Knowledge Discovery from Large Amounts of Social Media Data
In recent years, social media analysis is arousing great interest in various scientific fields, such as sociology, political science, linguistics, and computer science. Large amounts of data gathered from social media are widely analyzed for extracting useful information concerning people’s behavior...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/3/1209 |
| _version_ | 1797489242246479872 |
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| author | Loris Belcastro Riccardo Cantini Fabrizio Marozzo |
| author_facet | Loris Belcastro Riccardo Cantini Fabrizio Marozzo |
| author_sort | Loris Belcastro |
| collection | DOAJ |
| description | In recent years, social media analysis is arousing great interest in various scientific fields, such as sociology, political science, linguistics, and computer science. Large amounts of data gathered from social media are widely analyzed for extracting useful information concerning people’s behaviors and interactions. In particular, they can be exploited to analyze the collective sentiment of people, understand the behavior of user groups during global events, monitor public opinion close to important events, identify the main topics in a public discussion, or detect the most frequent routes followed by social media users. As an example of the countless works in the state-of-the-art on social media analysis, this paper presents three significant applications in the field of opinion and pattern mining from social media data: (i) an automatic application for discovering user mobility patterns, (ii) a novel application for estimating the political polarization of public opinion, and (iii) an application for discovering interesting social media discussion topics through a hashtag recommendation system. Such applications clearly highlight the abundance and wealth of useful information in many application contexts of human life that can be extracted from social media posts. |
| first_indexed | 2024-03-10T00:14:42Z |
| format | Article |
| id | doaj.art-1aab739b33ea495fabb0187c97b8c9ff |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:14:42Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1aab739b33ea495fabb0187c97b8c9ff2023-11-23T15:53:41ZengMDPI AGApplied Sciences2076-34172022-01-01123120910.3390/app12031209Knowledge Discovery from Large Amounts of Social Media DataLoris Belcastro0Riccardo Cantini1Fabrizio Marozzo2Department of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, 87036 Rende, ItalyDepartment of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, 87036 Rende, ItalyDepartment of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, 87036 Rende, ItalyIn recent years, social media analysis is arousing great interest in various scientific fields, such as sociology, political science, linguistics, and computer science. Large amounts of data gathered from social media are widely analyzed for extracting useful information concerning people’s behaviors and interactions. In particular, they can be exploited to analyze the collective sentiment of people, understand the behavior of user groups during global events, monitor public opinion close to important events, identify the main topics in a public discussion, or detect the most frequent routes followed by social media users. As an example of the countless works in the state-of-the-art on social media analysis, this paper presents three significant applications in the field of opinion and pattern mining from social media data: (i) an automatic application for discovering user mobility patterns, (ii) a novel application for estimating the political polarization of public opinion, and (iii) an application for discovering interesting social media discussion topics through a hashtag recommendation system. Such applications clearly highlight the abundance and wealth of useful information in many application contexts of human life that can be extracted from social media posts.https://www.mdpi.com/2076-3417/12/3/1209Big Datasocial media analysisBig Data analysissocial media applicationsknowledge discovery |
| spellingShingle | Loris Belcastro Riccardo Cantini Fabrizio Marozzo Knowledge Discovery from Large Amounts of Social Media Data Applied Sciences Big Data social media analysis Big Data analysis social media applications knowledge discovery |
| title | Knowledge Discovery from Large Amounts of Social Media Data |
| title_full | Knowledge Discovery from Large Amounts of Social Media Data |
| title_fullStr | Knowledge Discovery from Large Amounts of Social Media Data |
| title_full_unstemmed | Knowledge Discovery from Large Amounts of Social Media Data |
| title_short | Knowledge Discovery from Large Amounts of Social Media Data |
| title_sort | knowledge discovery from large amounts of social media data |
| topic | Big Data social media analysis Big Data analysis social media applications knowledge discovery |
| url | https://www.mdpi.com/2076-3417/12/3/1209 |
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