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<p>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.</p>
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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.

Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method

Sapientia Hungarian University of Transylvania Târgu Mureş

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper we investigate the comments from the AlgoRythmics YouTube channel using the Comment Term Frequency Comparison social media analytics method....

Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method

Publié en ligne: 04 févr. 2023

Pages: 273 - 301

Reçu: 20 déc. 2022

Accepté: 30 déc. 2022

DOI: https://doi.org/10.2478/ausi-2022-0016

Mots clés
social media, YouTube, comments, time series graph, subtopic word frequency analysis, gender differences analysis, sentiment analysis

© 2022 Pálma Rozália Osztián et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method

Publié en ligne: 04 févr. 2023

Pages: 273 - 301

Reçu: 20 déc. 2022

Accepté: 30 déc. 2022

DOI: https://doi.org/10.2478/ausi-2022-0016

Mots cléssocial media, YouTube, comments, time series graph, subtopic word frequency analysis, gender differences analysis, sentiment analysis

© 2022 Pálma Rozália Osztián et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
social media, YouTube, comments, time series graph, subtopic word frequency analysis, gender differences analysis, sentiment analysis