zulfikar, and L. H. Lumbantoruan. "The Institutional Social Role for Maritim Village’s Food Stability." IOP Conference Series: Earth and Environmental Science 1148, no. 1 (March 1, 2023): 012033. http://dx.doi.org/10.1088/1755-1315/1148/1/012033.
Full textAbstract:
Abstract This study aims to explore social networks and interactions between networks in strengthening food stability in maritime villages in coastal areas. The geographical condition of the region has become one of the triggers for food instability and food security problems in coastal areas. This study uses the method of Social Network Analysis (SNA). Data processing uses R to visualize the role of stakeholder centrality. The analysis used is the analysis of baseline network, degree, and betweenness centrality. The value of modularity is an indicator of increasing or decreasing community networks. The number of nodes and links identifies the actors in the network and the interactions between actors. The results showed that social networks have an essential role in food stability. The private sector is a key actor in food stability in maritime villages. Is indicated by the modularity value of 0.43 (>0), which connects to 30 social network nodes and 62 links to food stability in maritime villages in the coastal area of Bintan Regency.
23
Bozhenko, Victoria, Serhii Mynenko, and Artem Shtefan. "Financial Fraud Detection on Social Networks Based on a Data Mining Approach." Financial Markets, Institutions and Risks 6, no. 4 (2022): 119–24. http://dx.doi.org/10.21272/fmir.6(4).119-124.2022.
Full textAbstract:
The article summarizes the arguments and counter-arguments within the scientific debate on the issue of researching financial frauds in the Internet. The main goal of the research is to develop methodological principles for identifying financial cyber fraud in social networks based on the analysis of comments to identify relevant text patterns that may indicate manipulation attempts and further fraud. The urgency of solving this scientific problem is due to the fact that the mass involvement of Internet users in social interactions in the virtual environment has contributed to the development of various criminal schemes, as well as personal data that is initially entered during registration and information that is published in social networks can be used by a fraudster to carry out illegal financial transactions. The study of the issue of identifying financial fraud in social networks in the article is carried out in the following logical sequence: collecting comments with a corresponding request under publications in the social network using the Instaloader tool; combining comments into groups based on content similarity; conducting preliminary processing of text data (decomposing the text into simpler components (tokens) and reducing similar word forms to their main dictionary form); determination of the level of similarity of text data using the cosine of similarity; building clusters of text data that can indicate the presence of signs of financial fraud under relevant comments in social networks. Instagram was chosen to identify fraudulent operations in social networks. The analysis of comments on the social network Instagram to identify text patterns showed that offers and appeals from specific groups of people and promoted in comments with the help of spam are dangerous. Based on the results of the study, it was concluded that national regulators need to strengthen public control of the Internet, as well as improve the security system at the technical level by using the latest machine learning methods to identify attempts to commit illegal actions with the subsequent imposition of sanctions on such users in social networks.
24
Rudikova, L. V., and O. R. Myslivec. "About a concept of creating a social network users information aggregation and data processing system." «System analysis and applied information science», no. 4 (February 6, 2019): 65–72. http://dx.doi.org/10.21122/2309-4923-2018-4-65-72.
Full textAbstract:
The development of a general concept and implementation of a data-storage and analysis system for practice oriented data, one of the subsystems of which is an analytical system for the accumulation and analysis of data from users of social networks, is topical. The development of a general concept and implementation of a data-storage and analysis system for practice oriented data, one of the subsystems of which is an analytical system for the accumulation and analysis of data from users of social networks, is topical. Data that users leave about themselves in social networks can be useful in solving various tasks. The proposed article describes the subject area associated with the collection and storage of data from users of social networks. Proceeding from the subject area, the general architecture of the universal data collection and storage system is proposed, which is based on the client-server architecture. For the server side of the system, a fragment of the data model is provided, which is associated with the accumulation of data from external sources. The framework of the system architecture is described. The developed universal system is based on the information technology of data warehousing, and it has the following aspects: an expandable complex subject area, the integration of stored data that come from various sources, the invariance of stored data in time with mandatory labels, relatively high data stability, the search for necessary trade-off in data redundancy, modularity of individual system units, fl and extensibility of the architecture, high security requirements vulnerable data.The proposed system organizes the process of collecting data and filling the database from external sources. To do this, the system has a module for collecting and converting information from third-party Internet sources and sending them to the database. The system is intended for various users interested in analyzing data of users of social networks.
25
Коshimbay, A. B., and A. N. Moldagulova. "Исследование метода анализа и обработки данных социальных сетей с целью определения тональности." INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES, no. 8(8) (March 4, 2022): 60–66. http://dx.doi.org/10.54309/ijict.2021.8.8.007.
Full textAbstract:
A wide spread of social online services and the advancement of Big Data technologies poses a challenge to utilize data from social media in numerous circles. Nowadays, the «social listening» and substance examination advances pick up ubiquity in Data Science. The sentiment analysis of the text is one of the especially important tasks in the fi eld of natural language processing. It is used in diff erent spheres. This article discusses the main methods of identifying emotions in text data and analyzes the current achievements in the fi eld of computer analysis of emotions in text data. At the moment, there are many unresolved problems in the fi eld of automatic text analysis to determine the emotional coloring of the vocabulary in social media texts. Быстрое распространение общественных онлайн-сервисов и эволюция технологий «Больших данных» инициировали внимание к применению сведений из общественных сеток во всевозможных секторах экономики. На сегодняшний момент, известность технологии завоевывают, технологии как «прогноз социальных сетей» (social listening) и контент анализа. В особенности анализ тональности текста является одной из важных задач в области обработки естественного языка. Необходимо подчеркнуть, что данная технология применяется в разных областях. В предоставленной статье рассматриваются основные методы идентификации эмоций в текстовых данных. Исследованы и проанализированы существующие достижения в области компьютерного анализа эмоций в текстах. В результате исследования, на данный момент существует множество нерешенных проблем в области автоматического анализа для определения эмоциональной окраски текстов в социальных сетях.
26
Sarwani, Mohammad Zoqi, Dian Ahkam Sani, and Fitria Chabsah Fakhrini. "Personality Classification through Social Media Using Probabilistic Neural Network Algorithms." International Journal of Artificial Intelligence & Robotics (IJAIR) 1, no. 1 (October 31, 2019): 9. http://dx.doi.org/10.25139/ijair.v1i1.2025.
Full textAbstract:
Today the internet creates a new generation with modern culture that uses digital media. Social media is one of the popular digital media. Facebook is one of the social media that is quite liked by young people. They are accustomed to conveying their thoughts and expression through social media. Text mining analysis can be used to classify one's personality through social media with the probabilistic neural network algorithm. The text can be taken from the status that is on Facebook. In this study, there are three stages, namely text processing, weighting, and probabilistic neural networks for determining classification. Text processing consists of several processes, namely: tokenization, stopword, and steaming. The results of the text processing in the form of text are given a weight value to each word by using the Term Inverse Document Frequent (TF / IDF) method. In the final stage, the Probabilistic Neural Network Algorithm is used to classify personalities. This study uses 25 respondents, with 10 data as training data, and 15 data as testing data. The results of this study reached an accuracy of 60%.
27
Подвесовский, Александр, Aleksandr Podvesovskiy, Дмитрий Будыльский, and Dmitriy Budylskiy. "Questions and features of automated social networks monitoring with support of intelligent user messages analysis." Bulletin of Bryansk state technical university 2014, no. 4 (December 5, 2014): 146–52. http://dx.doi.org/10.12737/23095.
Full textAbstract:
An opinion mining monitoring model for social networks introduced. The model includes text mining processing over social network data and uses sentiment analysis approach in particular. Practical usage results of software implementation and its requirements described as well as further research directions.
28
Suat-Rojas, Nestor, Camilo Gutierrez-Osorio, and Cesar Pedraza. "Extraction and Analysis of Social Networks Data to Detect Traffic Accidents." Information 13, no. 1 (January 10, 2022): 26. http://dx.doi.org/10.3390/info13010026.
Full textAbstract:
Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods.
29
Vlasova, Yulia. "Using social networks for social upbringing." Education & Self Development 16, no. 3 (September 30, 2021): 278–88. http://dx.doi.org/10.26907/esd.16.3.24.
Full textAbstract:
The article addresses the potential of using social networks in social education and for solving the problems of youth socialization. There is an urgent social need for the harmonious development of the child's personality, including in the Internet environment. At the same time, there is insufficient knowledge about the organization of education in social networks in science. The article identifies the experience of successful interaction between teachers and students on the Internet and ways of organizing event situations in social networks. The study was conducted in February-October 2020 and analysed the content of open official accounts of educational organizations in the social networks ‘VKontakte’, Instagram and documents of educational organizations. The complex use of observation methods, quantitative data processing, expert assessments was aimed at identifying forms of educational activity that are promising for implementation online in social networks. The study showed that, despite the variety of topics and styles, the content of official school accounts resembles a list of news about holidays and other public events. The accounts do not contain materials that could cause vivid emotions and sensations in children, become a source of experiences, value attitudes, experience of interacting with people. Consequently, schools do not provide children with opportunities for self-knowledge, self-determination, self-realization, and do not support their initiatives in social networks. The article recommends that schools expand the practice of organizing networked educational events. For this, they need to create groups of different ages for children and adults for joint planning, organizing, conducting, summing up creative deeds. The maintenance of a thematic Instagram account of a school is offered as an example of a successful subject-subject interaction between a teacher-educator and students in social networks.
30
Nguyen, Van Quan, Tien Nguyen Anh, and Hyung-Jeong Yang. "Real-time event detection using recurrent neural network in social sensors." International Journal of Distributed Sensor Networks 15, no. 6 (June 2019): 155014771985649. http://dx.doi.org/10.1177/1550147719856492.
Full textAbstract:
We proposed an approach for temporal event detection using deep learning and multi-embedding on a set of text data from social media. First, a convolutional neural network augmented with multiple word-embedding architectures is used as a text classifier for the pre-processing of the input textual data. Second, an event detection model using a recurrent neural network is employed to learn time series data features by extracting temporal information. Recently, convolutional neural networks have been used in natural language processing problems and have obtained excellent results as performing on available embedding vector. In this article, word-embedding features at the embedding layer are combined and fed to convolutional neural network. The proposed method shows no size limitation, supplementation of more embeddings than standard multichannel based approaches, and obtained similar performance (accuracy score) on some benchmark data sets, especially in an imbalanced data set. For event detection, a long short-term memory network is used as a predictor that learns higher level temporal features so as to predict future values. An error distribution estimation model is built to calculate the anomaly score of observation. Events are detected using a window-based method on the anomaly scores.
31
Jiang, Yanji, Xueli Shen, and Sifa Zheng. "An Effective Data Sharing Scheme Based on Blockchain in Vehicular Social Networks." Electronics 10, no. 2 (January 7, 2021): 114. http://dx.doi.org/10.3390/electronics10020114.
Full textAbstract:
Vehicular social networks (VSNs) are the vehicular ad hoc networks (VANETs) that integrate social networks. Compared with traditional VANETs, VSNs are more suitable to serve a group of vehicles with common interests. In VSNs, vehicles can upload the necessary data in the cloud service provider (CSP) and other vehicles can query the data they are interested in through CSP, which enables VSNs to provide more user-friendly services. However, due to the wireless network communication environment, the data sent by the vehicle can easily be monitored. Adversaries are able to violate the privacy of the vehicle based on the collected data, thereby threatening the security of the entire network. In addition, if a vehicle shares malicious or false data with other vehicles, it is easy to mislead drivers and even cause serious traffic accidents. This paper proposes an effective data sharing scheme based on blockchain in VSNs. By integrating an identity based signature mechanism and pseudonym generation mechanism, we first propose an anonymous authentication mechanism as the basis for establishing trust relationships before data transmission between entities in VSNs. Then, a data sharing scheme based on blockchain is described, in which the signature mechanism and the consensus mechanism guarantee the security and traceability of data. The result of the performance analysis and the simulation experiment indicate that VAB can achieve a favourable performance compared with existing schemes.
32
Bhatti, Uzair Aslam, Hao Tang, Guilu Wu, Shah Marjan, and Aamir Hussain. "Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence." International Journal of Intelligent Systems 2023 (February 28, 2023): 1–28. http://dx.doi.org/10.1155/2023/8342104.
Full textAbstract:
Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real-life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge-based graph, and other areas due to their excellent characteristics in processing non-European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph-based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.
33
Fang, Zhiyuan, Liu Chang, Jingwen Luo, and Jia Wu. "A Data Transmission Algorithm Based on Triangle Link Structure Prediction in Opportunistic Social Networks." Electronics 10, no. 9 (May 10, 2021): 1128. http://dx.doi.org/10.3390/electronics10091128.
Full textAbstract:
With the popularization of 5G communications, the scale of social networks has grown rapidly, and the types of messages have become increasingly complex. The rapid increases in the number of access nodes and the amount of data have put a greater burden on the transmission of information in the networks. However, when transferring data from a large number of users, the performance of traditional opportunistic network routing algorithms is insufficient, which often leads to problems such as high energy consumption, network congestion, and data packet loss. The way in which to improve this transmission environment has become a difficult task. Therefore, in order to ensure the effective transmission of data and reduce network congestion, this paper proposed a link prediction model based on triangular relationships in opportunistic social networks (LPMBT). In the topological structure of the social network, the algorithm scores links based on the frequency of use and selects the optimal relay node based on the score. It can also efficiently track the target node and reconstruct the sub-community. The simulation experimental results showed that the algorithm had excellent performance, effectively reduced overhead, reduced the end-to-end delay, and greatly improved the data transfer rate in the opportunistic network.
34
Grunspan, Daniel Z., Benjamin L. Wiggins, and Steven M. Goodreau. "Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research." CBE—Life Sciences Education 13, no. 2 (June 2014): 167–78. http://dx.doi.org/10.1187/cbe.13-08-0162.
Full textAbstract:
Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data.
35
Hassan, Alaa Abdelraheem, and Tarig Mohammed Hassan. "Real-Time Big Data Analytics for Data Stream Challenges: An Overview." European Journal of Information Technologies and Computer Science 2, no. 4 (July 25, 2022): 1–6. http://dx.doi.org/10.24018/compute.2022.2.4.62.
Full textAbstract:
The conventional approach of evaluating massive data is inappropriate for real-time analysis; therefore, analysing big data in a data stream remains a critical issue for numerous applications. It is critical in real-time big data analytics to process data at the point where they are arriving at a quick reaction and good decision making, necessitating the development of a novel architecture that allows for real-time processing at high speed and low latency. Processing and anlayzing a data stream in real-time is critical for a variety of applications; however, handling a large amount of data from a variety of sources, such as sensor networks, web traffic, social media, video streams, and other sources, is a considerable difficulty. The main goal of this paper is to give an overview of the current architecture for real time big data analytics, real-time data stream processing methods available, including their system architectures Lambda, kappa, and delta large data stream processing.
36
Cao, Xin, Peng Li, Xiaozhi Huang, and Limin Fan. "The dual mechanism of social networks on the relationship between internationalization and firm performance: Empirical evidence from china." PLOS ONE 17, no. 2 (February 11, 2022): e0263674. http://dx.doi.org/10.1371/journal.pone.0263674.
Full textAbstract:
The effects of social networks on the relationship between internationalization and firm performance have been well documented in the international literature, and two dimensions of social networks have also been identified: business ties and political ties. However, few efforts have been made to examine whether there are different mechanisms of business ties and political ties. Based on social network theory and boundary spanning theory, we build a model of a dual mechanism of social networks, and the business ties and political ties of social networks that correspond with information processing and the external representation of boundary spanning theory. Using the data of Chinese listed companies in 2005–2013 and 2013–2017 to test the model, the results indicate that (1) in the relationship between internationalization and firm performance, the role of social networks has a dual mechanism. (2) Business ties play a mediating role in the relationship between internationalization and firm performance. Business ties are conducive to a company’s information acquisition and knowledge sharing and play the role of information processing. (3) Political ties play a U-shaped moderating role in the relationship between internationalization and firm performance and assume the role of external representation.
37
Xiaokaiti, Aizimaiti, Yurong Qian, and Jia Wu. "Efficient Data Transmission for Community Detection Algorithm Based on Node Similarity in Opportunistic Social Networks." Complexity 2021 (May 28, 2021): 1–18. http://dx.doi.org/10.1155/2021/9928771.
Full textAbstract:
With the rapid development of 5G era, the number of messages on the network has increased sharply. The traditional opportunistic networks algorithm has some shortcomings in processing data. Most traditional algorithms divide the nodes into communities and then perform data transmission according to the divided communities. However, these algorithms do not consider enough nodes’ characteristics in the communities’ division, and two positively related nodes may divide into different communities. Therefore, how to accurately divide the community is still a challenging issue. We propose an efficient data transmission strategy for community detection (EDCD) algorithm. When dividing communities, we use mobile edge computing to combine network topology attributes with social attributes. When forwarding the message, we select optimal relay node as transmission according to the coefficients of channels. In the simulation experiment, we analyze the efficiency of the algorithm in four different real datasets. The results show that the algorithm has good performance in terms of delivery ratio and routing overhead.
38
Bejarano-Luque, Juan Luis, Matías Toril, Mariano Fernández-Navarro, Luis Roberto Jiménez, and Salvador Luna-Ramírez. "Statistical Model for Mobile User Positioning Based on Social Information." Electronics 10, no. 15 (July 26, 2021): 1782. http://dx.doi.org/10.3390/electronics10151782.
Full textAbstract:
In spite of the vast set of measurements provided by current mobile networks, cellular operators have problems pinpointing problematic locations because the origin of such measurements (i.e., user location) is usually not registered. At the same time, social networks generate a huge amount of data that can be used to infer population density. In this work, a data-driven model is proposed to deduce the statistical distribution of connections, exploiting the knowledge of network layout and population density in the scenario. Due to the absence of GPS measurements, the proposed method combines data from radio connection traces stored in the network management system and geolocated posts from social networks. This information is enriched with user context information inferred from data traffic attributes. The method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database of geotagged posts from social networks collected in real-time.
39
Tertyshnikova, A. G., U. O. Pavlova, and M. V. Cimbal. "Social exclusion as a side effect of machine learning mechanisms." Digital Sociology 5, no. 4 (January 31, 2023): 23–30. http://dx.doi.org/10.26425/2658-347x-2022-5-4-23-30.
Full textAbstract:
The development of neural network technologies leads to their integration in decision-making processes at the level of such important social institutions as healthcare, education, employment, etc. This situation brings up the question of the correctness of artificial intelligence decisions and their consequences. The aim of this work is to consider the origin and replication of social exclusion, inequality and discrimination in society as a result of neurotraining. Neurotraining understood as the principles of any neural networks’ training. Social exclusion and the resulting discrimination in decisions made by artificial intelligence is considered as a consequence of the big data processing principles. The authors review the theories of foreign and Russian authors concerning the impact of artificial intelligence on strengthening the existing social order, as well as problems with processing and interpreting data for training computer systems on them. Real situations of the specifics of the data itself and its processing that have led to increased inequality and exclusion are also given. The conclusion about the sources of social exclusion and stigmatization in society is made due to the similarity between natural and artificial neural networks functioning. The authors suggest that it is the principles of neurotraining in a “natural” society that lead not only to discrimination at the macro level, but also cause vivid negative reactions towards representatives of the exclusive groups, for example, interethnic hatred, homophobia, sexism, etc. The question about the possibility of studying “natural” society in comparison with “artificial” one is raised.
40
Lanza-Cruz, Indira, Rafael Berlanga, and María Aramburu. "Modeling Analytical Streams for Social Business Intelligence." Informatics 5, no. 3 (August 1, 2018): 33. http://dx.doi.org/10.3390/informatics5030033.
Full textAbstract:
Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network’s contents and the company’s analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector.
41
Cheng, Yong, Jie Du, Yonggang Yang, Zhibao Ma, Ning Li, Jia Zhao, and Di Wu. "Designing Collaborative Edge Computing for Electricity Heterogeneous Data Based on Social IoT Systems." International Journal of Distributed Systems and Technologies 13, no. 7 (July 12, 2022): 1–22. http://dx.doi.org/10.4018/ijdst.307955.
Full textAbstract:
Power generation, transmission, maintenance costs, and electricity prices are heavily influenced by accurate load forecasts at energy suppliers' operation centers. Every aspect of our life has been transformed by the social internet of things (SIoT). Collaborative edge computing (CEC) has emerged as a new paradigm for meeting the demands of the internet of things by alleviating resource congestion (IoT). Remote devices can connect to CEC's processing, storage, and network resources. About short-term electrical load forecasting, this study explores the application of feed-forward deep neurological networking (FF-DNN) and recurrent deep neuronal networking (R-DNN) methods and analyzes their accuracy and computing performance. A dynamic prediction system using a deep neural network (DPS-DNN) is proposed in this research. The recently unveiled smartgrid with the results shows the higher performance of the proposed DPS-DNN model than the existing models with an enhancement of 93.15% based on collaborative edge networks based on SIoT.
42
Lu, Yu, Liu Chang, Jingwen Luo, and Jia Wu. "Routing Algorithm Based on User Adaptive Data Transmission Scheme in Opportunistic Social Networks." Electronics 10, no. 10 (May 11, 2021): 1138. http://dx.doi.org/10.3390/electronics10101138.
Full textAbstract:
With the rapid popularization of 5G communication and internet of things technologies, the amount of information has increased significantly in opportunistic social networks, and the types of messages have become more and more complex. More and more mobile devices join the network as nodes, making the network scale increase sharply, and the tremendous amount of datatransmission brings a more significant burden to the network. Traditional opportunistic social network routing algorithms lack effective message copy management and relay node selection methods, which will cause problems such as high network delay and insufficient cache space. Thus, we propose an opportunistic social network routing algorithm based on user-adaptive data transmission. The algorithm will combine the similarity factor, communication factor, and transmission factor of the nodes in the opportunistic social network and use information entropy theory to adaptively assign the weights of decision feature attributes in response to network changes. Also, edge nodes are effectively used, and the nodes are divided into multiple communities to reconstruct the community structure. The simulation results show that the algorithm demonstrates good performance in improving the information transmission’s success rate, reducing network delay, and caching overhead.
43
Chakraborty, Chinmay, Shaohua Wan, and Mohammad R. Khosravi. "Editorial: Ontology-based Knowledge Presentation and Computational Linguistics for Semantic Big Social Data Analytics in Asian Social Networks." ACM Transactions on Asian and Low-Resource Language Information Processing 22, no. 5 (May 9, 2023): 1–3. http://dx.doi.org/10.1145/3594719.
Full textAbstract:
Data-driven ontology-based knowledge (OK) presentation and computational linguistics for evolving semantic Asian social networks (ASNs) can make one of the most important platforms that provide robust and real-time data mapping in massive access across the heterogeneous big data sources in the web that is named OK-ASN. It benefits from computational intelligence, web-of-things (WoT) architecture, semantic features, statistical learning and pattern recognition, database management, computer vision, cyber-security, and language processing. OK-ASN is a critical strategy for WoT big data mining and enterprises from social media to medical and industrial sectors.
44
Zhang, Kainan, Zhipeng Cai, and Daehee Seo. "Privacy-Preserving Federated Graph Neural Network Learning on Non-IID Graph Data." Wireless Communications and Mobile Computing 2023 (February 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/8545101.
Full textAbstract:
Since the concept of federated learning (FL) was proposed by Google in 2017, many applications have been combined with FL technology due to its outstanding performance in data integration, computing performance, privacy protection, etc. However, most traditional federated learning-based applications focus on image processing and natural language processing with few achievements in graph neural networks due to the graph’s nonindependent identically distributed (IID) nature. Representation learning on graph-structured data generates graph embedding, which helps machines understand graphs effectively. Meanwhile, privacy protection plays a more meaningful role in analyzing graph-structured data such as social networks. Hence, this paper proposes PPFL-GNN, a novel privacy-preserving federated graph neural network framework for node representation learning, which is a pioneer work for graph neural network-based federated learning. In PPFL-GNN, clients utilize a local graph dataset to generate graph embeddings and integrate information from other collaborative clients to utilize federated learning to produce more accurate representation results. More importantly, by integrating embedding alignment techniques in PPFL-GNN, we overcome the obstacles of federated learning on non-IID graph data and can further reduce privacy exposure by sharing preferred information.
45
Wang, Yingxu, and Victor J. Wiebe. "Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks." International Journal of Cognitive Informatics and Natural Intelligence 8, no. 3 (July 2014): 29–44. http://dx.doi.org/10.4018/ijcini.2014070103.
Full textAbstract:
Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.
46
MacFadden, Robert James. "The Microcomputer Millennium: Transforming the Small Social Agency." Social Casework 67, no. 3 (March 1986): 160–65. http://dx.doi.org/10.1177/104438948606700305.
Full textAbstract:
Microcomputers can provide sophisticated data base management and decision-support systems, budget calculations, word processing, and telecommunication networks. Agencies that develop expertise in microcomputer technology will make a larger contribution to the communities they serve.
47
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 (July 1, 2020): 4098–102. http://dx.doi.org/10.1166/jctn.2020.9025.
Full textAbstract:
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 this API, sentiment analysis report is generated. Using Python’s matplotlib package, a pie chart is generated to visualize the sentiment analysis results. Further an algorithm is proposed for sentiment analysis, which not only categorizes the tweets into positive, negative and neutral (as Aylien API does), but also categorizes the tweets into strongly and weakly, positive and negative based on the polarity and subjectivity. Django platform and Python’s TextBlob package are used for implementing this algorithm. For this experiment, data is collected from Twitter using the hash tags related to different events/topics like IPL2018, World Cup2018, Modi, and Delete Facebook etc. during the period Monday Jan 22, 2018 to Monday May 28, 2018. Moreover, the data is collected and processed using Python TextBlob. Also conducted the Sentiment analysis on text data using TextBlob and visual reports are generated using Google chart. The results obtained from both the above-mentioned approaches are compared and it is observed that the proposed algorithm gives better sentiment analysis of the tweets.
48
Dzihora, Kyryl. "Representation of historical memory in social network communities." Skhid 3, no. 1 (April 1, 2022): 15–24. http://dx.doi.org/10.21847/1728-9343.2022.3(1).254336.
Full textAbstract:
The article is devoted to the study of the representation of historical memory in social network communities. The purpose of this study is to identify current trends in some aspects of historical memory on social media. The study has been conducted on the three most popular networks in Ukraine, namely: Facebook, Instagram, YouTube. A comprehensive approach with the application of specialized software has been used for data collection. 468 communities from three social networks, the topics of which correspond to the search queries “Історія України”, “История Украины”, “History of Ukraine”, “Історія села”, “СCСР”, have been studied. The analysis of the obtained data has revealed that some groups formed a new category of educational groups. Further processing of the data has shown that groups often aim to break myths and promote “correct” history. Another category of groups is focused on the problem of preserving historical memory at the local level or the history of a particular industry. These trends demonstrate that social network communities are an indirect state of historical memory at the societal level, which, in turn, confirms Jameson’s thesis of the “New Historicism” of “installing historical attractions” and resisting theory.
49
Johal, Sukhnandan Kaur, and Rajni Mohana. "Effectiveness of Normalization Over Processing of Textual Data Using Hybrid Approach Sentiment Analysis." International Journal of Grid and High Performance Computing 12, no. 3 (July 2020): 43–56. http://dx.doi.org/10.4018/ijghpc.2020070103.
Full textAbstract:
Various natural language processing tasks are carried out to feed into computerized decision support systems. Among these, sentiment analysis is gaining more attention. The majority of sentiment analysis relies on the social media content. This web content is highly un-normalized in nature. This hinders the performance of decision support system. To enhance the performance, it is required to process data efficiently. This article proposes a novel method of normalization of web data during the pre-processing phase. It is aimed to get better results for different natural language processing tasks. This research applies this technique on data for sentiment analysis. Performance of different learning models is analysed using precision, recall, f-measure, fallout for normalize and un-normalize sentiment analysis. Results shows after normalization, some documents shift their polarity i.e. negative to positive. Experimental results show normalized data processing outperforms un-normalized data processing with better accuracy.
50
Ajmal, Sahar, Muhammad Awais, Khaldoon S. Khurshid, Muhammad Shoaib, and Anas Abdelrahman. "Data mining-based recommendation system using social networks—an analytical study." PeerJ Computer Science 9 (February 8, 2023): e1202. http://dx.doi.org/10.7717/peerj-cs.1202.
Full textAbstract:
In the current age, social media is commonly used and shares enormous data. However, a huge amount of data makes it difficult to deal with. It requires a lot of storage and processing time. The content produced by social media needs to be stored efficiently by using data mining methods for providing suitable recommendations. The goal of the study is to perform a systematic literature review (SLR) which finds, analyzes, and evaluates studies that relate to data mining-based recommendation systems using social networks (DRSN) from 2011 to 2021 and open up a path for scientific investigations to enhance the development of recommendation systems in a social network. The SLR follows Kitchenhem’s methodology for planning, guiding, and reporting the review. A systematic study selection procedure results in 42 studies that are analyzed in this article. The selected articles are examined on the base of four research questions. The research questions focus on publication venues, and chronological, and geographical distribution in DRSN. It also deals with approaches used to formulate DRSN, along with the dataset, size of the dataset, and evaluation metrics that validate the result of the selected study. Lastly, the limitations of the 42 studies are discussed. As a result, most articles published in 2018 acquired 21% of 42 articles, Whereas, China contributes 40% in this domain by comparing to other countries. Furthermore, 61% of articles are published in IEEE. Moreover, approximately 21% (nine out of 42 studies) use collaborative filtering for providing recommendations. Furthermore, the Twitter data set is common in that 19% of all other data sets are used, and precision and recall both cover 28% of selected articles for providing recommendations in social networks. The limitations show a need for a hybrid model that concatenates different algorithms and methods for providing recommendations. The study concludes that hybrid models may help to provide suitable recommendations on social media using data mining rules.