Social Capital on Social Media—Concepts, Measurement Techniques and Trends in Operationalization

The introduction of the Web 2.0 era and the associated emergence of social media platforms opened an interdisciplinary research domain, wherein a growing number of studies are focusing on the interrelationship of social media usage and perceived individual social capital. The primary aim of the present study is to introduce the existing measurement techniques of social capital in this domain, explore trends, and offer promising directions and implications for future research. Applying the method of a scoping review, a set of 80 systematically identified scientific publications were analyzed, categorized, grouped and discussed. Focus was placed on the employed viewpoints and measurement techniques necessary to tap into the possible consistencies and/or heterogeneity in this domain in terms of operationalization. The results reveal that multiple views and measurement techniques are present in this research area, which might raise a challenge in future synthesis approaches, especially in the case of future meta-analytical contributions

Let’s play on Facebook: using sentiment analysis and social media metrics to measure the success of YouTube gamers’ post types

This paper discusses the analysis results of successful self-marketing techniques on Facebook pages in the cases of three YouTube gamers: PewDiePie, Markiplier, and Kwebbelkop. The research focus was to identify significant differences in terms of the gamers’ user-generated Facebook metrics and commentary sentiments. Analysis of variance (ANOVA) and k-nearest neighbor sentiment analysis were employed as core research methods. ANOVA of the classified post categories revealed that photos tended to show significantly more user-generated interactions than other post types, while, on the other hand, re-posted YouTube videos gained significantly fewer numbers in the retrieved metrics than other content types. K-nearest neighbor sentiment analysis pointed out underlying follower negativity in cases where user-generated activity was relatively low, thereby improving the understanding of the opinion of the masses previously hidden behind metrics such as the number of likes, comments, and shares. The paper at hand highlights the methodological design of the study as well as a detailed discussion of key findings and their implications, and future work. The results per se indicate the need to utilize natural language processing techniques to optimize brand communication on social media and highlight the importance of considering machine learning sentiment analysis techniques for a better understanding of consumer feedback

Social media metrics and sentiment analysis to evaluate the effectiveness of social media posts

The present paper presents the results of an analysis of indicators underlying successful self-marketing techniques on social media. The participants included YouTube gamers. We focus on the content of their communication on Facebook to identify significant differences in terms of their user-generated Facebook metrics and commentary sentiments. Methodologically, ANOVA and sentiment analysis were applied. ANOVA of the classified post categories revealed that re-posted YouTube videos gained significantly fewer likes, comments, and shares from the audience. On the other hand, photos tended to show significantly more follower-generated actions compared to other post types in the sample. Sentiment analysis revealed underlying follower negativity when user-generated activity tended to be relatively low, offering valuable complementary results to the mere analysis of other post indicators, such as the number of likes, comments, and shares. The results indicated the necessity to utilize natural language processing techniques to optimize brand communication on social media and highlighted the importance of considering the opinion of the masses for better understanding of consumer feedback

Let’s play on Facebook: using sentiment analysis and social media metrics to measure the success of YouTube gamers’ post types

This paper discusses the analysis results of successful self-marketing techniques on Facebook pages in the cases of three YouTube gamers: PewDiePie, Markiplier, and Kwebbelkop. The research focus was to identify significant differences in terms of the gamers’ user-generated Facebook metrics and commentary sentiments. Analysis of variance (ANOVA) and k-nearest neighbor sentiment analysis were employed as core research methods. ANOVA of the classified post categories revealed that photos tended to show significantly more user-generated interactions than other post types, while, on the other hand, re-posted YouTube videos gained significantly fewer numbers in the retrieved metrics than other content types. K-nearest neighbor sentiment analysis pointed out underlying follower negativity in cases where user-generated activity was relatively low, thereby improving the understanding of the opinion of the masses previously hidden behind metrics such as the number of likes, comments, and shares. The paper at hand highlights the methodological design of the study as well as a detailed discussion of key findings and their implications, and future work. The results per se indicate the need to utilize natural language processing techniques to optimize brand communication on social media and highlight the importance of considering machine learning sentiment analysis techniques for a better understanding of consumer feedback.(c) The Author(s) 201