Methods of Analytics of Big Data of Popular Electronic Newspapers on Facebook

Mysiuk, I.; Mysiuk, R.; Shuvar, R.; Yuzevych, V.

doi:10.30970/eli.19.6

Cited by 3 publications

(3 citation statements)

References 7 publications

(7 reference statements)

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“…The tool helps configure requests through the user interface, not the command line. Based on the collected data from social networks [1,2], it is possible to fill the database. Data on the news from well-known publications that published information in Facebook posts Forbes, New York Times, Reuters, and Washington Post are added to the relevant indexes index-fbnewspapers, index-fb-newspapers-1, index-fbnewspapers-2, index-fb-newspapers-3.…”

Section: Figure 5 -Kibana Event Logging Viewmentioning

confidence: 99%

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Designing a Data Warehouse for Collected Data About User Activity in Social Networks Using Elasticsearch

Mysiuk

2023

PoS

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In this paper, a data storage data warehouse is designed to store collected data from social networks. Creating indexes with data and selecting a configuration with the appropriate number of shards and replicas is described -the primary states of the cluster and possibilities of its scaling. The features of working with the non-relational Elasticsearch database are described when working with data on user activity in social network posts. Among social networks, Facebook and Instagram were chosen for analysis. The paper describes the advantages and disadvantages of using such a data store compared to Apache Kafka. Analysed existing data insertion Application Program Interfaces (APIs) and data visualisation tools integrated with Elasticsearch. The study describes the use of the Bulk API to insert many records at once into a database. The designed data warehouse uses Kibana, a data visualisation and analytics tool integrated with the selected database. Also, it is shown the ability to insert and view logs using Elasticsearch, Logstash, and Kibana (ELK stack). Tested data ingest by logging into the database using Beats. The obtained results can help implement a system for analysing user activities from social network data based on Elasticsearch as a central component.

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Section: Figure 5 -Kibana Event Logging Viewmentioning

confidence: 99%

“…This can be the number of likes, comments, shares, etc. To obtain data from social networks, a program developed based on the Selenium library is used [1,2].…”

Section: Introductionmentioning

confidence: 99%

Designing a Data Warehouse for Collected Data About User Activity in Social Networks Using Elasticsearch

Mysiuk

2023

PoS

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“…The data collection process takes place with the help of a program for reading data from the Document Object Model (DOM) web page of social media and saves it to a file for further processing. The program is written in the Java programming language with the connection of the Selenium library for automating the testing of web pages in browsers [1,2]. For the collected data from several social networks, it is possible to summarize and analyze the data for the presence of bots according to certain attributes [3].…”

Section: Data Collection and Formation Of Data Setsmentioning

confidence: 99%

Detection of Unnatural User Behavior in Social Media Using Machine Learning Technologies

Mysiuk,

Shuvar

2023

ELIT

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Information from social media influence public sentiment, the spread of opinions and reactions to events. The spread of often false information and the incitement of people in sensitive topics are often influenced by artificially created users for certain events. In other words, such users are called bots, which should be quickly detected and blocked in order to stop the promotion of certain profitable questions to customers. The detection process can be automated with the help of trained models based on the collected data and clustering of users according to the features inherent in bots. Among the mentioned differences are information about the number of subscribers and followers, activity of publishing posts, photo, date of profile creation and others. For training and testing models, a method of automated data collection using the Selenium framework from user pages is described. Correlation of various attributes is shown, and classification results are shown for identifying users with unnatural behavior in social media.

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Designing a Data Pipeline Architecture for Intelligent Analysis of Streaming Data

Mysiuk,

Shuvar

et al. 2024

Communications in Computer and Information Science

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Methods of Analytics of Big Data of Popular Electronic Newspapers on Facebook

Cited by 3 publications

References 7 publications

Designing a Data Warehouse for Collected Data About User Activity in Social Networks Using Elasticsearch

Designing a Data Warehouse for Collected Data About User Activity in Social Networks Using Elasticsearch

Detection of Unnatural User Behavior in Social Media Using Machine Learning Technologies

Designing a Data Pipeline Architecture for Intelligent Analysis of Streaming Data

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