Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method
In this paper we investigate the comments from the AlgoRythmics YouTube channel using the Comment Term Frequency Comparison social media analytics method. Comment Term Frequency Comparison can be a useful tool to understand how a social media platform, such as a Youtube channel is being discussed by...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2022-12-01
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| Series: | Acta Universitatis Sapientiae: Informatica |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/ausi-2022-0016 |
| _version_ | 1811159159254548480 |
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| author | Osztián Pálma Rozália Kátai Zoltán Sántha Ágnes Osztián Erika |
| author_facet | Osztián Pálma Rozália Kátai Zoltán Sántha Ágnes Osztián Erika |
| author_sort | Osztián Pálma Rozália |
| collection | DOAJ |
| description | In this paper we investigate the comments from the AlgoRythmics YouTube channel using the Comment Term Frequency Comparison social media analytics method. Comment Term Frequency Comparison can be a useful tool to understand how a social media platform, such as a Youtube channel is being discussed by users and to identify opportunities to engage with the audience. Understanding viewer opinions and reactions to a video, identifying trends and patterns in the way people are discussing a particular topic, and measuring the effectiveness of a video in achieving its intended goals is one of the most important points of view for a channel to develop. Youtube comment analytics can be a valuable tool looking to understand how the AlgoRythmics channel videos are being received by viewers and to identify opportunities for improvement. Our study focuses on the importance of user feedback based on ten algorithm visualization videos from the AlgoRythmics channel. In order to find evidence how our channel works and new ideas to improve we used the so-called comment term frequency comparison social media analytics method to investigate the main characteristics of user feedback. We analyzed the comments using both Youtube Studio Analytics and Mozdeh Big Data Analysis tool. |
| first_indexed | 2024-04-10T05:37:27Z |
| format | Article |
| id | doaj.art-16038dbf29b5452ba8c2ca57111db5fa |
| institution | Directory Open Access Journal |
| issn | 2066-7760 |
| language | English |
| last_indexed | 2024-04-10T05:37:27Z |
| publishDate | 2022-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Acta Universitatis Sapientiae: Informatica |
| spelling | doaj.art-16038dbf29b5452ba8c2ca57111db5fa2023-03-06T17:00:03ZengSciendoActa Universitatis Sapientiae: Informatica2066-77602022-12-0114227330110.2478/ausi-2022-0016Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics methodOsztián Pálma Rozália0Kátai Zoltán1Sántha Ágnes2Osztián Erika3Sapientia Hungarian University of Transylvania Târgu Mureş, Romania, Department of Mathematics-InformaticsSapientia Hungarian University of Transylvania Târgu Mureş, Romania, Department of Mathematics-InformaticsSapientia Hungarian University of Transylvania Târgu Mureş, Romania, Department of Applied Social SciencesSapientia Hungarian University of Transylvania Târgu Mureş, Romania, Department of Mathematics-InformaticsIn this paper we investigate the comments from the AlgoRythmics YouTube channel using the Comment Term Frequency Comparison social media analytics method. Comment Term Frequency Comparison can be a useful tool to understand how a social media platform, such as a Youtube channel is being discussed by users and to identify opportunities to engage with the audience. Understanding viewer opinions and reactions to a video, identifying trends and patterns in the way people are discussing a particular topic, and measuring the effectiveness of a video in achieving its intended goals is one of the most important points of view for a channel to develop. Youtube comment analytics can be a valuable tool looking to understand how the AlgoRythmics channel videos are being received by viewers and to identify opportunities for improvement. Our study focuses on the importance of user feedback based on ten algorithm visualization videos from the AlgoRythmics channel. In order to find evidence how our channel works and new ideas to improve we used the so-called comment term frequency comparison social media analytics method to investigate the main characteristics of user feedback. We analyzed the comments using both Youtube Studio Analytics and Mozdeh Big Data Analysis tool.https://doi.org/10.2478/ausi-2022-0016social mediayoutubecommentstime series graphsubtopic word frequency analysisgender differences analysissentiment analysis68w40 |
| spellingShingle | Osztián Pálma Rozália Kátai Zoltán Sántha Ágnes Osztián Erika Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method Acta Universitatis Sapientiae: Informatica social media youtube comments time series graph subtopic word frequency analysis gender differences analysis sentiment analysis 68w40 |
| title | Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method |
| title_full | Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method |
| title_fullStr | Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method |
| title_full_unstemmed | Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method |
| title_short | Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method |
| title_sort | investigating the algorythmics youtube channel the comment term frequency comparison social media analytics method |
| topic | social media youtube comments time series graph subtopic word frequency analysis gender differences analysis sentiment analysis 68w40 |
| url | https://doi.org/10.2478/ausi-2022-0016 |
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